Smart systems for real-time bearing faults diagnosis by using vibro-acoustics sensor fusion with Bayesian optimised 1-D CNNs

Diagnosis of bearing faults in real-time is challenging when healthy bearing conditions are mixed with faulty ones, affecting the overall system of rotating machinery. Deep Learning provides an effective approach for condition-based maintenance of bearing faults, bypassing traditional signal processing complexity. Convolutional Neural Networks (CNNs) excel in real-time fault detection in bearings, leveraging their feature extraction capabilities from heterogeneous sensors. Usually, selecting optimal hyperparameters for CNNs is time-consuming and impacts model performance. Recent literature demonstrates that CNNs-based models for detecting bearing faults typically undergo trial searches to select optimal hyperparameters, leading to time-intensive procedures. To fill this research gap, our study proposes a Bayesian optimised 1-D CNNs method to address hyperparameter tuning challenges. Using Machinery Fault Simulator®, we demonstrate the effectiveness of our approach in identifying various bearing fault conditions through vibro-acoustics sensors. Bayesian optimisation efficiently partitions datasets for parallel computation, optimises hyperparameters, and minimises loss functions to enhance validation accuracy. The proposed method achieved a test accuracy of 99.62%, surpassing the benchmark's 99.27%. Its effectiveness for bearing fault diagnosis is evident, compared to 96.76% without optimisation. Therefore, this study presents technical innovations, showcasing the diagnosis of diverse bearing faults with limited data through the integration of vibro-acoustics sensors.