An Intelligent Fault Classification Method Based on Data-Driven Stability Margin

Abstract

Thanks to rapid development of artificial intelligence (AI), a new branch of computer science, modern industry system becomes increasingly intelligent. What's more, mountains of data in industrial process can be saved for data-driven intelligent fault detection and classification. A method of intelligent data-driven fault classification based on stability margin is proposed in this paper, which gives a data-driven stability margin solution. As an important feature, the stability margin, together with the input and output (I/O) data, is input into the LM-BP neural network multi-classifier for fault classification. Moreover, the proposed method is demonstrated to be effective with high accuracy through a DC motor benchmark.