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Easier-to-interpret vibration data
13 February 2019
Collecting and analysing vibration data is a vital part of predictive maintenance strategies for rotating machinery, but deciphering the information is not always straightforward. Matthew Hurst explains how peak value analysis and auto correlation methodology makes vibration data easier to interpret
Vibration analysis is one of the most important techniques for monitoring the condition of rotating machinery as part of a predictive maintenance strategy. Analysing vibration data enables the early detection of problems such as misalignment, gearbox meshing issues, insufficient lubrication, pump cavitation and rolling element bearing defects. This helps to prevent unexpected failures that can cause safety incidents and production loss, and enables maintenance to take place during scheduled downtime, reducing costs and increasing plant efficiency.
Correctly interpreting the often-complex vibration data can be challenging, especially as companies don’t always have maintenance engineers with the necessary experience. However, by using peak value analysis and auto correlation methodology, the interpretation of vibration data can be made much more straightforward, helping even less experienced operators to identify problems easily.
Vibration data is collected from sensors monitoring the rotating equipment. The data can be collected manually via handheld vibration analysis devices, but critical equipment is often monitored on a continuous basis by online monitoring systems, to ensure that any indication of a potential problem is not missed between manual rounds.
In general, the analogue signal from a vibration sensor is routed via an analogue signal processor, converted into a digital format, then further processed digitally. When analysing vibration in rotating equipment, the most common form of signal processing is the Fourier transform. This uses an algorithm to enable the signal to be converted and to construct a vibration spectrum, providing information to help determine the source and cause of any problem.
Spectral analysis is the traditional method used to gain insight into machinery problems that create vibration, but its complexity makes data interpretation difficult. In contrast, peak value analysis methodology presents vibration data in a way that is easier for non-specialists to interpret.
Peak value analysis
Peak value analysis technology filters out traditional vibration signals to focus exclusively on impacting faults, where metal parts come into contact with each other, thereby providing a simple, reliable indication of equipment health. Peak values are observed over sequential discrete time intervals, captured, and then analysed. The analyses are the peak values; spectra computed from the peak value time waveform; and the auto correlation coefficient computed from the peak value time waveform. All three analysis tools enable the defect, and often its severity, to be identified.
As a measure of impacting, peak value analysis readings are much easier to interpret. A healthy machine that is correctly installed and well lubricated shouldn’t have any impacting. This establishes the zero principle: the peak value measurement on a healthy machine should be at, or close to, zero. As common machinery faults begin to appear on rotating equipment, the peak value reading typically can be evaluated using the so-called Rule of 10’s, which applies to rolling element bearing machines operating between 1000 and 4000 rpm. It simply states that when the peak value levels reach 10, there is some problem with the machine; when they double to 20 there is a serious problem; and when they double again to 40 there is a critical problem. Once an abnormal situation has been identified, detailed diagnostic information can be extracted from the peak value analysis waveform or spectrum to determine the nature of the defect.
Auto correlation is a time domain analysis, computed from the peak value time waveform, that is useful for determining the periodicity or repeating patterns of a vibration signal. The auto correlated waveform can be presented in a circular format, which makes interpretation of the data even more straightforward. Using auto correlation and circular displays, problems that were previously difficult to identify become easier to diagnose at an early stage.
Matthew Hurst is business development manager for asset performance at Emerson Automation Solutions.
- When analysing vibration in rotating equipment, the most common form of signal processing is the Fourier transform
- Peak value analysis technology filters out traditional vibration signals to focus exclusively on impacting faults
- Auto correlation is a time domain analysis, that is useful for determining repeating patterns