Deep Learning Approaches for Predictive Maintenance and Intelligent Fault Diagnosis

Deep-learning-based intelligent fault diagnosis methods are the new research hotspots in the fault diagnosis field. Auto detection and correct detection of the emerging micro-fault of rotating machinery, particularly with respect to fault orientation and level of severity, remains a significant issue in the area of intelligent fault diagnosis. To detect an early fault successfully in rotating machinery, this paper presents a Hybrid Recurrent Neural Network and Gated Recurrent Unit (RNN +GRU) that can be used to diagnose intelligent faults on the Case Western Reserve University (CWRU) bearing dataset. The suggested hybrid model covers both the short- and long-term temporal characteristics of bearing vibration signals. Experimental outcomes show high-quality performance with an accuracy of 99.7, a precision of 99.4, a recall of 99 and an F1-score of 99.8, which is considerably better than the traditional machine learning models as well as standalone deep learning models. Such findings confirm how effective and appropriate the proposed solution is in terms of high-precision predictive maintenance and intelligent diagnosis of faults in industries.