2.4. CONDITION MONITORING (PREDICTIVE) MAINTENANCE
By Aleksandar
Pudar
Technical Superintendent
and Planned Maintenance Supervisor Reederei Nord BV
Co-founder of "Out of Box
Maritime Thinker Blog" and Founder of Naro Consilium Group
Predictive maintenance is a critical maintenance
strategy in the marine industry but is often misunderstood and misused. Many
believe that it is only used to prevent catastrophic failure of critical
equipment or as a maintenance scheduling tool that uses data such as vibration
and infrared or lubricating oil analysis to determine the need for corrective
maintenance actions. However, these definitions limit the potential benefits of
predictive maintenance and often fail to achieve a marked decrease in
maintenance costs or measurable improvement in overall vessel performance.
Predictive maintenance in the marine industry is
much more than a maintenance scheduling tool and should not be restricted to
maintenance management. Instead, it should be part of an integrated, total
vessel performance management program that can improve the production capacity,
product quality, and effectiveness of the vessel's equipment and machinery.
Predictive maintenance can reduce breakdown losses, quality defects, capacity
factors, and maintenance costs by monitoring the condition of equipment and detecting
potential issues before they lead to equipment failure. This strategy can also
improve safety, reduce environmental impact, and increase the reliability and
efficiency of the vessel's equipment and machinery.
It is essential to recognise that predictive
maintenance is not a panacea for all problems in vessel maintenance management.
Instead, it is just one tool that should be integrated with other maintenance
strategies and overall vessel management practices. By implementing a
comprehensive predictive maintenance program that includes various techniques
such as vibration monitoring, thermography, tribology, electric motor analysis,
visual inspection, ultrasonic monitoring, and operating dynamics analysis,
vessel owners and operators can ensure the safety, reliability, and efficiency
of their equipment and machinery.
2.4.1 DEFINITION OF PREDICTIVE MAINTENANCE
Predictive maintenance regularly evaluates vessel
equipment and machinery operating conditions as a management technique to
optimise total vessel operation. It is important to note that predictive
maintenance is not a panacea for all factors that limit total vessel
performance, and it cannot directly affect vessel performance. The output of a
predictive maintenance program is data, and action must be taken to resolve the
deviations or problems revealed by the program before vessel performance can be
improved. Therefore, a management philosophy committed to vessel improvement
must exist before any meaningful benefit can be derived.
Properly used, predictive maintenance can
identify most if not all, factors that limit the effectiveness and efficiency
of the total vessel. Predictive technology can be used for much more than just
measuring the operating condition of critical vessel machinery. The technology
permits accurate evaluation of all functional groups within the company, such
as maintenance. However, without the absolute commitment and support of senior
management and the full cooperation of all vessel functions, a predictive maintenance
program cannot provide the means to resolve poor vessel performance.
2.4.1.1 CONDITION
MONITORING (PREDICTIVE) MAINTENANCE
2.4.1.1.1 POTENTIAL
FAILURE (P-F) DIAGRAM
Although many failure modes are not age-related,
most of them give some warning that they are in the process of occurring or
about to occur. If evidence can be found that something is in the final stages
of a failure, it may be possible to take action to prevent it from failing
and/or to avoid the consequences. Section 4, Figure 1 illustrates the final
stages of failure, called the P-F curve. Section 4, Figure 1 illustrates how a
condition deteriorates to the point at which it can be detected (Point P) and then,
if it is not detected and corrected, continues to deteriorate until it reaches
the point of functional failure (Point F). In practice, there are many ways of
determining whether failures are in the process of occurring (e.g., hot spots
showing deterioration of furnace refractories or electrical insulation,
vibrations indicating imminent bearing failure, increasing level of
contaminants in lubricating oil). If a potential failure is detected between
Point P and Point F, it may be possible to take action to prevent the
functional failure (or at least to minimise the effects). Tasks designed to
detect potential failure are known as condition-monitoring tasks.
2.4.1.1.2 THE P-F
INTERVAL
The time interval between Point P and Point F in
Section 4, Figure 1 is called the "P-F interval"; this is
the warning period (e.g., the time between the
point at which the potential failure becomes detectable and the point at which
it deteriorates into a functional failure). If a condition-monitoring task is
performed on
intervals longer than the P-F interval, the
potential failure may not be detected. On the other hand, if the
condition-monitoring task is performed too frequently compared to the P-F
interval, resources are wasted. For example, if the inspection interval is once
per month and the P-F interval is six (6) months, the time between the
discovery of the potential failure and the occurrence of the functional failure
is five (5) months; this is sometimes known as the available P-F interval. For
a condition-monitoring task to be technically feasible, the available P-F
interval must be longer than required to take action to prevent the functional
failure (or minimise its effects). It should be noted that the P-F interval can
vary in practice, and in some cases, it can be very inconsistent. A task
interval substantially less than the shortest of the likely P-F intervals
should be selected in these cases.
2.4.2 VESSEL
MAINTENANCE MANAGEMENT
Vessel maintenance management involves managing
vessel maintenance activities to ensure equipment and machinery's safety,
reliability, and efficiency; this includes maintenance management,
machinery/equipment operation management, maintenance quality improvement, and
predictive maintenance techniques.
Properly used, predictive maintenance can provide
the means to eliminate all factors that limit vessel performance. One factor
that limits the effective management of vessels is the lack of timely, factual
data that define the operating conditions of critical production systems and
the effectiveness of critical vessel functions, such as purchasing,
engineering, and production. High maintenance costs in the marine industry are
the direct result of inherent problems throughout the vessel, not just ineffective
maintenance management. Poor design standards and purchasing practices,
improper operation, and outdated management methods contribute more to high
operations and maintenance costs than delays caused by the catastrophic failure
of critical vessel machinery.
Because of the breakdown mentality and myopic
view of the root cause of ineffective vessel performance, too many vessels
restrict predictive maintenance to the maintenance function. Expansion of the
program to include regular evaluation of all factors that limit vessel
performance will significantly enhance the benefits that can be derived. In a full
vessel performance mode, predictive technology can accurately measure the
effectiveness and efficiency of all vessel functions, not just machinery. In
addition, the data generated by regular evaluation can isolate specific
limitations in skill levels, inadequate procedures, poor management methods,
and incipient machine or process system problems.
By implementing a comprehensive predictive
maintenance program that includes various techniques such as vibration
monitoring, thermography, tribology, electric motor analysis, visual
inspection, ultrasonic monitoring, and operating dynamics analysis, vessel
owners and operators can ensure the safety, reliability, and efficiency of
their equipment and machinery. Predictive maintenance can reduce breakdown
losses, quality defects, capacity factors, and maintenance costs. This strategy
can also improve safety, reduce environmental impact, and increase the
reliability and efficiency of the vessel's equipment and machinery.
