Machine Learning Techniques Application for Fault Detection and Classification in Control Systems

Machine learning application has been shown to be very effective in the detection and diagnosis of faults in control systems. Machine learning integration with these systems brings numerous benefits that include increased productivity, product quality improvement, and energy conservation. This research involves the use of machine learning techniques for control system fault detection and classification. An enhanced mathematical model using parameter estimation methodology is initially developed, which is referred to as the four-stage recursive identification algorithm. The suggested approach reduces the computational load and increases the estimated parameters accuracy. Next, the utilization of Machine learning algorithms in fault detection and classification comprises two principal phases: feature extraction and classification of fault. Feature extraction step is achieved through principal components analysis and quadratic classifier design is utilized for fault classification. The simulation example finally verifies the effectiveness of the suggested methodology.