The condition monitoring of diesel engines using acoustic measurements part 2: Fault detection and diagnosis

In this paper, the focus is upon the condition monitoring implications of acoustic monitoring. Namely, its ability to detect, diagnose and locate incipient deterioration in a number of common failure modes. To improve the reliability of the condition monitoring procedures, the noise contaminated signals are conditioned based upon the results of speed and load dependency investigation of the identified low and high frequency regions of sound. High pass filtering is shown to eliminate much of the environmental dependency of the monitored signals, whilst retaining the pertinent condition indicating information content. Real fault testing shows that the location of different faults, their influences upon combustion, and the ability to distinguish between them can all be extracted from the shape of the contours in the Continuous Wavelet Transform (CWT). The sister paper to this ("Part 1 - Acoustic Characteristics of the Engine and Representation of the Acoustic Signals"), the sound generation of a diesel engine is modelled based upon the combustion process. Real monitored data is shown to be highly contaminated, and the representation of acoustic signals using the smoothed pseudo-Wigner-Ville distribution (SPWVD) and continuous wavelet transform (CWT), however, is found to permit recognition of the adverse influences of the measurement environment.