2.4.2.1 MAINTENANCE
MANAGEMENT
Maintenance management involves planning,
scheduling, and executing maintenance activities to ensure equipment meets the
required standards. It includes maintenance planning, work order management,
asset management, inventory management, and maintenance reporting.
As a maintenance management tool, predictive
maintenance can provide the data required to schedule preventive and corrective
maintenance tasks on an as-needed basis for vessel equipment and machinery.
Instead of relying on average-life statistics, such as mean-time-to-failure, to
schedule maintenance activities, predictive maintenance uses direct monitoring
of the operating condition, system efficiency, and other indicators to
determine the actual mean-time-to-failure or loss of efficiency for each machinery
and system on board.
A comprehensive predictive maintenance program
can provide factual data that define the actual mechanical condition of each
machinery train and the operating efficiency of each process system on board
the vessel. These data can be used to schedule maintenance activities and
minimise unscheduled breakdowns of all mechanical equipment on the vessel while
ensuring that repaired equipment is in acceptable mechanical condition.
To achieve these goals, the predictive
maintenance program must correctly identify the root cause of incipient
problems in the machinery and systems on board the vessel. Furthermore, the
predictive maintenance program must be combined with a viable maintenance
planning function to use the data to plan and schedule appropriate repairs. In
addition, it depends on the skill and knowledge of maintenance craftsmen. Both
ineffective planning and improper repairs will severely restrict the benefits
of predictive maintenance.
Predictive maintenance utilising vibration
signature analysis is predicated on standard failure modes having distinct
vibration frequency components that can be isolated and identified. In
addition, predictive maintenance utilising process efficiency, heat loss, or
other nondestructive techniques can quantify the operating efficiency of
nonmechanical plant equipment or systems. Combined with vibration analysis,
these techniques can provide the maintenance manager or marine engineer with
factual information that will enable them to achieve optimum reliability and
availability from their vessel equipment and machinery.
Proper implementation of predictive maintenance
can minimise maintenance costs and increase vessel efficiency and reliability.
However, it is essential to note that predictive maintenance cannot function in
a void and must be integrated into a comprehensive maintenance management
program that encompasses all factors that limit the effectiveness and
efficiency of vessel equipment and machinery.
2.4.2.1.1
CONDITION-MONITORING MAINTENANCE TASK APPLICABILITY AND EFFECTIVENESS
For a condition-monitoring maintenance task to be
considered applicable and practical, the following considerations must be made:
·
The onset of failure must be detectable. There must be some measurable
parameter that can detect the deterioration in the equipment's condition. In
addition, maintenance personnel must be able to establish limits to determine
when corrective action is needed.
·
Reasonably consistent P-F interval. The P-F interval must be
consistent enough to ensure that corrective actions are not implemented
prematurely or that failure occurs before corrective actions are implemented.
·
Practical interval in which condition-monitoring tasks can be
performed. The P-F interval must be sufficient to permit a practical task
interval. For example, a failure with a P-F interval of minutes or hours is
probably not a good candidate for a condition-monitoring maintenance task.
·
Sufficient warning so that corrective actions can be implemented. The
P-F interval must be long enough to implement corrective actions. This can be
determined by subtracting the task interval from the expected P-F interval and
then judging whether sufficient time remains to take necessary corrective
actions.
·
Reduces the probability of failure (and therefore the risk) to an
acceptable level. The tasks must be carried out at an interval so that the
probability of failure allows an acceptable risk level to be achieved.
Agreed-upon risk acceptance criteria should be determined and recorded.
·
It must be cost-effective. The cost of undertaking a task over a
period of time should be less than the total cost of the consequences of
failure.
2.4.2.2
MACHINERY/EQUIPMENT OPERATION MANAGEMENT
In the marine industry, managing vessel machinery
and equipment operations is crucial to ensure safety, reliability, and
efficiency; this involves ensuring that the equipment is operated according to
the manufacturer's guidelines and best practices. In addition, proper
operation, starting, and shutting down of equipment prevent damage and maximise
equipment life.
As a comprehensive program, predictive
maintenance provides valuable data for maintenance management, machinery and
equipment operation management, and maintenance quality improvement. In
addition, the program can be a critical production management tool on board and
onshore. The data derived from the program can be used to increase vessel
voyage capacity, vessel service quality, and overall effectiveness of the vessel
function.
Optimum vessel efficiency is directly dependent
on several machine-related factors. Predictive maintenance can provide data for
consistent reliability, capacity, and efficiency from critical equipment and
systems. However, many factors that affect vessels' efficiency are outside the
maintenance function. These factors include standard operating procedures and
operator errors. Therefore, the traditional evaluation methods of predictive
maintenance are essential to determine the effects of these influences and
achieve optimal vessel performance.
Predictive maintenance also benefits vessel
management in terms of service quality and total costs. Regular evaluation of
critical equipment and systems can anticipate potential problems resulting in
reduced quality and increased costs. While the output of the predictive
maintenance program is data, the information can be used to correct various
problems affecting the effectiveness and efficiency of the vessel.
Therefore, predictive maintenance is essential in
the marine industry to ensure vessel machinery and equipment's safe, reliable,
and efficient operation. It provides accurate information for maintenance
management and helps improve efficiency, product quality, and overall
effectiveness.
2.4.2.3 MAINTENANCE
QUALITY IMPROVEMENT
Maintenance quality improvement in the marine
industry involves implementing strategies to improve maintenance quality and
reduce maintenance costs, such as implementing preventive maintenance programs,
optimising maintenance schedules, and reducing downtime. In addition, predictive
maintenance can effectively identify the root causes of maintenance quality
issues and reduce maintenance costs.
In addition, most safety and environmental
incidents in the marine industry can be traced back to problems with equipment,
systems, or procedures. Predictive maintenance can help identify and anticipate
potential problems before they can result in incidents, thereby improving
safety and environmental compliance.
A comprehensive program using predictive
maintenance techniques such as vibration monitoring, thermography, tribology,
process parameters, and operating dynamics can provide the data required to
correct problems that may result in reduced product quality, safety incidents,
or environmental violations. By implementing predictive maintenance, vessel
operators can optimally maintain their equipment and machinery, reducing the
likelihood of breakdowns, costly repairs, and safety incidents.
2.4.2.3.1 DETERMINING CONDITION-MONITORING MAINTENANCE
TASK INTERVALS
Condition-monitoring maintenance task intervals
must be determined based on the expected P-F interval. The following sources
may be referred to as an aid in determining the P-F interval:
·
Expert opinion and judgment (e.g., manufacturer's recommendations)
·
Published information about condition-monitoring tasks (e.g., Appendix
1, RCM texts)
·
Historical data (e.g., current condition-monitoring task intervals)
2.4.2.3.1.1 CONDITION-MONITORING
TASK INTERVAL
The interval for a condition-monitoring task
should be set at no more than half the expected P-F interval and should be
adjusted based on the following considerations:
·
Reduce the task interval if the P-F interval minus the task interval
(based on 1/2 [P-F interval]) does not provide sufficient time to implement
corrective actions.
