New types of faults detection and diagnosis using a mixed soft & hard clustering framework

In general, one limitation in current diagnosis approaches is that they could only detect the existing types of faults, while not be able to detect new types of faults. It is difficult to know in advance all fault types and new types of faults may occur in industry. As such, effective detection and diagnosis on new types of faults are important. In this paper, a novel mixed soft&hard assignment clustering framework will be proposed to detect and diagnose new types of faults based on the feature signals. As a popular soft assignment strategy, Gaussian mixture model targets to diagnose existing types from training and detect new category. Next, the hard assignment strategy based on the Euclidean distance of K-means is used to further classify the fault details if the new category is detected. Effectiveness of the proposed framework is testified on a partial discharge measurement dataset of different high voltage electronic and power equipment in industry. It is able to achieve as good performance as benchmark approaches for conventional diagnosis without new fault category, while it also effectively detects and classifies new types of faults with average accuracy of 75.0%.