Condition monitoring
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Condition monitoring is the process of monitoring a parameter of condition in machinery, such that a significant change is indicative of a developing failure. It is a major component of predictive maintenance. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. It is typically much more cost effective than allowing the machinery to fail. Serviceable machinery include rotating machines and stationary plant such as boilers and heat exchangers.
[edit] Rotating machinery
The most commonly used method for rotating machines is called vibration analysis. Measurements can be taken on machine bearing casings with seismic or peizo-electric transducers to measure the casing vibrations, and on the vast majority of critical machines, with eddy-current transducers that directly observe the rotating shafts to measure the radial (and axial) vibration of the shaft. The level of vibration can be compared with historical baseline values such as former start-ups and shutdowns, and in some cases established standards such as load changes, to assess the severity.
Interpreting the vibration signal so obtained is a complex process that requires specialized training and experience. One commonly employed technique is to examine the individual frequencies present in the signal. These frequencies correspond to certain mechanical components (for example, the various pieces that make up a rolling-element bearing) or certain malfunctions (such as shaft unbalance or misalignment). By examining these frequencies and their harmonics, the analyst can often identify the location and type of problem, and sometimes the root cause as well. For example, high vibration at the frequency corresponding to the speed of rotation is most often due to residual imbalance and is corrected by balancing the machine. As another example, a degrading rolling-element bearing will usually exhibit increasing vibration signals at specific frequencies as it wears. Special analysis instruments can detect this wear weeks or even months before failure, giving ample warning to schedule replacement before a failure which could cause a much longer down-time.
Most vibration analysis instruments today utilize a Fast Fourier Transform (FFT) which is a special case of the generalized Discrete Fourier Transform and converts the vibration signal from its time domain representation to its equivalent frequency domain representation. However, frequency analysis (sometimes called Spectral Analysis or Vibration Signature Analysis) is only one aspect of interpreting the information contained in a vibration signal. Frequency analysis tends to be most useful on machines that employ rolling element bearings and whose main failure modes tend to be the degradation of those bearings, which typically exhibit an increase in characteristic frequencies associated with the bearing geometries and constructions. In contrast, depending on the type of machine, its typical malfunctions, the bearing types employed, rotational speeds, and other factors, the skilled analyst will often need to utilize additional diagnostic tools, such as examining the time domain signal, the phase relationship between vibration components and a timing mark on the machine shaft (often known as a keyphasor), historical trends of vibration levels, the shape of vibration, and numerous other aspects of the signal along with other information from the process such as load, bearing temperatures, flow rates, valve positions and pressures to provide an accurate diagnosis. This is particularly true of machines that use fluid bearings rather than rolling-element bearings. To enable them to look at this data in a more simplified form vibration analysts or machinery diagnostic engineers have adopted a number of mathematical plots to show machine problems and running characteristics, these plots include the bode plot, the waterfall plot, the polar plot and the orbit time base plot amongst others.
Handheld data collectors and analyzers are now commonplace on non-critical or balance of plant machines on which permanent online vibration instrumentation cannot be economically justified. The technician can collect data samples from a number of machines, then download the data into a computer where the analyst (and sometimes artificial intelligence) can examine the data for changes indicative of malfunctions and impending failures. For larger, more critical machines where safety implications, production interruptions (so-called "downtime"), replacement parts, and other costs of failure can be appreciable (determined by the criticality index), a permanent monitoring system is typically employed rather than relying on periodic handheld data collection. However, the diagnostic methods and tools available from either approach are generally the same.
[edit] Other techniques
- The most rudimentary form of condition monitoring is visual inspection by experienced operators and maintainers. Failure modes such as cracking, leaking, corrosion, etc can often be detected by visual inspection before failure is likely. This form of condition monitoring is generally the cheapest and is a vital part of workplace culture to give ownership of the equipment to the people that work with it. Consequently, other forms of condition monitoring should generally augment, rather than replace, visual inspection.
- Slight temperature variations across a surface can be discovered with visual inspection and non-destructive testing with thermography. Heat is indicative of failing components, especially degrading electrical contacts and terminations. Thermography can also be successfully applied to high-speed bearings, fluid couplings, conveyor rollers, and storage tank internal build-up.
- Using a Scanning Electron Microscope of a carefully taken sample of debris suspended in lubricating oil (taken from filters or magnetic chip detectors). Instruments then reveal the elements contained, their proportions, size and morphology. Using this method, the site, the mechanical failure mechanism and the time to eventual failure may be determined. This is called WDA - Wear Debris Analysis.
- Spectrographic oil analysis that tests the chemical composition of the oil can be used to predict failure modes. For example a high silicon content indicates contamination of grit etc, and high iron levels indicate wearing components. Individually, elements give fair indications, but when used together they can very accurately determine failure modes eg. for internal combustion engines, the presence of iron/aluminium, and carbon would indicate worn piston rings.
- Ultrasound can be used for high-speed mechanical applications and for high-pressure fluid situations. A high pitched 'buzzing sound' in bearings indicates flaws in the contact surfaces, and when partial blockages occur in high pressure fluids the orifice will cause a large amount of ultrasonic noise.
- Performance analysis, where the physical efficiency, performance, or condition is found by comparing actual parameters against an ideal model. Deterioration is typically the cause of difference in the readings. After motors, centrifugal pumps are arguably the most common machines. Condition monitoring by a simple head-flow test near duty point using repeatable measurements has long been used but could be more widely adopted. An extension of this method can be used to calculate the best time to overhaul a pump based on balancing the cost of overhaul against the increasing energy consumption that occurs as a pump wears.
[edit] The Criticality Index
The Criticality Index is often used to determine the degree on condition monitoring on a given machine taking into account the machines purpose, redundancy (i.e. if the machine fails, is there a standby machine which can take over), cost of repair, downtime impacts, health, safety and environment issues and a number of other key factors. The criticality index puts all machines into one of three categories:
- 1 - Critical machinery - Machines that are vital to the plant or process and without which the plant or process cannot function. Machines in this category include the steam or gas turbines in a power plant, crude oil export pumps on an oil rig or the cracker in an oil refinery. With critical machinery being at the heart of the process it is seen to require full online condition monitoring to continually record as much data from the machine as possible regardless of cost and is often specified by the plant insurance. Measurements such as loads, pressures, temperatures, casing vibration and displacement, shaft axial and radial displacement, speed and differential expansion are taken where possible. These values are often fed back into a machinery management software package (such as GE Energy's System 1) which is capable of trending the historical data and providing the operators with information such as performance data and even predict faults and provide diagnosis of failures before they happen.
- 2 - Machinery that is a key part of the process but if it fails the process can still operates. The machines that mainly fall under this category are machines which provide redundancy i.e. a process may need three pumps to operate but there may be four pumps so if one pump fails the spare (redundant or standby) pump can be utilized. These types of machine are normally boiler feed pumps in a power plant, air compressors and export pumps on an oil refinery. These machines have condition monitoring applied where possible but the level of which is usually specified by the cost of implementing the technology against the cost of failure of the machine. Some expensive/specialist machines in this category may have full online monitoring like critical machines whereas other machines such as large motors, which are easily replaced, may have monitoring systems that take measurements periodically rather than continuously
- 3 - Non-critical or balance of plant machines - These are the machines that make up the remainder of the plant and normally monitored using a handheld data collector as mentioned previously to periodically create a picture of the health of the machine.
