Fault Diagnosis of Motors via Multi-state Space Modeling Mamba

XU Yongjun; ZENG Dechan

doi:10.19554/j.cnki.1001-3563.2025.15.031

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Packaging Engineering ›› 2025, Vol. 46 ›› Issue (15) : 269-276. DOI: 10.19554/j.cnki.1001-3563.2025.15.031

Automatic and Intelligent Technology

Fault Diagnosis of Motors via Multi-state Space Modeling Mamba

XU Yongjun¹, ZENG Dechan^1,2

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Abstract

The work aims to enhance the accuracy and efficiency of multi-state fault diagnosis for motor systems in high-load continuous packaging equipment, addressing the limitations of traditional methods in capturing complex dynamic features and achieving model lightweighting. A selective state space model (Mamba) was constructed based on time-frequency domain multimodal signal feature fusion. Fast Fourier Transform was firstly applied to extract global frequency-domain features from motor vibration signals, while time-frequency domain images were generated by continuous wavelet transform to capture local dynamic characteristics. A structured state space modeling framework was then designed to establish differential equation models for dynamic evolution trajectories of motor health states. Finally, a lightweight classifier was developed for multimodal feature collaborative inference. Experiments on CWRU and TBVD datasets demonstrated that time-frequency feature fusion achieved 99.88% fault classification accuracy, showing 7.2% improvement over single-modal methods. The Mamba model required only 2.7×10⁶ parameters, representing over 39.3% reduction compared to traditional diagnosis models, with 3.8× faster inference speed. The proposed multi-state space modeling method effectively realizes dynamic characterization and efficient fusion of motor fault features in packaging equipment, significantly improving diagnosis accuracy while maintaining model lightweighting, providing an engineering-deployable solution for intelligent maintenance systems.

Key words

time-frequency feature fusion / packaging equipment fault diagnosis / selective state space model (Mamba) / lightweight model / intelligent fault diagnosis

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XU Yongjun, ZENG Dechan. Fault Diagnosis of Motors via Multi-state Space Modeling Mamba[J]. Packaging Engineering. 2025, 46(15): 269-276 https://doi.org/10.19554/j.cnki.1001-3563.2025.15.031

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