A Hierarchical Stochastic Network Approach for Fault Diagnosis of Complex Industrial Processes

• Published in: IEEE/CAA journal of automatica sinica Vol. 12; no. 8; pp. 1683 - 1701
• Main Authors: Lv, Mingjie, Li, Yonggang, Gao, Huanzhi, Sun, Bei, Huang, Keke, Yang, Chunhua, Gui, Weihua
• Format: Journal Article
• Language: English
• Published: Chinese Association of Automation (CAA) 01.08.2025
• Subjects: Autoencoders; Complex industrial processes; Fault diagnosis; Feature extraction; hierarchical structure; multi-class fault diagnosis; Noise; Noise measurement; Overfitting; Principal component analysis; stochastic network; Stochastic processes; Sun; Uncertainty
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2025.125249

Complex industrial processes present typical uncertainty due to fluctuations in the composition of raw materials and frequently changing operating conditions. This poses three challenges for precise fault diagnosis, including random noise interference, less distinguishability between multi-class faults, and the new fault emerging. To address these issues, this study formulates fault diagnosis in uncertain industrial processes as a multi-level refined fault diagnosis problem. A hierarchical stochastic network approach is proposed to refine fault diagnosis of multi-class faults. This method considers the augmentation of fault categories as naturally following a hierarchical structure. At each hierarchical stage, stochastic network methods are designed according to the sources of uncertainty. For fault feature extraction, a doubly stochastic attention-based variational graph autoencoder is introduced to suppress noise during the message-passing process, ensuring the extraction of high-quality fault features and providing the provision of differentiated information. Subsequently, multiple stochastic configuration networks are deployed to realize multi-level fault diagnosis from coarse to fine granularity via a hierarchical structure rather than treating all faults equally. This approach effectively enhances the precision of multi-class fault diagnosis and ensures its robust generalization capability. Finally, the feasibility and effectiveness of the proposed method are validated using two industrial processes. The results demonstrate that the proposed method can effectively suppress the random noise interference and adapt to the emergence of small samples and imbalanced extreme fault-type data, achieving a satisfactory fault diagnosis performance.