Mining Abnormal Patterns from Heterogeneous Time-Series with Irrelevant Features for Fault Event Detection

We address the issue of detecting fault events in multivariate time series. We suppose the following realistic situation: A) the features to which multivariate time series correspond are heterogeneous; B) relative to a large number of normal examples, only a small number of examples of fault events are available in advance; and C) many features irrelevant to fault events are included. In such a situation, we require real-time, high-accuracy processing. We propose an algorithm to resolve the issue. Key ideas in it include: 1) transforming the time-series for each feature into a sequence of anomaly scores, in order to map heterogeneous features to homogeneous features (an anomaly score indicates the degree of anomaly relative to an ordinal sequence) and then representing the pattern of a fault event in terms of anomaly score vectors; 2) selecting features specifying a fault event by means of iterative optimization using both normal and fault anomaly score vectors. We then monitor the degree of abnormal with regard to test anomaly score vectors by matching with the abnormal patterns. We demonstrate the effectiveness of our proposed algorithm through an application to an actual automobile fault diagnosis data set.