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With the development of industrial automation, the requirement of abnormal early warning in the industrial production process is getting higher and higher.Facing complex chemical processes, traditional fault detection and abnormal early warning methods have low detection efficiency and poor real-time performance. Therefore, this paper analyzes and studies fault detection and abnormal early warning methods, and puts forward an improved intelligent early warning method based on the moving window sparse principal component analysis (MWSPCA) suitable for complex chemical processes. The sparse principal component analysis algorithm is used to establish the initial early warning model, and then the moving window is used to update the early warning model, which makes the early warning model more suitable for the characteristics of time-varying data. Furthermore, the proposed method reduces the false alarm rate and missed alarm rate of the early warning, and improves the real-time performance of the early warning model. Finally, the feasibility and the validity of the proposed method are verified by the TE process and the oil drilling process. The experiment results show that the proposed method can reduce the risk of complex chemical processes. K E Y W O R D Scomplex chemical processes, fault detection, intelligent early warning, MWSPCA

Periodic components are of great significance for fault diagnosis and health monitoring of rotating machinery. Time synchronous averaging is an effective and convenient technique for extracting those components. However, the performance of time synchronous averaging is seriously limited when the separate segments are poorly synchronized. This paper proposes a new averaging method capable of extracting periodic components without external reference and an accurate period to solve this problem. With this approach, phase detection and compensation eliminate all segments' phase differences, which enables the segments to be well synchronized. The effectiveness of the proposed method is validated by numerical and experimental signals.