Maintenance, predictive

Predictive maintenance or Condition Monitoring (CM) uses primarily non-intmsive testing techniques, visual inspections and performance data to assess equipment condition. It replaces arbitrarily timed maintenance tasks with maintenance scheduled only when warranted by equipment condition. Continuous analysis of equipment condition monitoring data allows planning and scheduling of maintenance or repairs in advance of catastrophic and functional failure. 

The CM data collected is used in one of the following ways to determine the condition of the equipment and to identify the precursors of failure  

Trend analysis - Reviewing data to see if the equipment is on an obvious and immediate downward slide toward failure (Newell (1999)). 

Pattern recognition - Looking at the data and realising the casual relationship between certain events and equipment failure (Parrondo et al. (1998)). 

Test against limits and ranges - Setting alarm limits (based on professional intuition) and seeing if they are exceeded (Sherwin and Al-Najjar (1999)). 

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Maintenance of process installation is still a necessity to realise high process reliability. Infrared thermography is becoming more and more an useful tool for predictive maintenance in the process and electrical industry. 

Precision casting Precursors of dyes Predelignification Predictive maintenance Predmcarb ate Prednisolone Prednisolone [50-24-8]... 

R. K. Mobley, M Introduction to Predictive Maintenance, Van Nostrand Reinhold, New York, 1990. 

Turbine and compressor online performance for use in equipment predictive maintenance. 

With a predictive maintenance strategy, long-term plans typically involve as few as only two regular procedures ... 

The standard refers to total planned preventive maintenance for which there is no definition in ISO/TS 16949. There is also a requirement for the system to include predictive maintenance. 

Predictive maintenance is part of planned preventive maintenance. In order to determine the frequency of checks you need to predict when failure may occur. Will failure occur at some future time, after a certain number of operating hours, when... 

REPEAT FAILURE UNEXPECTED FAILURE DESIGN PREVENTATIVE /PREDICTIVE MAINTENANCE PROGRAMME INSTALLATlON/COflR- ECTIVE/PREVEMTATIVE MAINTENANCE DIFFICULTY ... 

Troubleshooting, Predictive Maintenance and Controls for Distillation Columns... 

The use of vibration analysis is not restricted to predictive maintenance. This technique is useful for diagnostic applications as well. Vibration monitoring and analysis are the primary diagnostic tools for most mechanical systems that are used to manufacture products. When used properly, vibration data provide the means to maintain optimum operating conditions and efficiency of critical plant systems. Vibration analysis can be used to evaluate fluid flow through pipes or vessels, to detect leaks, and to perform a variety of non-destmctive testing functions that improve the reliability and performance of critical plant systems. 

Some of the applications that are discussed briefly in this section are predictive maintenance, acceptance... 

The fact that vibration profiles can be obtained for all machinery having rotating or moving elements allows vibration-based analysis techniques to be used for predictive maintenance. Vibration analysis is one of several predictive maintenance techniques used to monitor and analyze critical machines, equipment, and systems in a typical plant. However, as indicated before, the use of vibration analysis to monitor rotating machinery to detect budding problems and to head off catastrophic failure is the dominant predictive maintenance technique used with maintenance management programs. 

Predictive maintenance utilizing vibration signature analysis is based on the following facts, which form the basis for the methods used to identify and quantify the root causes of failure ... 

For routine monitoring of machine vibration, however, this approach is not cost effective. The time required to manually isolate each of the frequency components and transient events contained in the waveform is prohibitive. However, time-domain data has a definite use in a total plant predictive maintenance or reliability improvement program. 

It is important for predictive maintenance programs using vibration analysis to have accurate, repeatable data. In addition to the type and quality of the transducer, three key parameters affect data quality the point of measurement, orientation, and transducer-mounting techniques. 

An analysis is only as good as the data therefore, the equipment used to collect the data is critical and determines the success or failure of a predictive maintenance or reliability improvement program. The accuracy as well as proper use and mounting determines whether valid data are collected. 

Another method used by some plants to acquire data is hand-held transducers. This approach is not recommended if it is possible to use any other method. Hand-held transducers do not provide the accuracy and repeatability required to gain maximum benefit from a predictive maintenance program. If this technique must be used, extreme care should be exercised to ensure that the same location, orientation, and compressive load are used for every measurement. Illustrates a hand-held device. 

The only useful function of broadband analysis is longterm trending of the gross overall condition of machinery. Typically, sets of alert/alarm limits are established to monitor the overall condition of the machine-trains in a predictive maintenance program. However, this approach has limited value and, when used exclusively, severely limits the ability to achieve the full benefit of a comprehensive program. 

Reference or baseline data sets should be acquired for each machine-train or process system to be included in a predictive maintenance program when the machine is installed or after the first scheduled maintenance once the program is established. These data sets can be used as a reference or comparison for all future measurements. However, such data sets must be representative of the normal operating condition of each machine-train. Three criteria are critical to the proper use of baseline comparisons reset after maintenance, proper identification, and process envelope. 

Typically, a machine-train s vibration signature is made up of vibration components with each component associated with one or more of the true running speeds within the machine-train. Because most machinery problems show up at or near one or more of the running speeds, the narrowband capability is very beneficial in that high-resolution windows can be preset to monitor the running speeds. However, many of the microprocessor-based predictive maintenance systems available do not have narrowband capability. Therefore, care should be taken to ensure that the system utilized does have this capability. 

Use of heat emissions of machinery or plant equipment as a monitoring and diagnostic predictive maintenance tool. For example, temperature differences on a coupling indicate misalignment and/or uneven mechanical forces. 

Science of rotor-bearing-support system design and operation. Predictive maintenance technique that uses spectrographic, wear particle, ferrography, and other measurements of the lubricating oil as a diagnostic tool. 

Motion of fluids in which local velocities and pressures fluctuate irregularly, in a random manner. Predictive maintenance technique that uses principles similar to those of vibration analysis to monitor the noise generated by plant machinery or systems to determine their actual operating condition. Ultrasonics is used to monitor the higher frequencies (i.e., ultrasound) that range between 20,000 Hertz and 100 kiloHertz. 

This limitation prohibits the use of most microprocessor-based vibration analyzers for complex machinery or machines with variable speeds. Single-channel data-acquisition technology assumes the vibration profile generated by a machine-train remains constant throughout the data-acquisition process. This is generally true in applications where machine speed remains relatively constant (i.e., within 5 to fO rpm). In this case, its use does not severely limit diagnostic accuracy and can be effectively used in a predictive-maintenance program. 

Most predictive-maintenance programs rely almost exclusively on frequency-domain vibration data. The microprocessor-based analyzers gather time-domain data and automatically convert it using Fast Fourier Transform (FFT) to frequency-domain data. A frequency-domain signature shows the machine s individual frequency components, or peaks. 

The following parameters are monitored in a typical predictive-maintenance program for fans aerodynamic instability, running speeds, and shaft mode shape, or shaft deflection. 

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