Gear pitting fault is a common issue in gear systems,affecting transmission efficiency and potentially leading to severe equipment shutdowns.Effective diagnosis enhances reliability,reduces maintenance costs,and exten...Gear pitting fault is a common issue in gear systems,affecting transmission efficiency and potentially leading to severe equipment shutdowns.Effective diagnosis enhances reliability,reduces maintenance costs,and extends equipment lifespan.However,existing deep learning based methods often neglect the inherent structure of temporal vibration signals and fail to address domain variations,resulting in poor generalization and performance.To overcome these limitations,we propose a novel approach based on domain-independent features.Vibration signals are mapped to time-frequency representations via short-time Fourier transform,and dependencies between different frequencies are effectively captured using a Transformer encoder.The proposed method incorporates a feature decoupling structure that combines singular value decomposition and Pearson correlation coefficient to extract low-rank approximations of domain-related and pitting-related features,while quantifying their correlation.This approach mitigates feature degradation in constructing domain-independent features.Additionally,the weighted LinSoftmax function is introduced as a replacement for the traditional Softmax,leading to a more stable optimization target and improved model accuracy,with a distance-based penalty weight focusing on significant prediction errors.Experiments on the 2023 PHM Data Challenge dataset demonstrate the effectiveness of the proposed method,achieving a mean absolute error of 0.11,an accuracy of 92.32%,and a fault tolerance accuracy of 98.02%.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62373360 and 62473368).
文摘Gear pitting fault is a common issue in gear systems,affecting transmission efficiency and potentially leading to severe equipment shutdowns.Effective diagnosis enhances reliability,reduces maintenance costs,and extends equipment lifespan.However,existing deep learning based methods often neglect the inherent structure of temporal vibration signals and fail to address domain variations,resulting in poor generalization and performance.To overcome these limitations,we propose a novel approach based on domain-independent features.Vibration signals are mapped to time-frequency representations via short-time Fourier transform,and dependencies between different frequencies are effectively captured using a Transformer encoder.The proposed method incorporates a feature decoupling structure that combines singular value decomposition and Pearson correlation coefficient to extract low-rank approximations of domain-related and pitting-related features,while quantifying their correlation.This approach mitigates feature degradation in constructing domain-independent features.Additionally,the weighted LinSoftmax function is introduced as a replacement for the traditional Softmax,leading to a more stable optimization target and improved model accuracy,with a distance-based penalty weight focusing on significant prediction errors.Experiments on the 2023 PHM Data Challenge dataset demonstrate the effectiveness of the proposed method,achieving a mean absolute error of 0.11,an accuracy of 92.32%,and a fault tolerance accuracy of 98.02%.