·
Reduce the task interval if there is low confidence in the
"guesstimate" of the expected P-F.
·
Reduce the task interval for higher-risk failure modes.
·
Set the task interval at half the expected P-F interval (or slightly
above) for lower-risk failure modes.
2.4.2.3.1.2 INITIAL
CONDITION-MONITORING TASK INTERVAL
Because few organisations will have detailed
knowledge about the equipment failure mode P-F interval, the following
guidelines can be used to establish initial condition-monitoring task
intervals:
·
If an existing condition-monitoring task is being performed and has
proven effective (e.g., no unexpected failures have occurred), use the existing
task interval as the initial default task interval.
·
If an existing condition-monitoring task is being performed and some
functional failures have occurred, adjust the task interval downward based on
the experience.
·
If no existing condition-monitoring task is being performed or a new one
is being proposed, the task interval must be based on the team's estimate of
the P-F interval and guidelines provided in Paragraph 4/4.1. The following
questions can help the team estimate the P-F interval:
o
How quickly can the condition deteriorate and result in a functional
failure? Will it deteriorate in minutes, hours, days, weeks, months or years?
o
What is the capability of the condition-monitoring task in detecting
the onset of failure? High or low?
o
How confident is the team in its judgment?
2.4.2.3.1.2 IMPROVING THE UNDERSTANDING OF P-F INTERVALS
As data from condition-monitoring tasks are
collected, and corrective actions are implemented, a facility will improve its
understanding of the P-F interval. For example, assume that vibration testing
is performed weekly on pumps in a similar service. On several occasions, the
vibration analysis detects the onset of failures. However, due to scheduling
delays, corrective action is not taken for six (6) to eight (8) weeks. Nevertheless,
during this delay, the pumps continue to operate appropriately. We then know
that the P-F interval for these pumps is probably at least six (6) weeks, and
the task interval can be changed to three (3) weeks (1/2 of six weeks). This is
a rough form of age-exploration testing.
2.4.3.2 ESTABLISHING
CONDITION-MONITORING MAINTENANCE TASK ACTION LIMITS
Another aspect of a condition-monitoring
maintenance task is establishing action limits; this involves establishing
limits that result in corrective actions when exceeded. The actions may involve
any of the following:
·
Reperforming the condition-monitoring task to verify the results
·
Altering the task interval to ensure closer monitoring of the
equipment
·
Initiating corrective actions to prevent the impending equipment
failure
Establishing limits helps ensure that
condition-monitoring tasks are effective in detecting and/or preventing the
failure
2.4.2.4 PREDICTIVE
MAINTENANCE TECHNIQUES
2.4.2.4.1
VIBRATION
MONITORING
Vibration monitoring is a crucial aspect of
predictive maintenance for vessels. It involves measuring the vibration of
equipment to determine its condition. Commercially available systems can
monitor, trend, and evaluate the condition of all machinery/equipment on a
vessel. Vibration analysis is the dominant technique used for predictive
maintenance management, and it can be used to detect and identify incipient
mechanical problems in critical machinery.
Recent advancements in microprocessor technology
have made it cost-effective to implement vibration-based predictive maintenance
programs in most maritime applications. In addition, these microprocessor-based
systems have simplified data acquisition, automated data management and minimised
the need for vibration experts to interpret data.
Most vibration-based predictive maintenance
programs rely on one or more trending and analysis techniques, including
broadband trending, narrowband trending, and signature analysis. By monitoring
the vibration of plant machinery, a direct correlation can be made between the
mechanical condition and the recorded vibration data of each machine on a
vessel. Any degradation of the mechanical condition can be detected using
vibration monitoring techniques, and vibration analysis can identify specific
degrading machine components or the failure mode of plant machinery before severe
damage occurs.
2.4.2.4.1.1 Broadband
Trending involves measuring the overall vibration of equipment
to detect changes in vibration levels. Broadband vibration monitoring is useful
for acquiring vibration readings from select points on a vessel's machinery.
This technique compares the acquired data with either a baseline reading taken
from a new machine or vibration severity charts to determine the relative
condition of the machinery. Generally, the unfiltered broadband measurement
that provides the total vibration energy between 10 and 10,000 Hz is used for
this type of analysis. However, it should be noted that broadband values must
be adjusted to the actual operating parameters, such as vessel speed and load,
to be effective. Changes in both the speed and load ( motor, pump) will directly
affect the overall vibration levels of the machinery. While this technique can
be used as a gross scan of the operating condition of critical machinery, it
does not provide any information about the actual machine problem or failure
mode. Therefore, it is essential to use other trending and analysis techniques,
such as narrowband trending and signature analysis, to identify specific
degrading machine components or the failure mode of the machinery.
2.4.2.4.1.2 Narrowband
Trending involves measuring the vibration at specific frequencies
to detect changes in vibration levels. Narrowband trending is a useful
predictive maintenance technique for monitoring the condition of critical
machine components in vessels. This technique involves monitoring specific
vibration frequencies that represent machine components or failure modes, providing
a quick and efficient means of monitoring the mechanical condition of these
components.; this can include monitoring the condition of gear sets, bearings,
and other components without manually analysing vibration signatures.
However, it is important to note that changes in
speed, load, and other process parameters can directly and significantly impact
the vibration energy produced by each machine component. Therefore, for
narrowband values to be meaningful and effective, they must be adjusted to the
actual machinery parameters; this will ensure that the data collected
accurately reflects the condition of the machine components and allows for
proper scheduling of maintenance tasks.
2.4.2.4.1.3 Signature
Analysis involves analysing equipment's vibration spectrum to
identify issues such as unbalance, misalignment, or bearing damage. Signature analysis
is a valuable tool for predictive maintenance. It allows the identification of
each frequency component a machine generates through visual representation.
With proper training, vessel staff can use vibration signatures to determine
the maintenance required for specific machinery/equipment. Many predictive
maintenance programs utilise some form of signature analysis. However, most of
these programs rely on comparative analysis instead of complete root-cause
techniques, limiting the program's potential benefits. Therefore, it is crucial
to incorporate a comprehensive approach that includes complete root-cause
techniques to achieve maximum results. (FASIT, FMEA etc.)
2.4.2.4.2
THERMOGRAPHY
Thermography involves using thermal imaging to
detect changes in equipment temperature.
