Machine Learning Model for Fault Detection in Safety Critical System

Common bearing failure modes (wear, contamination, corrosion, overload, misalignment, etc.) have unique characteristics, requiring diverse identification and mitigation strategies. No single definition can encompass all contributing factors. Understanding these complexities is crucial for safety critical system to implement fault detection. Machine learning offers a data-driven and intelligent approach to fault detection to improve safety, efficiency, and cost-effectiveness. In this work we propose a supervised machine learning model using naïve bayes classifier for safety critical system fault detection using time domain vibration features. Isolation forest-based anomaly detection is used for labeling faults or healthy condition. The proposed model is tested on the PRONOSTIA dataset, and the model detects fault before their failure criteria in all eleven experiments. The models hold promise for early fault detection in safety-critical systems.