This paper investigates the classification of a valve clearance fault in an internal combustion diesel engine using vibration time domain features extracted from signal segments measured at several points on the engine bloc. Signals containing a large number of engine cycles are used to obtain a number of observations of each feature. The set of features is thus considered a set of variables. A stepwise variable selection algorithm based on univariate and multivariate analysis of variance is then used to sort the variables according to their diagnostic ability. The algorithm is also used to construct sets of variables of increasing size used to improve fault classification. Four commonly used supervised classifiers are trained and then tested, giving roughly the same percentage of correct classification. The tested classifiers confirmed that the use of more variables selected by the stepwise variable selection algorithm increases the percentage of correct classification.
By using the unsupervised fuzzy clustering, this study attempts to design a new scheme for the unsupervised detection and classification of two injection faults using the time–frequency analysis of vibration signals of an internal combustion, four-stroke, diesel engine with six cylinders in-line. To reach this objective, two new methods called modified S-transform and two-dimensional non-negative matrix factorization are used. Three fuzzy clustering algorithms and nine cluster validity indices, for a variable number of classes, are also used to detect and classify the fault classes. The implementation of these methods resulted in a high detection rate of the injection faults.
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