Engine Fault Analysis: Part I-Statistical Methods

Several studies have been performed to detect faults in engines. Fourier series and autocorrelation-based methods have been shown to be useful for this purpose. However, these and other methods discussed in the literature cannot locate the fault. In this paper, the focus is on techniques that will enable the location of the fault. In general, our approach involves the analysis of the instantaneous angular velocity of the flywheel. Three methods of analysis are presented. The first method depends on the computation of a set of statistical correlations. The second method is based on evaluation of similarity measures. These methods are able to locate faults in several tests that have been performed. The third approach uses pattern recognition methods and involves three stages-data extraction, functional approximation to determine a feature vector, and classification based on a Bayesian approach. This method is computationally more complex than the other approaches. However, on the basis of the experimental results it appears that the third method leads to a lower error rate. Cases involving faults in one and two cylinders are presented.