Thermography is a useful predictive maintenance
technique for monitoring the condition of plant machinery, structures, and
systems on board tanker vessels.
Infrared technology is used to detect thermal
anomalies and determine the operating condition of equipment by monitoring the
emission of infrared energy, i.e., temperature. All objects emit infrared
radiation with a temperature above absolute zero, and the intensity of the
radiation is a function of the object's surface temperature.
The surface of an object affects the amount of
emitted or reflected energy. Therefore, careful consideration of the actual
emissivity of an object improves the accuracy of temperature measurements used
for predictive maintenance. Most onboard equipment falls under the category of
graybodies, which have an emissivity factor of less than 1.0. However, the
actual emissivity factor for equipment varies due to variations in surface
conditions, protective coatings, and other variables.
Thermographic techniques require special filters
to avoid the negative effects of atmospheric attenuation of infrared data to
ensure the accuracy of temperature measurements. Three general types of
instruments can be used for predictive maintenance: infrared thermometers or
spot radiometers, line scanners, and imaging systems. These instruments collect
radiant energy and convert it into an electrical signal that is processed and
displayed. An experienced and trained engineer can identify incipient problems within
the vessel's machinery, structures, and systems by detecting thermal anomalies.
2.4.2.4.2.1 Infrared
Thermometers involve
measuring the temperature of equipment surfaces.
Infrared thermometers or spot radiometers can be helpful
for predictive maintenance in the marine industry. They are designed to measure
the surface temperature at a single point on a machine or surface. This
technique can monitor critical points on vessel machinery or equipment, such as
bearing cap temperatures, motor winding temperatures, and spot checks of
process piping temperatures. In conjunction with other predictive maintenance
techniques, such as vibration monitoring, point-of-use infrared data can
provide valuable insights into the condition of the equipment and help prevent
potential problems before they occur. However, it is essential to note that
this technique is limited because it only provides temperature data for a
single point on the machine or structure.
2.4.2.4.2.2 Line
Scanners involve
scanning equipment surfaces to detect temperature changes.
Line scanners can provide a larger field of view
when monitoring the condition of the equipment. This type of infrared
instrument provides a two-dimensional scan or line of comparative radiation,
which can monitor the temperature distribution of critical components such as
pipes, heat exchangers, and engines. While this technique is not as precise as
spot radiometers, it can still provide valuable information for predicting
maintenance needs and preventing equipment failures.
2.4.2.4.2.3 Infrared Imaging involves
using thermal cameras to detect changes in temperature across large areas of
equipment. Thermal
or infrared imaging can be a valuable tool in predictive maintenance for the
marine industry. This technique allows the scanning of complete machines,
equipment, and systems, providing a wide area's thermal emission profile. The
use of thermal imaging instruments can range from relatively inexpensive
black-and-white scanners to full-colour microprocessor-based systems that
provide the capability of store-and-recall thermal images, enabling a long-term
predictive maintenance program.
Incorporating thermography into a predictive
maintenance program for vessels can improve the reliability and efficiency of
critical process systems that rely on heat transfer or retention, electrical
equipment, and other parameters. Infrared techniques can detect potential
issues in marine systems and equipment, including electrical switchgear,
gearboxes, electrical substations, transmissions, circuit breaker panels,
motors, hulls, bearings, and process systems that rely on heat retention or
transfer. This application of thermography can help vessel operators detect and
prevent failures or malfunctions, increasing safety and reducing downtime and
maintenance costs.
2.4.2.4.3 TRIBOLOGY
Tribology is the study of the design and
operating dynamics of the bearing-lubrication-rotor support structure of
machinery, which is important for predictive maintenance in the marine
industry. Several tribology techniques can be used for predictive maintenance
on board tanker vessels, including lubricating oil analysis, spectrographic
analysis, ferrography, and wear particle analysis.
Lubricating oil analysis is an important
technique determining the condition of lubricating oils used in mechanical and
electrical equipment. This analysis can provide an accurate breakdown of
individual chemical elements, both oil additives and contaminants, contained in
the oil. Comparing the amount of trace metals in successive oil samples can
indicate wear patterns of oil-wetted parts in marine equipment and will
indicate impending machine failure.
Recently, tribology analysis has become more
efficient and cost-effective with the availability of microprocessor-based
systems that can automate most of the lubricating oil and spectrographic
analysis. Spectrographic or lubricating oil analysis can be used for quality
control, reducing lubricating oil inventories, and determining the most
cost-effective interval for an oil change. Lubricating, hydraulic, and
dielectric oils can be periodically analysed using these techniques to
determine their condition.
As a predictive maintenance tool, lubricating oil
and spectrographic analysis can be used to schedule oil change intervals based
on the actual condition of the oil. In the marine industry, this technique can
result in a considerable annual reduction in maintenance costs. Frequent
sampling and trending the data for each machinery on board the vessel is vital
to obtain the full benefit of oil analysis, which can provide a wealth of
information to base maintenance decisions.
2.4.2.4.3.1 LUBRICATING
OIL ANALYSIS
Oil analysis has become essential to preventive
maintenance on board ocean-going vessels. Therefore, laboratories suggest
taking scheduled samples of engine lubricants to assess the condition of the
critical lubricating film necessary for engine operation. Typically, ten tests
are conducted on lube oil samples:
Viscosity is critical for
lubricating oil in the main engine on board a vessel. Regular viscosity testing
is necessary to determine if the oil has thinned or thickened during use. A
comparison is made between the actual viscosity of used oil samples and an
unused sample. Low viscosity can lead to reduced oil film strength, which
weakens its ability to prevent metal-to-metal contact. On the other hand, high
viscosity can impede oil flow to essential locations in the bearing support
structure, reducing its lubrication capacity. Therefore, maintaining optimal
viscosity levels is crucial for ensuring the longevity and reliable performance
of the main engine.
Contamination.
Oil
contamination by water or coolant can lead to significant issues in the
lubricating system. As many lubricant additives have elements similar to those
used in coolant additives, accurate analysis of new oil is crucial for
comparison to detect contamination. This issue can be crucial for the main
engine in the marine industry, where contamination can lead to reduced engine
efficiency and potentially costly repairs. In addition, regular oil analysis
can help detect any contamination early, allowing for corrective measures to be
taken.
Fuel
Dilution. Oil dilution in a marine engine weakens the oil film strength,
sealing ability, and detergency, leading to potential machinery failures. In
addition, improper operation, fuel system leaks, ignition problems, improper
timing, or other deficiencies may cause it. Fuel dilution is considered
excessive when it reaches a level of 2.5 to 5 per cent, and it is crucial to
monitor oil samples for any signs of dilution to prevent potential damage to
the engine.
