Advanced Statistical and Meta-heuristic based Optimization Fault Diagnosis Techniques in Complex Industrial Processes: A Comparative Analysis

Industrial processes are nonlinear and complicated, requiring accurate fault identification to minimize performance deterioration respond quickly emergencies. This work investigates industrial process defect isolation, which is analytically difficult owing their complexity. paper carefully analyzes four design methods for flaw isolation based on Principal Component Analysis (PCA), Fisher Discriminant (FDA), Kernel (KFDA), Sequential quadratic programming (SQP). Our study includes the Tennessee Eastman Process (TEP) Penicillin Fermentation (PFP), among other comparable methods. We assess proposed detection through detailed analysis comparison. The simulation findings from our extensive investigation provide remarkable insights. Simulation show that FDA KFDA well in but PCA has certain limits. also considered SQP as a TEP improvement tool. noted its success restricted optimization problems, making it ideal complicated processes. Data-driven approaches increase problem with greater reliability efficiency than PCA-based shows advanced data-driven techniques can improve diagnosis, improving operational safety system by leveraging FDA, KFDA, SQP.