Fault Diagnosis With Dual Cointegration Analysis of Common and Specific Nonstationary Fault Variations

Nonstationary variations widely exist in abnormal industrial processes, in which the mean values and variances of the fault nonstationary variables change with time. Thus, the stationary fault information may be buried by nonstationary fault variations resulting in high misclassification rate for fault diagnosis. Besides, the existing fault diagnosis methods do not consider underlying relations among different fault classes, which may lose important classification information. Here, it is recognized that different faults may not only share some common information but also have some specific characteristics. A fault diagnosis strategy with dual analysis of common and specific nonstationary fault variations is proposed here. The nonstationary variables and stationary variables are first separated using Augmented Dickey–Fuller (ADF) test. Then common and specific information is analyzed for fault diagnosis. Two models are developed, in which, the fault-common model is constructed by cointegration analysis (CA) to capture common nonstationary fault variations, and the fault-specific model is built to capture specific fault nonstationary variations of each fault class. With dual consideration of common and specific fault characteristics, the classification accuracy and fault diagnosis performance can be greatly improved. The performance of the proposed method is illustrated with both a well–known benchmark process and a real industrial process. Note to Practitioners—Process data analysis methods play an increasing important role in system maintenance and process monitoring in real industrial processes. The focus of this paper is to develop a fault diagnosis strategy with dual analysis of common and specific nonstationary fault variations. The proposed strategy can automatically describe the relationships between different fault classes using process data without complex mechanism knowledge. By exploring the relationship information between different faults, more accurate diagnosis models can be developed. Besides, the proposed strategy is a feasible technique for the nonstationary problem of complicated and varied industrial processes.

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