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<p>Rolling bearings are critical components of rotating machinery. The operational status of these bearings directly impacts the stability and safety of the entire system. However, conventional data-driven models frequently demonstrate deficiencies in generalization and robustness when detecting early, subtle fault features. The present paper puts forth a novel approach to fault diagnosis, namely PIFC-SABiLSTM (Physics-Inspired Feature Constraint with Self-Attention BiLSTM). PIFC-SABiLSTM is a data-driven model that incorporates physics-inspired feature constraints with a self-attention mechanism and a bidirectional long short-term memory (BiLSTM) network. This integration aims to address the limitations of conventional data-driven models, particularly their poor generalization and limited robustness. This approach is predicated on the transformation of the statistical patterns of rolling bearing vibration characteristics during steady-state operation into differentiable and physically feasible domain constraints. A Physics-Inspired Feature Constraint (PIFC) layer is constructed, where a projection operator guides the learning process of the network to conform to actual vibration physics and suppress the generation of non-physical features. This approach effectively decouples and fuses multi-scale time-frequency features by integrating the global dependency modeling capability of self-attention mechanisms for periodic impact patterns with the bidirectional temporal capture capability of Bidirectional Long Short-Term Memory (BiLSTM) for transient events. The experimental findings on the MFPT (Mechanical Faults Prevention Technology) bearing dataset demonstrate that the proposed method achieves 98.70% accuracy and an F1 score exceeding 98%, thereby significantly outperforming existing mainstream methods. This research proposes a novel “data-mechanism” synergistic optimization approach for high-precision, intelligent fault diagnosis in complex industrial environments. The approach demonstrates significant practical value for engineering applications.</p>

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PIFC-SABiLSTM: physics-inspired feature constraint with self-attention bidirectional LSTM for interpretable fault diagnosis of rolling bearings

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  • 出版模式：

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  • 发表日期：

    2026年5月22日

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  • Wanjun Qin,
  • Jie Ma,
  • Qiao Peng

摘要

Rolling bearings are critical components of rotating machinery. The operational status of these bearings directly impacts the stability and safety of the entire system. However, conventional data-driven models frequently demonstrate deficiencies in generalization and robustness when detecting early, subtle fault features. The present paper puts forth a novel approach to fault diagnosis, namely PIFC-SABiLSTM (Physics-Inspired Feature Constraint with Self-Attention BiLSTM). PIFC-SABiLSTM is a data-driven model that incorporates physics-inspired feature constraints with a self-attention mechanism and a bidirectional long short-term memory (BiLSTM) network. This integration aims to address the limitations of conventional data-driven models, particularly their poor generalization and limited robustness. This approach is predicated on the transformation of the statistical patterns of rolling bearing vibration characteristics during steady-state operation into differentiable and physically feasible domain constraints. A Physics-Inspired Feature Constraint (PIFC) layer is constructed, where a projection operator guides the learning process of the network to conform to actual vibration physics and suppress the generation of non-physical features. This approach effectively decouples and fuses multi-scale time-frequency features by integrating the global dependency modeling capability of self-attention mechanisms for periodic impact patterns with the bidirectional temporal capture capability of Bidirectional Long Short-Term Memory (BiLSTM) for transient events. The experimental findings on the MFPT (Mechanical Faults Prevention Technology) bearing dataset demonstrate that the proposed method achieves 98.70% accuracy and an F1 score exceeding 98%, thereby significantly outperforming existing mainstream methods. This research proposes a novel “data-mechanism” synergistic optimization approach for high-precision, intelligent fault diagnosis in complex industrial environments. The approach demonstrates significant practical value for engineering applications.