Abstract

In manufacturing systems, multiple faults often occur simultaneously and generate complex fault patterns. However, most of the previous research on multiple fault diagnosis assumes that faults either occur one at a time or have linearly additive fault patterns. In this paper, we present a new approach for multiple simultaneous fault diagnosis. A comprehensive pattern modeling approach is developed for prediction based on functional regression. With the complete set of fault patterns, a minimum distance pattern classifier is designed for diagnosis. The proposed approach is demonstrated to be successful with a simulation study. An example from the resistance spot welding process is also provided. In this case, an 88% success rate for fault classification is achieved based on the predicted fault patterns.

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