Dynamic hidden variable fuzzy broad neural network based batch process anomaly detection with incremental learning capabilities

•The method is more conducive to realizing fault monitoring and improves the monitoring accuracy.•The slow feature information captured are fed into the monitoring model for fault monitoring.•The novel method can simultaneously process the non-linearity and dynamic of the data. Affected by the operation environment and uncertainties, batch processes have complex dynamic characteristics, presenting autocorrelation and mutual correlation among process variables. Many conventional methods tend to ignore this property when constructing monitoring models, resulting in inadequate process feature extraction and unsatisfactory monitoring performance. A novel monitoring method named Dynamic Hidden Variable Fuzzy Broad Neural Network (DHVFBN) monitoring model is constructed for batch processes to address the aforementioned issues. For the details, in order to capture dynamic feature and nonlinearity in batch process, Slow Feature Analysis (SFA) is first used to extract the slowly changing components and sorting them from the raw time series data, which has strong capability in dynamic feature processing. In the monitoring model, the incremental learning ability of Fuzzy Broad Learning System (FBLS) is adopted to complete the quick reconstruction and expeditiously updating of the monitoring model without having to retrain the entire network when new fault samples are added to the training set or the accuracy of the network barely meet the requirements, which hugely relieves the computation burden and thus accomplishes the online fault surveillance. In addition, to fully extract the feature of process data, the fuzzy mechanism of the FBLS is then selected to extract the full fuzzified feature information of the process data so as to identify the slight difference between abnormal and normal process data effectively, which can improve the performance of fault monitoring. Finally, this method is evaluated by conducting experiments on the penicillin fermentation platform and Real-world industrial application. Compared with common state-of-the-art methods involved, the monitoring results indicate that the DHVFBN outperforms them.