Monitoring multi-domain batch process state based on fuzzy broad learning system

• Published in: Expert systems with applications 2022-01, Vol.187, p.115851, Article 115851
• Main Authors: Peng, Chang, ChunHao, Ding
• Format: Article
• Language: English
• Subjects: Algorithms; Batch process state monitoring; Deep learning; Distance measurement; Fault detection; Feature extraction; Fermentation; Fuzzy broad learning system; Machine learning; Minor faults; Monitoring; Penicillin; Visualized classification
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.115851

In the real-world batch process, the minor faults caused by aging equipment and catalyst failure have subtle difference from normal data, making it difficult to monitor them timely with the traditional monitoring methods based on variance measurement and the deep learning methods based on distance measurement. In this research, a novel monitoring approach based on fuzzy broad learning system (FBLS) is employed to address the aforementioned issues. To fully extract the feature of raw data, the Takagi–Sugeno (TS) fuzzy system is first adopted to process the input data in order to identify minor faults effectively. Incremental learning algorithm is then employed to reconstruct network model quickly without retraining the entire network, which contributes to better accuracy and lower computation complexity and achieves online fault monitoring. After that, the classification of monitoring results is visualized to evaluate the fault type intuitively so as to take corresponding remedial actions quickly. Consequently, this algorithm is conducted into the penicillin fermentation platform and real industrial process. The results demonstrate that FBLS can better capture the fuzzified feature and quickly update monitoring model to accomplish the self-increase of fault database without retraining the entire process. •It is the first time FBLS for fault monitoring in batch process is introduced.•The method is able to distinguish the minor faults and improve monitoring performance.•Visualized classification is performed to recognize the fault types directly.•The novel method can simultaneously process the non-linearity and non-Gaussian of the data.