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

ABSTRACT

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.

KEYWORDS:

• Bearing fault diagnosis
• condition-based maintenance
• hyperparameter tuning
• loss function value
• vibro-acoustic sensor fusion

Acknowledgments

The authors express their gratitude for the valuable feedback provided by both the reviewers and editors, acknowledging their contributions in enhancing the quality of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data can readily be shared on the basis of a request.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.