Dissimilarity-Based Fault Diagnosis through Ensemble Filtering of Informative Variables

Despite the fact that fault diagnosis, similar to pattern recognition, has been widely studied in recent years, two key challenges remain: insufficient training samples and overlapping characteristics faced by reference fault classes. Recognition of these challenges motivate this study. First, an ensemble filtering of informative variables, also serving as a dimensionality reduction step, is proposed to address the challenge of insufficient training samples vs high dimensionality. Second, to characterize the difference among overlapped fault classes, a dissimilarity analysis, that detects changes in a distribution of two data sets, is employed. A moving window technique with incrementally increasing window sizes is used to gather data from online abnormal samples as well as each reference fault class. The dissimilarity for a pairwise set of data windows is then computed using the informative variables. The fault class recognition depends on the minimum dissimilarity achieved by the reference fault classes at each moving window step. The comparisons demonstrate that the recognition performance of the proposed approach is considerably better than that of discriminate models as well as other pattern matching methods.