Solids
Content. The solid particle count test is a standard test used to measure
the percentage of solid materials present in a lubricating oil sample. The
amount of solids in the oil can significantly impact the wear of lubricated
parts in the engine. Therefore, an unexpected increase in the amount of solid
particles reported in the analysis is a cause for concern and may indicate a
potential problem in the lubrication system.
Fuel Soot.
Fuel
soot is always present to some extent in the oil used in diesel engines. Therefore,
a test to measure fuel soot in diesel engine oil is vital since it indicates
the fuel-burning efficiency of the engine. Infrared analysis is the most
commonly used method for testing fuel soot in diesel engine oil.
Oxidation.
Lubricating oil oxidation can cause the formation of lacquer
deposits, corrosion of metal components, or thickening of the oil. Most
lubricants contain oxidation inhibitors to prevent such problems. However, when
these additives are depleted, the oil becomes susceptible to oxidation. An oil
sample's oxidation degree can be measured using differential infrared analysis.
Nitration.
Fuel combustion in diesel engines results in the
formation of nitration products that are highly acidic and can cause deposits
in combustion areas, accelerating oil oxidation. Infrared analysis is a common
technique for detecting and measuring nitration products in lubricating oil
samples. The presence of nitration products can indicate engine performance or
fuel quality issues, and monitoring their levels can aid in predicting and
preventing potential engine problems.
Total
Acid Number. TAN (Total Acid Number) is a critical parameter for
determining the acidity of lubricating oils. The acid number of unused oil is
compared with that of a used oil sample to determine its acidic value. The
presence of acids in lubricating oil can lead to corrosion of metal surfaces
and other machinery components. Therefore, it is essential to monitor TAN
regularly to ensure that the oil remains within acceptable limits.
Total
Base Number. Total base number (TBN) measures an oil's ability
to neutralise acidity. A higher TBN indicates a greater ability to neutralise
acidity. In marine engines, low TBN can be caused by using the wrong type of
oil for the application, waiting too long between oil changes, overheating, and
using high-sulfur fuel. Therefore, regular analysis of TBN at specific
intervals is crucial for evaluating the oil's effectiveness in neutralising
acidity and preventing damage to the engine.
Particle
Count. Particle count analysis is essential to predicting
possible issues in hydraulic systems and other machinery. This type of analysis
is distinct from wear particle analysis and is usually included in a routine
lubricant analysis. High particle counts may indicate abnormal wear in
machinery or the possibility of temporary or permanent blockages in orifices
that could cause machine failures. However, the analysis is not designed to
determine the size, wear patterns, or other factors that could identify the
failure mode within the machine.
2.4.2.4.3.2
SPECTROGRAPHIC ANALYSIS
The spectrographic analysis is a
valuable tool for the marine industry to accurately and quickly measure the
presence of wear metals, contaminants, and additives in lubricating oil. This
information can help identify potential problems in machine-train components.
However, it is important to note that spectrographic analysis alone cannot
determine the specific failure modes of developing problems. Therefore, it
should be used with other techniques in a comprehensive predictive maintenance
program for marine vessels.
2.4.2.4.3.3
WEAR PARTICLE ANALYSIS
Wear particle analysis is an essential
technique in marine predictive maintenance programs, as it provides direct
information on the condition of the main engine. Studying particle shape,
composition, size, and quantity in the lubricating oil can provide important
information about the engine's mechanical condition. The wear particle analysis
is conducted in two stages. The first stage involves routine monitoring and
trending machine lubricant's solids content. Low levels of solids with a size
less than 10 μm indicate a normal machine condition, while an increase in the
number and size of particulate matter indicates the degradation of the
machine's condition. The second stage involves the analysis of the particulate
matter in each lubricating oil sample. The five basic types of wear that can be
identified according to the classification of particles are rubbing wear,
cutting wear, rolling fatigue wear, combined rolling and sliding wear, and
severe sliding wear. Rubbing wear and early rolling fatigue mechanisms generate
particles predominantly less than 15 μm in size. Wear particle analysis can
help detect potential problems before they become major issues, allowing for
timely maintenance and repair of the main engine.
Rubbing
Wear. In the marine industry, rubbing wear is expected in
the main engine and other machinery due to normal sliding wear. When a "wear"
surface is broken, a unique layer is formed at the surface, which allows the
surface to wear normally. However, if this layer is removed faster than it is
generated, the wear rate and the maximum particle size also increase. If the
lubrication system contains excessive contaminants, it can accelerate rubbing
wear by over ten times without completely removing the mixed shear layer.
Although catastrophic failure is unlikely, the machinery can wear out rapidly.
An increase in wear particles can indicate impending trouble. Therefore, it is
important to monitor the level of contamination in the lubrication system and
take necessary actions to prevent excessive wear.
Cutting
Wear Particles. These particles result from one surface penetrating
another, typically due to misaligned or fractured hard surfaces producing edges
that cut into softer surfaces or abrasive contaminants becoming embedded in
soft surfaces and cutting opposing surfaces. Cutting wear particles is not
normal and should always be investigated. If the particles are only a few
micrometres long and a fraction of a micrometre wide, they are likely caused by
contaminants. However, increasing quantities of longer particles are a warning
sign of potential component failure and should be addressed promptly; this is
especially important in the marine industry, where unexpected equipment failure
can have serious consequences.
Rolling
Fatigue. Rolling contact bearings are commonly used on
marine vessels, making this information relevant to predictive maintenance for
such machinery. Rolling fatigue wear can generate three types of particles:
fatigue spall particles, spherical particles, and laminar particles. Fatigue
spall particles are generated when a pit or spall opens up on a bearing surface
due to rolling fatigue, and an increase in their quantity or size indicates an
abnormality. Other sources can also generate spherical particles but are
important because their presence is detectable before spalling occurs. Laminar
particles are thin and often have holes, and they are thought to be formed by
the passage of a wear particle through a rolling contact. While they may be
generated throughout the life of a bearing, the quantity of laminar particles
increases at the onset of fatigue spalling.
Combined
Rolling and Sliding Wear. In the marine industry, this results
from moving contact of surfaces in propulsion systems. These larger particles
are generated from tensile stresses on the propeller surface, causing fatigue
cracks to spread deeper before pitting. Propeller fatigue cracks do not
generate spheres. Instead, propeller scuffing is caused by high loads or
speeds, generating excessive heat that breaks down the lubricating film and
causes adhesion of the mating propeller surfaces. As the wear surfaces become
rougher, the wear rate increases. Once started, scuffing typically affects each
propeller blade.
Severe
Sliding Wear. This is caused by excessive loads or heat in a gear
system, resulting in the breakaway of large particles from the wear surfaces.
This can cause a rapid increase in the wear rate. If the stresses on the
surface continue to increase, a second transition point is reached, causing the
surface to break down and leading to catastrophic wear.
