Predictive maintenance techniques are designed to help determine the condition
of in-service equipment in order to predict when maintenance should be
performed. This approach promises cost savings over routine or time-based
preventive maintenance, because tasks are performed only when warranted.
The main promise of Predictive Maintenance is to allow convenient
scheduling of corrective maintenance, and to prevent unexpected equipment
failures. The key is "the right information in the right time". By
knowing which equipment needs maintenance, maintenance work can be
better planned and what would have been "unplanned stops" are transformed to
shorter and fewer "planned stops", thus increasing plant availability. Other
potential advantages include increased equipment lifetime, increased plant
safety, fewer accidents with negative impact on environment, and optimized
spare parts handling. Overview
PdM to evaluate the condition of equipment by performing periodic or
continuous equipment condition monitoring. The ultimate goal of PdM is
to perform maintenance at a scheduled point in time when the maintenance
activity is most cost-effective and before the equipment loses performance
within a threshold. This is in contrast to time- and/or operation count-based
maintenance, where a piece of equipment gets maintained whether it needs it or
not. Time-based maintenance is labor-intensive, ineffective in
identifying problems that develop between scheduled inspections, and is
not cost-effective. The "predictive" component of predictive
maintenance stems from the goal of predicting the future trend of the
equipment's condition. This approach uses principles of statistical process
control to determine at what point in the future maintenance activities will
be appropriate. Most PdM inspections are performed while
equipment is in service, thereby minimizing disruption of normal system
operations. Adoption of PdM can result in substantial cost savings and higher
system reliability. Reliability-centered maintenance, or
RCM, emphasizes the use of predictive maintenance techniques in addition to
traditional preventive measures. When properly implemented, RCM provides
companies with a tool for achieving lowest asset Net Present Costs for a
given level of performance and risk. One area that many times is overlooked
is how to, in an efficient way, transfer the PdM data to a computerized
maintenance management system system so that the equipment condition data is
sent to the right equipment object in the CMMS system in order to trigger
maintenance planning, execution and reporting. Unless this is achieved, the
PdM solution is of limited value, at least if the PdM solution is implemented
on a medium to large size plant with tens of thousands pieces of equipment.
In 2010, the mining company Boliden, as a first, implemented a combined
Distributed Control System and Pdm solution integrated with the plant CMMS
system on an object to object level, transferring equipment data using
protocols like Highway Addressable Remote Transducer Protocol, IEC61850 and
OLE for process control. Technologies
To evaluate equipment condition, predictive maintenance utilizes
nondestructive testing technologies such as infrared, acoustic, corona detection,
vibration analysis, sound level measurements, oil analysis, and other
specific online tests. A new approach in this area is to utilize measurements on
the actual equipment in combination with measurement of process performance,
measured by other devices, to trigger equipment maintenance. This is primarily
available in Collaborative Process Automation Systems(CPAS). Site
measurements are often supported by wireless sensor networks to reduce the
wiring cost. Vibration analysis is most productive on
high-speed rotating equipment and can be the most expensive component of a PdM
program to get up and running. Vibration analysis, when properly done, allows the
user to evaluate the condition of equipment and avoid failures. The latest
generation of vibration analyzers comprises more capabilities and
automated functions than its predecessors. Many units display the
full vibration spectrum of three axes simultaneously, providing a snapshot of
what is going on with a particular machine. But despite such capabilities,
not even the most sophisticated equipment successfully predicts
developing problems unless the operator understands and applies the basics of
vibration analysis. Acoustical analysis can be done on a
sonic or ultrasonic level. New ultrasonic techniques for condition
monitoring make it possible to “hear” friction and stress in rotating
machinery, which can predict deterioration earlier than conventional
techniques. Ultrasonic technology is sensitive to high-frequency sounds that
are inaudible to the human ear and distinguishes them from lower-frequency
sounds and mechanical vibration. Machine friction and stress waves produce
distinctive sounds in the upper ultrasonic range. Changes in these
friction and stress waves can suggest deteriorating conditions much earlier
than technologies such as vibration or oil analysis. With proper ultrasonic
measurement and analysis, it’s possible to differentiate normal wear from
abnormal wear, physical damage, imbalance conditions, and lubrication
problems based on a direct relationship between asset and operating conditions.
Sonic monitoring equipment is less expensive, but it also has fewer uses
than ultrasonic technologies. Sonic technology is useful only on mechanical
equipment, while ultrasonic equipment can detect electrical problems and is
more flexible and reliable in detecting mechanical problems.
Infrared monitoring and analysis has the widest range of application, and it can
be effective for spotting both mechanical and electrical failures; some
consider it to currently be the most cost-effective technology. Oil analysis
is a long-term program that, where relevant, can eventually be more
predictive than any of the other technologies. It can take years for a
plant's oil program to reach this level of sophistication and effectiveness.
Analytical techniques performed on oil samples can be classified in two
categories: used oil analysis and wear particle analysis. Used oil analysis
determines the condition of the lubricant itself, determines the quality
of the lubricant, and checks its suitability for continued use. Wear
particle analysis determines the mechanical condition of machine
components that are lubricated. Through wear particle analysis, you can identify
the composition of the solid material present and evaluate particle type,
size, concentration, distribution, and morphology.
The use of Model Based Condition Monitoring for predictive maintenance
programs is becoming increasingly popular over time. This method involves
spectral analysis on the motor’s current and voltage signals and then compares
the measured parameters to a known and learned model of the motor to diagnose
various electrical and mechanical anomalies. This process of "model based"
condition monitoring was originally designed and used on NASA’s space
shuttle to monitor and detect developing faults in the space shuttle’s main
engine. It allows for the automation of data collection and analysis tasks,
providing round the clock condition monitoring and warnings about faults as
they develop. This method has been uniquely adapted into a tool called
“Artesis MCM” for industrial motors, generators and driven equipment by the
Turkish company Artesis. In an agreement with General Electric the product was
rebranded “AnomAlert”. Artesis has received the “Editor’s Choice Award” for
the 40 best products of 2000 by control Engineering USA and the “Technology
Innovation Award 2007” for simplifying predictive maintenance by the
Institution of Engineering in the UK for their work on the AnomAlert Motor
Anomaly Detector. See also
RCASE Root cause analysis
References