MSFTNet: multi-scale frequency-temporal network for machinery fault diagnosis under complex working conditions

Abstract

Fault diagnosis is a cornerstone of mechanical equipment stability. With accurate health identification dictating system reliability, achieving high-reliability diagnosis is imperative. However, in practical scenarios, mechanical equipment often operates under variable speeds, fluctuating loads, and severe noise, leading to non-stationary vibration characteristics and feature distribution shifts that impede conventional diagnostic approaches. To address these challenges, a multi-scale frequency-temporal network (MSFTNet) is proposed. The framework begins by integrating frequency-domain transformation with large-kernel convolutions to extract features with a broad receptive field in the frequency domain. This design enhances sensitivity to weak fault signatures and improves robustness against noise. Subsequently, the network employs multi-scale convolutions coupled with a bidirectional long short-term memory network applied along the frequency dimension. This allows the model to concurrently capture fine-grained local patterns within specific frequency bands and model global contextual dependencies across different bands, effectively addressing the feature distribution shift induced by varying operational conditions. Finally, a residual enhancement module and a deep classifier are utilized to stabilize feature fusion and achieve precise fault classification. Extensive experimental results on several bearing and gear datasets, including SDUST and HUST, demonstrate the model’s powerful ability to extract weak fault features and excellent anti-interference performance. Specifically, MSFTNet achieves an average accuracy of 99.58% under variable speeds and maintains a robust accuracy of 70.14% even under extreme noise conditions (SNR = −6 dB), outperforming baseline methods across a range of complex scenarios.