However, regular spectrographic
analysis is limited to detecting particulate contamination with a size of 10 μm,
or less, and larger contaminants are often ignored. This limitation can
restrict the benefits obtained from this technique in identifying potential
issues with machinery.
2.4.2.4.4
FERROGRAPHY
Tribology analysis is a valuable tool
for predictive maintenance, but some limitations must be considered.
Ferrography is a technique similar to spectrography, but it uses a magnetic
field to separate ferrous or magnetic particles, making it better suited for
larger contaminants up to 100 μm in size. However, one major limitation is the
acquisition of accurate oil samples that genuinely represent the lubricant in
the machine. Therefore, careful and consistent sampling techniques should be
used to obtain representative samples from sample points consistent with
detecting large particles, such as drawing samples before filtration and not
from the bottom of a sump.
Sample frequency should be based on the
mean time to failure from the onset of an abnormal wear mode to catastrophic
failure. Sampling every 25 hours is recommended for critical machinery, while
monthly sampling is adequate for most continuous-service equipment. However,
for machines with extreme loads, weekly sampling is advised.
Interpreting analysis results is
another limiting factor, as the terminology used can be unfamiliar to vessel
engineers or attending maker technicians. It is crucial to have a good
understanding of quantitative and qualitative chemistry to interpret tribology
results accurately. Vessel engineering staff should be trained in basic
chemistry and receive specific instructions on interpreting tribology analysis
results.
2.4.2.4
PROCESS PARAMETERS
Many vessels do not consider machinery
or system efficiency part of the maintenance responsibility. However, machinery
not operating within acceptable efficiency parameters severely limits vessels'
productivity. Therefore, a comprehensive predictive maintenance program should
include routine monitoring of process parameters. As an example of the
importance of monitoring process parameters, consider a vessel's main engine, which
may be critical to its operation. Vibration-based predictive maintenance will
provide the mechanical condition of the engine, and infrared imaging will
provide the condition of the electric motor and bearings. Unfortunately, neither
provides any indication of the operating efficiency of the engine. Therefore,
the engine could operate at less than optimum efficiency, and the predictive
maintenance program would not detect the problem.
Like the example cited, process
inefficiencies are often a vessel's most serious limiting factor. Their
negative impact on vessel productivity and profitability is often more
significant than the total cost of the maintenance operation. However, many
vessels do not recognise this unfortunate fact without regularly monitoring
process parameters. If your program included monitoring the engine's fuel
consumption, exhaust gas temperature, and cylinder condition, you could
determine the operating efficiency. The brake-specific fuel consumption (BSFC)
formula could be used to calculate the engine's fuel efficiency. The engine's
power output can be estimated by measuring the exhaust gas temperature and
cylinder condition. With these measured data, the efficiency can be calculated.
Process parameters monitoring should
include all machinery and systems in the vessel's process that can affect its
production capacity. Typical systems include the main engine, generators,
pumps, boilers, fans, blowers, and other critical systems. The inclusion of
process parameters in predictive maintenance can be accomplished in two ways:
manual or microprocessor-based systems. However, both methods will normally
require installing instrumentation to measure the parameters that indicate the
actual operating condition of vessel systems. Even though most vessels have
installed pressure gauges, thermometers, and other instruments that should
provide the information required for this type of program, many of them are no
longer functioning. Therefore, including process parameters in your program
will require an initial capital cost to install calibrated instrumentation.
Data from the installed instrumentation can be periodically recorded using
either manual logging or a microprocessor-based data logger. If the latter is
selected, many vibration-based microprocessor systems can also provide the
means of acquiring process data. This should be considered when selecting the
vibration monitoring system used in your program. In addition, some
microprocessor-based predictive maintenance systems can calculate unknown
process variables. For example, they can calculate the main engine's fuel
efficiency used in the example. This ability to calculate unknowns based on
measured variables will enhance an entire vessel predictive maintenance program
without increasing the manual effort required. In addition, some of these
systems include nonintrusive transducers that can measure temperatures, flows,
and other process data without installing permanent instrumentation; this
further reduces the initial cost of including process parameters in your
program.
2.4.2.5
AI & MACHINE LEARNING USE IN PREDICTIVE MAINTENANCE
In AI and machine learning, predictive
maintenance refers to the ability to use volumes of data to anticipate and
address potential issues before they lead to breakdowns in operations,
processes, services, or systems. Having robust predictive maintenance tools
enables owners and vessel operators to anticipate when and where potential service
breakdowns can occur and respond to them to prevent service interruptions and
accrue off-hire.
One example of AI and machine learning
being used in predictive maintenance on board a tanker vessel is the
application of a machine learning algorithm to analyse data from vibration
sensors on the vessel's equipment. The algorithm is trained to recognise
patterns in the data that indicate potential equipment failures, such as
bearing wear or misalignment, and can alert maintenance personnel to
maintenance or repairs before a failure occurs.
Another example is using AI to monitor
and analyse real-time data from a vessel's engines and other critical systems.
By using machine learning algorithms to identify patterns and anomalies in the
data, the system can predict potential equipment failures or other issues
before they occur, allowing for proactive maintenance and repairs.
2.4.2.6
ELECTRIC MOTOR ANALYSIS
Evaluation of electric motors and other
electrical equipment is crucial for a comprehensive vessel predictive
maintenance program. While vibration data can identify some mechanical and
electrical problems in critical drive motors, it is insufficient for achieving
optimum vessel performance. Thus, a predictive maintenance program for a marine
vessel must include data acquisition and evaluation methods specifically
designed to detect issues in motors and other electrical equipment.
2.4.2.6.1
INSULATION RESISTANCE
Insulation resistance tests are crucial
in a comprehensive predictive maintenance program for vessels as they can
reveal flaws in insulation, poor insulating material, the presence of moisture,
and several other problems. Such tests can be conducted on the insulation of
electrical machinery, from the windings to the frame to underground cables,
insulators, capacitors, and other auxiliary electrical components. Various
instruments such as meggers, Wheatstone bridges, Kelvin double bridges, and
other specialised instruments can be used for conducting these tests.
Meggers are commonly used in the marine
industry to measure the condition of motor insulation directly. This method
uses a device that generates a known output, typically 500 V, and directly
measures the insulation's resistance within the motor. When the insulation
resistance falls below the prescribed value, the stator and rotor can be
cleaned and dried to return to the required standards. However, the accuracy of
meggering and most insulation resistance tests can vary widely with the parts'
temperature, humidity, and cleanliness. Therefore, additional testing and
inspections may be required to confirm the results of insulation resistance
tests.
