Health status assessment-based remaining useful life prediction of bearings: an improved temporal convolutional network approach

Abstract Bearings are critical components of mechanical equipment, and predicting their remaining useful life (RUL) is important in industry. This paper proposes a RUL prediction method based on the assessment of a bearing’s health status. Features from the time, frequency, and time–frequency domains of the bearing’s vibration signal are extracted to construct a feature set. A multibranch encoder and restricted Boltzmann machine are used to improve the stacked autoencoder to reduce dimensionality. Local weights and health-sample means are introduced into the Mahalanobis distance to improve the health index. Subsequently, the weighted convolutional Euclidean distance serves as the distance metric in K -means clustering to achieve a more accurate health status assessment and provide historical data for RUL prediction. An improved self-attention (ISA) mechanism is proposed by incorporating depthwise separable convolutions and residual-like connections into self-attention mechanisms, enhancing the global and local dependencies of the temporal convolutional network (TCN). Thus, a more accurate RUL prediction is achieved. Comparative and ablation experiments confirm that the proposed ISA-TCN achieves superior predictive accuracy. Generalization experiments further demonstrate its strong adaptability, while anti-noise experiments demonstrate its strong robustness to uncertainties. Finally, experiments on multi-output RUL predictions validate the model’s effectiveness. This approach offers valuable insights for RUL prediction of rotating machinery under real-world scenarios involving variable operating conditions, noise interference, and multi-device environments.