Automated Machine Learning for Fault Diagnosis Using Multimodal Mel-Spectrogram and Vibration Data

Zehao Li¹, Xuting Zhang¹, Hongqi Lin¹, Wu Qin², Junyu Qi³, Zhuyun Chen^1,*, Qiang Liu^1,*
1 Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System, and State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou, China
2 School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang, China
3 Electronics & Drives Centers, Reutlingen University, Reutlingen, Germany
* Corresponding Author: Zhuyun Chen. Email: email ; Qiang Liu. Email: email
(This article belongs to the Special Issue: Intelligent Dynamics Modeling, Predictive Operations & Maintenance, and Control Optimization for Complex Systems)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.075436

Received 31 October 2025; Accepted 12 January 2026; Published online 29 January 2026

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Abstract

To ensure the safe and stable operation of rotating machinery, intelligent fault diagnosis methods hold significant research value. However, existing diagnostic approaches largely rely on manual feature extraction and expert experience, which limits their adaptability under variable operating conditions and strong noise environments, severely affecting the generalization capability of diagnostic models. To address this issue, this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning (AutoML). The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary fault representations. On this basis, automated machine learning techniques are introduced to enable end-to-end diagnostic workflow construction and optimal model configuration acquisition. Finally, diagnostic decisions are achieved by automatically integrating the predictions of multiple high-performance base models. Experimental results on a centrifugal pump vibration and acoustic dataset demonstrate that the proposed framework achieves high diagnostic accuracy under noise-free conditions and maintains strong robustness under noisy interference, validating its efficiency, scalability, and practical value for rotating machinery fault diagnosis.