2.4.2.6.2
OTHER ELECTRICAL TESTING
A comprehensive predictive maintenance
program for a marine vessel should include various testing and evaluation
methods to monitor all critical vessel systems regularly. Along with routine
vibration monitoring and oil analysis, the program should also include the
following techniques:
·
Dielectric loss analysis: This
method measures the electrical losses in insulating materials and can help
detect moisture ingress, ageing, and contamination.
·
Gas-in-oil analysis: This
test is used to identify any abnormal gases that may be present in the oil of
power transformers and other electrical equipment. Certain gases can indicate a
potential problem, such as overheating or arcing.
·
Stray field monitoring: Stray
field monitoring can detect any magnetic fields that may be present outside of
the normal magnetic circuits of electrical equipment, such as transformers or
motors; this can help identify potential issues with equipment insulation or
connections.
·
High-voltage switchgear discharge testing: This
test detects any potential issues with high-voltage electrical equipment, such
as switchgear or circuit breakers, that may be prone to arcing or discharges.
·
Resistance measurements: This
technique measures the resistance of electrical components such as
transformers, cables, and motors. Changes in resistance can indicate problems
such as corrosion or degradation of insulation.
·
Rogowski coils: These sensors can measure electrical
conductors' current without physically contacting the conductor. They are
useful in monitoring current fluctuations in large motors or generators.
·
Rotor bar current harmonics: This
test detects any issues with the rotor bars in AC motors, which can cause
vibration and other mechanical issues.
2.4.2.6
VISUAL INSPECTION
Regular visual inspection of the
machinery and systems on a vessel is necessary for any predictive maintenance
program. In many cases, a visual inspection will detect potential problems that
will be missed using other predictive maintenance techniques. Many potentially
serious problems can remain undetected even with the predictive techniques
discussed. Routine visual inspection of all critical vessel systems will
augment the other techniques and ensure potential problems are detected before
serious damage occurs. Most vibration-based predictive maintenance systems can
record visual observations during routine data acquisition.
Since the incremental costs of these
visual observations are small, this technique should be incorporated into all
predictive maintenance programs. All equipment and systems in the vessel should
be visually inspected regularly. The additional information provided by visual
inspection will augment the predictive maintenance program regardless of the
primary techniques used. This includes inspecting the hull, machinery,
electrical systems, fuel and oil lines, valves, pumps, and other critical
components.
2.4.2.7
ULTRASONIC MONITORING
This predictive maintenance technique
utilises the same principles as vibration analysis but focuses on monitoring
high-frequency ultrasound emissions rather than vibrations. Ultrasonic
monitoring is particularly useful in detecting valves, piping, and other
process system leaks. The range for ultrasonic monitoring is typically between
20,000 and 100 kHz, which is higher than the frequency range for vibration
analysis.
Two types of ultrasonic systems can be
used in predictive maintenance: structural and airborne. Airborne ultrasonic
detectors can detect gas pressure leaks and locate vacuum leaks. They can
operate in scanning and a contact mode, with a metal rod acting as a waveguide
in the contact mode. Ultrasonic transmitters can also be placed inside plant
piping or vessels to detect areas of sonic penetration along the container's
surface.
While ultrasonic monitoring can detect
machine dynamics such as gear meshing frequencies and blade pass, it cannot
differentiate these from bearing frequencies, which are monitored by vibration
analysis. Therefore, the use of ultrasonics to monitor bearing conditions is
not recommended. Nevertheless, incorporating ultrasonic monitoring in a
predictive maintenance program can provide additional insights into process
systems and detect leaks that other techniques may miss.
2.4.2.8
OPERATING DYNAMICS ANALYSIS
This analysis method, tailored to the
marine industry, is driven by vessel design and operating characteristics and
is not limited to traditional analysis techniques. The diagnostic logic is
derived from the vessel's machinery and systems' specific design and operating
characteristics. Based on the unique dynamics of each vessel's machinery or
system, all parameters defining optimum operating conditions are routinely
measured and evaluated. Then, using the logic of normal operating conditions,
operating dynamics can detect, isolate, and provide cost-effective corrective
action for any deviation from optimum.
Operating dynamics analysis combines
traditional predictive maintenance techniques into a holistic evaluation
technique that will isolate any deviation from the optimum condition of
critical vessel systems. This concept uses raw data derived from vibration,
infrared, ultrasonics, process parameters, and visual inspection but applies a
unique diagnostic logic to evaluate vessel systems. By using this method, operators
can ensure that their vessel systems operate at peak efficiency, reducing the
risk of costly failures and maximising the lifespan of the equipment.
2.4.2.8.9
MATHEMATICAL/DIGITAL TWIN
The use of mathematical/digital twins
in predictive maintenance on board vessels is becoming increasingly popular in
the marine industry. A digital twin is a virtual model of a physical system or
asset created using data from various sensors and systems. This virtual model
is used to predict how the physical system or asset will behave in different
conditions and can be used to identify potential issues before they occur.
In the marine industry, digital twins
can be used to monitor the performance of critical equipment such as engines,
propulsion systems, and navigational systems. By analysing data from sensors
and other sources, a digital twin can identify potential issues such as wear
and tear, corrosion, and other forms of damage. This allows maintenance crews
to take proactive steps to prevent problems before they occur, reducing the risk
of downtime and costly repairs.
One example of the use of digital twins
in the marine industry is in the monitoring of engine performance. By creating
a digital twin of the engine, data from sensors can be analysed to predict when
maintenance is required; this can help crews to schedule maintenance more
efficiently, reducing the risk of downtime and ensuring that the engine
operates at optimal levels.
Another use of digital twins in the
marine industry is monitoring ship systems such as ballast tanks and cargo
holds. By creating a digital twin of the ship, data from sensors can be analysed
to predict when maintenance is required, such as when tanks need to be cleaned
or when corrosion is likely to occur; this can help to reduce the risk of
damage to the ship and ensure that it operates safely and efficiently.
2.4.2.10
OTHER TECHNIQUES
Several nondestructive techniques are
available to identify potential marine equipment and systems issues. However,
some techniques may not provide comprehensive coverage or be too costly to
support a predictive maintenance program. Therefore, these techniques can
confirm failure modes identified by other predictive maintenance methods
discussed in this chapter. Some techniques that can complement predictive
maintenance in the marine industry include acoustic emissions, eddy currents,
magnetic particles, residual stress, and traditional nondestructive methods.
2.4.3
PROGRAM COSTS
Establishing and maintaining a
comprehensive predictive maintenance program for marine vessels will involve
initial and recurring costs. The costs will depend on the technology and system
chosen for the vessel. Although the initial or capital cost may be more
visible, the recurring labour, training, and technical support required to
maintain a total program will be the program's actual cost. When evaluating the
program's cost, it is essential to consider the long-term benefits of
implementing a predictive maintenance program, such as increased equipment
reliability, reduced downtime, and improved safety.
