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ClassBD Outperforms Competitors in Real-World Bearing Fault Diagnosis Using PU Dataset
- The article highlights the ClassBD approach for real-world bearing fault diagnosis using the PU dataset, which is capable of overcoming the limitations of existing approaches.
- The PU dataset was collected by Paderborn University (PU) Bearing Data Center and has 32 bearings including vibration and current signals. The bearings were categorized into three groups, including healthy and manually-induced or real damage included accelerated lifetime tests.
- In this study, the authors exclusively use the real damaged bearings for classification for validation in real-world scenarios, and the datasets include 5776 training data, 1444 validation data, and 380 test data.
- The ClassBD model shows superior performance compared to its competitors across various noise levels and operating conditions and delivers competitive performance across diverse high-noise scenarios.
- In the challenging scenario of extremely limited sample availability, ClassBD, EWSNet, and DRSN models exhibit commendable performance. All three methods achieve over 90% F1 scores with a small dataset.
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