A Text-Oriented Fault Diagnosis Method for Electromechanical Device Based on Belief Rule Base

Abstract

At present, quantitative data is often used for fault diagnosis of electromechanical devices, while qualitative data in the form of text is rarely used. In order to integrate qualitative data in the form of text and quantitative data in the fault diagnosis of an electromechanical device, a text-oriented fault diagnosis method based on belief rule base (BRB) is proposed in this paper. Specifically, the key information of fault diagnosis is extracted from the text through natural language processing (NLP) and then converted into belief rules. Then, a rule supplement method is adopted to add the extracted belief rules to the BRB for the completion of the BRB construction. This method applies qualitative data in the form of text to the process of BRB construction, which is a new attempt at the BRB construction method. It not only solves the problem that BRB cannot use qualitative data in text form but also improves the modeling accuracy and data comprehensive processing ability of BRB. To verify the effectiveness of the algorithm, we designed an experiment of asynchronous motor fault diagnosis in the case study. The experimental result shows that the proposed method can use qualitative data in text form to construct BRB and effectively diagnose faults of asynchronous motors. The MSE of the proposed method is 0.0451, which is better than that of traditional BRB (0.1461), BP (0.0613), and SVR (0.0974) under the same experimental conditions.

Keywords

• qualitative data
• text form
• belief rule base
• NLP