Vibration monitoring as a predictive maintenance tool for reciprocating engines

Abstract

The vibration signature of a four-stroke, four-cylinder carburetted spark ignition engine has been analysed. The significance and contribution of the signature components with respect to the overall information about the engine health have been explored. The engine block vibrations were recorded at four different locations, two in the vicinity of the rear crankshaft bearing and two at opposing sides of the engine block. The vibrations were measured along the three principal axes. It was found that the engine block side is the most sensitive location for collecting data and the direction transverse to the pistons’ movement plane is the most informative one. For the sake of simplicity in possible practical applications in the future the tests were conducted under idle conditions. The measured vibration waveform has been synchronized with the crankshaft position by using the primary coil signal and then transformed to the frequency domain by a fast Fourier transform procedure. Frequencies below 1 Hz and above 10 kHz were filtered out. A reliable data persistence was obtained by averaging eight spectra. Spectral analyses of several common faults, such as disconnected spark plug, early spark timing, late spark timing, worn spark plug, fouled spark plug and loose support, have been carried out. Reflections of these faults have been revealed in the vibration signature. General evaluation criteria for the engine's health have been proposed. For example, the appearance of vibration components with unusual frequencies can provide an early warning that the engine is operating at abnormal conditions. The appearance of peaks at one-half or three-halves of the fundamental frequency indicates that a severe malfunction has developed in the engine. The appearance of a peak at the fundamental frequency implies that a mechanical looseness enables the engine to vibrate at the crankshaft frequency. In general terms, dispersion of the energy at subharmonic frequencies, or at six to eight times the fundamental frequency, indicates a developing malfunction.

An Approach to Reducing Input Parameter Volume for Fault Classifiers

“…Due to their prevalence and importance in industrial processes, there is naturally much interest in the detection and diagnosis of reciprocating compressor (RC) faults [35][36][37] . Of the two major faults groups, due to fail-ure of mechanical moving parts and those due to loss of elasticity in sealing components resulting in leaking gas, the latter is most prevalent and forms the focus of this illustration [36] .…”

“…The basic concept of FFT analysis is to reduce a complex signal in the time domain to its component parts in the frequency domain. Salient features of the signal thus become apparent as confusion due to noise is removed [36][37][38][39][40][41][42] . A combined approach using time-frequency analysis of vibration signals in conjunction with image-based pattern recognition techniques also realised high classification success rates [43] , the vibration signals with prior feature reduction using PCA and a Baysian classification approach were successful in assessment of discharge valve deterioration with 90% accuracy.…”