Fault detection using machine learning based dynamic ICA-distributed CCA: Application to industrial chemical process

•Novel approach: Introducing a novel machine learning based DICA-DCCA approach improved safety process management in industrial chemical processes.•Enhanced fault detection: DICA-DCCA ensures superior anomaly and fault detection with remarkable accuracy (FDR 100 % and FAR 0 %) compared to traditional approaches (ICA, DICA).•Robust practical application: The DICA-DCCA model shows robustness, effectiveness, and operational productivity in a continuous stirred tank reactor (CSTR) framework, highlighting its potential as a standardized benchmark for industrial safety. Unexpected accidents and events in industrial chemical processes have resulted in a considerable number of causalities and property damage. Safety process management in industrial chemical processes is critical to avoid and ensure casualties and property damage. However, due to the immense scope and high complexity of current industrial chemical processes, the traditional safety process management approaches cannot address these challenges to attain adequate fault detection accuracy. To address this issue, an innovative machine learning-based distributed canonical correlation analysis-dynamic independent component analysis (DICA-DCCA) approach is needed to improve the fault detection effectiveness of complicated systems. The (DICA-DCCA) model could potentially detect anomalies and faults in industrial chemical data by utilizing three essential statistics:Id2,Ie2and squared prediction error (SPE). The practical effectiveness of the proposed frameworks is evaluated and compared using a continuous stirred tank reactor (CSTR) framework as a standard benchmark study. The research findings present that the suggested (DICA-DCCA) approach is more resilient and effective in detecting abnormalities and faults than the ICA and DICA approaches with FDR 100 % and FAR 0 %. The implied research approach is robust, operational, and productive. [Display omitted]