Data-driven ensemble deep learning approach for rolling bearing failures monitoring using multiple source data
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. 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. 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. 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.