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Journal Article
AnUpper-Probability-Based Softmax Ensemble Model for Multi-Sensor Bearing Fault Diagnosis
- Authors
- Hangyeol Jo, Yubin Yoo, Miao Dai, Sang-WooBan
- Issue Date
- 2025-11
- Citation
- Sensors, v.25, no.22, pp.1-17
- ISSN
- 1424-8220
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Language
- English
- Type
- Journal Article
- Abstract
- In bearing fault diagnosis for rotating machinery, multi-sensor data—such as acoustic and vibration signals—are increasingly leveraged to enhance diagnostic performance. However, existing methods often rely on complex network architectures and incur high computational costs, limiting their applicability in real-time industrial environments. To address these challenges, this study proposes a lightweight and efficient multi-sensor ensemble framework that achieves high diagnostic accuracy while minimizing computational overhead. The proposed method transforms vibration and acoustic signals into spectrograms, which are independently processed by modality-specific lightweight convolutional neural networks (CNNs). The softmax outputs from each classifier are integrated using an AdaBoost-based ensemble strategy that emphasizes high-confidence predictions and adapts to sensor-specific misclassification patterns. Experimental results on benchmark datasets—UORED-VAFCLS, KAIST, and an in-house bearing dataset—demonstrate an average classification accuracy exceeding 99.90%, with notable robustness against false positives and missed detections. Furthermore, the framework significantly reduces resource consumption in terms of FLOPs, inference latency, and model size compared to existing state-of-the-art multi-sensor fusion approaches. Overall, this work presents a practical and deployable solution for real-time bearing fault diagnosis, balancing classification performance with computational efficiency without resorting to complex feature fusion mechanisms.
- KSP Keywords
- Acoustic and vibration, Acoustic signal, Bearing fault diagnosis, Benchmark datasets, Classification Performance, Complex network(CN), Computational Efficiency, Convolution neural network(CNN), Diagnostic accuracy, Ensemble strategy, False Positive(FP)
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