2.4.3.1
VIBRATION MONITORING
The capital cost for a vibration-based predictive maintenance program on a
vessel will vary depending on the specific techniques and equipment required.
It may range from $4,000 to more than $50,000 initial cost. Training is a
critical component of a successful predictive maintenance program based on
vibration monitoring and analysis. Even simplified programs that rely on
trending or comparison techniques require a practical knowledge of vibration
theory for meaningful interpretation of machine conditions. Advanced techniques
such as signature and root-cause failure analysis require knowledge of machine
dynamics and failure modes. Therefore, personnel involved in predictive maintenance
programs on vessels should receive appropriate training to ensure effective
implementation and interpretation of results.
2.4.3.2
THERMOGRAPHY
Point-of-use infrared thermometers and
infrared imaging systems are available in the marine industry for monitoring
the temperature of equipment and machinery. On average, a point-of-use infrared
thermometer costs less than $200, while infrared imaging systems range from $800
for a basic FLIR scanner (colour-imaging system )to over $10,000 for EX-proof
one. Training is critical for these imaging systems to obtain accurate data and
interpret the results. In addition, factors impacting the accuracy and consistency
of thermal data must be accounted for, and a solid understanding of thermal
imaging theory and application is required for effective analysis.
2.4.3.3
TRIBOLOGY
The capital cost of spectrographic analysis instrumentation for marine vessels
is typically too high to justify in-plant testing. For example, a
microprocessor-based spectrographic system for marine applications can cost
between $30,000 and $60,000. Therefore, most predictive maintenance programs
for vessels rely on third-party analysis of oil samples. Simple lubricating oil
analysis by a testing laboratory can range from about $20 to $50 per sample,
depending on the type and scope of analysis required. The standard analysis
will typically include viscosity, flash point, total insoluble, total acid
number (TAN), total base number (TBN), fuel content, and water content. More
detailed analysis using spectrographic or ferrographic techniques, including
metal scans, particle distribution (size), and other data, can range up to well
over $150 per sample, depending on the complexity of the analysis.
2.4.3.4
ULTRASONICS
Most ultrasonic monitoring systems are
scanners that do not provide long-term trending or storage of data, and they
are used as point-of-use instruments that indicate the overall amplitude of
noise within the instrument's bandwidth. Therefore, the cost of this type of
instrument is relatively low. Typically, ultrasonic instruments cost between
$1,000 and $8,000. While ultrasonic techniques require minimal training for
leak detection, caution should be exercised in applying this technique in a
predictive maintenance program. In addition, many ultrasonic systems are sold
as bearing condition monitors, even though the natural frequencies of
rolling-element bearings fall within the bandwidth of ultrasonic instruments.
However, this is not a reliable technique for determining the condition of
rolling-element bearings. Therefore, it is important to ensure that the
selected ultrasonic system is appropriate for the specific application and to
use it accordingly.
2.4.3.5 AI & MACHINE LEARNING
The cost of implementing AI and machine
learning for predictive maintenance will depend on the size and complexity of
the vessel and its equipment. The cost can range from tens to hundreds of
thousands of dollars for a smaller vessel and millions for a larger vessel with
more advanced technology.
The initial cost of implementing AI and
machine learning for predictive maintenance on a vessel can include the cost of
hardware, software, sensors, and data storage systems. The cost of training
personnel to operate and maintain the system should also be considered.
Additionally, ongoing costs such as software licensing, maintenance, and
upgrades should be considered.
Despite the initial cost, implementing
AI and machine learning for predictive maintenance can provide significant cost
benefits over the long term. The technology can help identify potential
equipment failures before they occur, allowing for timely repairs and minimising
downtime. It can also help optimise maintenance schedules and reduce the need
for manual inspections, which can save time and money.
2.4.3.5
BENEFITS
Properly implemented predictive
maintenance can significantly benefit the marine industry beyond just
scheduling maintenance tasks. Predictive maintenance, based on operating
dynamics, can yield substantial results. Using the four major loss
classifications, first-year results from a maintenance improvement program
based on a comprehensive predictive maintenance program for vessels may include
the following:
2.4.3.5.1
BREAKDOWN LOSSES
In the first year of implementation on
boar VLCC, a comprehensive predictive maintenance program reduced vessel delays
from machine and system breakdowns. All critical systems and machinery onboard
the vessel reflected a marked reduction in total delays. Therefore, limiting
the scope of unscheduled delays and reducing scheduled maintenance time is
crucial.
Accepting planned delays for
maintenance arbitrarily restricts available employment time, and historical
data should not be the sole reason for planned maintenance downtime. A
comprehensive predictive maintenance program for vessels and marine systems must
incorporate specific methods to evaluate all delays and downtime to achieve 100
per cent availability.
2.4.3.5.2
QUALITY DEFECTS
In the first year of a comprehensive
predictive maintenance program implemented on board the VLCC tanker, the number
of Oil Major ( BP, Exxon, Total) rejects, voyage diversions, and delays were
reduced, resulting in a decrease in negative costs associated with poor vessel
performance by more than $5 saving per ton of fuel used for sailing. Over the
next two years, the costs associated with poor service quality were further
reduced.
2.4.3.5.3
AVAILABILITY FACTOR
Setup and adjustment, reduced speed,
start-up losses, and operating efficiency of vessel processes directly affect
the overall vessel availability factor. Reduction of these major losses, in
conjunction with the reduction in delays and rejects, can result in an overall
increase in vessel availability for employment. Implementing a comprehensive
predictive maintenance program as part of a total vessel improvement program
can significantly improve net operating profit.
2.4.3.5.4
MAINTENANCE COSTS
Traditional maintenance costs such as
labour and materials are important factors in any predictive maintenance
program. Implementing a predictive maintenance program based on operating
dynamics can significantly reduce labour and material costs. Traditional
predictive maintenance applications may not reduce overall maintenance costs
within a vessel environment. Instead, they may only slightly decrease overall
costs, while material costs such as bearings and couplings may increase.
References
& Bibliography:
1.
https://www.datarobot.com/wiki/predictive-maintenance/#:~:text=In%20AI%20and%20machine%20learning,processes%2C%20services%2C%20or%20systems.
Disclaimer:
Out
of Box Maritime Thinker © by Naro Consilium Group 2022 and Aleksandar Pudar
assumes no responsibility or liability for any errors or omissions in the
content of this paper. The information in this paper is provided on an "as
is" basis with no guarantees of completeness, accuracy, usefulness, or
timeliness or of the results obtained from using this information. The ideas
and strategies should never be used without first assessing your company's
situation or system or consulting a consultancy professional. The content of
this paper is intended to be used and must be used for informational purposes
only.
