A Fault Diagnosis and Predictive Maintenance Algorithm for Mechanical Systems Based on Deep Learning

As modern industry rapidly expands and mechanical operating equipment undergoes continual advancement, mechanical systems are growing increasingly intricate, precise, and intelligent. Concurrently, the environments and conditions under which these systems operate are also escalating in complexity. To mitigate potential economic losses and prevent safety incidents, timely and accurate fault diagnosis of mechanical systems is paramount. In this study, we introduce a deep learning (DL)-driven algorithm for fault diagnosis and predictive maintenance in mechanical systems. This approach addresses the challenges inherent in traditional mechanical fault diagnosis methods, which often struggle with manually extracting uncertainty. Initially, sensor data pertaining to the mechanical system is gathered and undergoes preprocessing. Subsequently, a DL model is employed to extract pertinent features from the refined data, followed by the utilization of a classification model to categorize these features, facilitating automated fault diagnosis. By leveraging the DL model's ability to unearth hidden features within the data and utilizing a prediction model to forecast these features, we can ascertain the likelihood and potential time window for mechanical system failures. Experimental findings demonstrate that our proposed algorithm enhances the precision and reliability of fault diagnosis. Moreover, it enables the learning of temporal patterns and inherent laws governing mechanical systems, allowing for the prediction of future fault trends and occurrence timings.