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

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.