MSA-Mamba: A Frequency-Enhanced Multi-Scale Adaptive State Space Model for Motor Fault Diagnosis

Abstract

In complex industrial environments, vibration and acoustic signals are often corrupted by strong noise, resulting in non-stationary and low signal-to-noise ratio (SNR) characteristics that hinder effective feature extraction and degrade model generalization under limited samples. This study proposes a frequency-enhanced multi-scale adaptive state space Mamba (MSA-Mamba) model for motor fault diagnosis. A CNN Stem is first introduced for local feature extraction and sequence compression, followed by an adaptive frequency-domain filtering module to suppress noise and enhance fault-related frequency components. An MSA-Mamba Block combining multi-scale convolutional attention and parallel state-space modeling is then developed for adaptive feature fusion. In addition, Mixup and label smoothing strategies are employed to improve small-sample generalization. Experimental results show that MSA-Mamba achieves accuracies of 88.28%, 98.44%, and 99.48% on single-, three-, and four-channel datasets, respectively, and maintains 91.41% accuracy under strong-noise conditions. The proposed method demonstrates superior robustness and generalization for motor fault diagnosis.