Robust Monitoring and Fault Isolation of Nonlinear Industrial Processes Using Denoising Autoencoder and Elastic Net

• Published in: IEEE transactions on control systems technology Vol. 28; no. 3; pp. 1083 - 1091
• Main Authors: Yu, Wanke, Zhao, Chunhui
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Coders; Data mining; Denoising autoencoder (DAE); Discriminant analysis; elastic net (EN); fault isolation; Feature extraction; Kernel; kernel density estimation (KDE); Monitoring; Neurons; Noise reduction; Nonlinearity; Principal component analysis; process monitoring; Robustness; Statistical methods
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2019.2897946

Robust process monitoring and reliable fault isolation in industrial processes usually encounter different challenges, including process nonlinearity and noise interference. In this brief, a novel method denoising autoencoder and elastic net (DAE-EN) is proposed to solve the aforementioned issues by effectively integrating DAE and EN. The DAE is first trained to robustly capture the nonlinear structure of the industrial data. Then, the encoder network is updated into a sparse model using EN, so that the key variables associated with each neuron can be selected. After that two statistics are developed based on the extracted systematic structure and the retained residual information. In addition, another statistic is also constructed by combining the aforementioned two statistics to provide an overall measurement for the process sample. In this way, a robust monitoring model can be constructed to monitor the abnormal status in industrial processes. After the fault is detected, the faulty neurons are identified by the sparse exponential discriminant analysis, so that the associated faulty variables along each faulty neuron can thus be isolated. Two real industrial processes are used to validate the performance of the proposed method. Experimental results show that the proposed method can effectively detect the abnormal samples in industrial processes and accurately isolate the faulty variables from the normal ones.