Optimal fuzzy attention deep learning enabled rotating machine fault diagnosis for sustainable manufacturing

Fault diagnoses of rotating machinery (RM) have played an important role in the safety and reliability of contemporary sustainable manufacturing systems. Extracting features from original signal is a fundamental process for conventional fault recognition performance which needs human intervention and expert knowledge. This article introduces a Modified Moth Flame Optimization with Fuzzy Attention Deep Learning Enabled Fault Diagnosis (MMFO-FADLFD) Model for RMs for the identification and classification of faults. It follows empirical mode decomposition (EMD)-based signal decomposition and principal component analysis (PCA)-based feature reduction processes and fuzzy attention based bidirectional long short term memory (FA-BLSTM) model. Further, the MMFO algorithm is applied as a hyperparameter tuning technique for enhanced fault classification outcomes. The experimental validation of the MMFO-FADLFD model is tested using a dataset and the outcomes are examined under varying aspects and it confirms a promising performance of the MMFO-FADLFD model over other recent methods.