Bearing Fault Diagnosis Using Machine Learning and Deep Learning Techniques

Machine Fault diagnosis plays a vital role in ensuring the reliability and efficiency of industrial systems. Among various components, roller element bearing is prone to failures due to critical function in supporting rotating machinery. This paper proposes a machine fault diagnosis approach specifically tailored for roller element bearings. The methodology combines vibration analysis, signal processing techniques, and machine learning algorithms to accurately classify bearing faults. Firstly, vibration signals are acquired from the machine using accelerometers, and relevant features are extracted using time-domain, frequency-domain, and statistical methods. Subsequently, autoencoders are also used to extract more features with the aid of existing features. Finally, few state-of-the-art machine learning algorithms such as support vector machines, random forests are trained to classify the fault types. Experimental results on a simulated dataset and real-world scenarios illustrate effectiveness and accuracy of proposed approach in diagnosing rolling bearing faults. This developed methodology offers a practical solution for conditional monitoring and predictive maintenance, enabling timely detection and mitigation of bearing faults, thereby enhancing system reliability, minimizing downtime, and reducing maintenance costs.