Data-driven ensemble deep learning approach for rolling bearing failures monitoring using multiple source data
Pauline Ong, Weng Hong Yap, Woon Kiow Lee, Chee Kiong Sia, Maznan Ismon, Kee Huong LaiPurpose
Rolling bearing failures remain a primary cause of induction motor breakdowns, creating significant reliability and maintenance challenges. This study aims to enhance fault diagnosis robustness under variable-speed conditions by addressing the limitations of fixed-speed datasets and single-model deep learning approaches.
Design/methodology/approach
An ensemble deep learning model (EDLM) is developed by integrating convolutional neural networks (CNN), deep belief networks (DBN) and stacked autoencoders (SAE). The framework applies a weighted-fusion strategy to exploit the complementary strengths of the base learners. Diagnostic performance is evaluated using three input modalities: raw vibration signals, spectrograms and infrared thermal images.
Findings
The EDLM consistently outperformed individual models across all metrics. Among the modalities, infrared thermal images achieved the highest diagnostic accuracy (98%), demonstrating superior capability in capturing subtle fault features under variable-speed conditions.
Originality/value
To the best of current knowledge, this study presents the first ensemble of CNN, DBN and SAE for bearing fault diagnosis under variable-speed conditions. By validating multi-sensor inputs and highlighting the diagnostic advantage of infrared thermography, the work provides a reliable and scalable solution for real-world machinery health monitoring.