Liu Yu, Liu Yuanbo, Yang Yan
College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China.
Entropy (Basel). 2023 Sep 9;25(9):1316. doi: 10.3390/e25091316.
Contact fatigue is one of the most common failure forms of typical basic components such as bearings and gears. Accurate prediction of contact fatigue performance degradation trends in components is conducive to the scientific formulation of maintenance strategies and health management of equipment, which is of great significance for industrial production. In this paper, to realize the performance degradation trend prediction accurately, a prediction method based on multi-domain features and temporal convolutional networks (TCNs) was proposed. Firstly, a multi-domain and high-dimensional feature set of vibration signals was constructed, and performance degradation indexes with good sensitivity and strong trends were initially screened using comprehensive evaluation indexes. Secondly, the kernel principal component analysis (KPCA) method was used to eliminate redundant information among multi-domain features and construct health indexes (HIs) based on a convolutional autoencoder (CAE) network. Then, the performance degradation trend prediction model based on TCN was constructed, and the degradation trend prediction for the monitored object was realized using direct multi-step prediction. On this basis, the effectiveness of the proposed method was verified using a bearing common-use data set, and it was successfully applied to performance degradation trend prediction for rolling contact fatigue specimens. The results show that using KPCA can reduce the feature set from 14 dimensions to 4 dimensions and retain 98.33% of the information in the original preferred feature set. The method of constructing the HI based on CAE is effective, and change processes versus time of the constructed HI can truly reflect the degradation process of rolling contact fatigue specimen performance; this method has obvious advantages over the two commonly used methods for constructing HIs including auto-encoding (AE) networks and gaussian mixture models (GMMs). The model based on TCN can accurately predict the performance degradation of rolling contact fatigue specimens. Compared with prediction models based on long short-term memory (LSTM) networks and gating recurrent units (GRUs), the model based on TCN has better performance and higher prediction accuracy. The RMS error and average absolute error for a prediction step of 3 are 0.0146 and 0.0105, respectively. Overall, the proposed method has universal significance and can be applied to predict the performance degradation trend of other mechanical equipment/parts.
接触疲劳是轴承和齿轮等典型基础部件最常见的失效形式之一。准确预测部件的接触疲劳性能退化趋势，有利于科学制定设备的维护策略和健康管理方案，对工业生产具有重要意义。本文为实现性能退化趋势的准确预测，提出了一种基于多域特征和时间卷积网络（TCN）的预测方法。首先，构建了振动信号的多域高维特征集，并利用综合评价指标初步筛选出灵敏度高、趋势性强的性能退化指标。其次，采用核主成分分析（KPCA）方法消除多域特征间的冗余信息，并基于卷积自编码器（CAE）网络构建健康指标（HI）。然后，构建基于TCN的性能退化趋势预测模型，通过直接多步预测实现对监测对象的退化趋势预测。在此基础上，利用轴承常用数据集验证了所提方法的有效性，并成功应用于滚动接触疲劳试样的性能退化趋势预测。结果表明，采用KPCA可将特征集从14维降至4维，保留原始优选特征集中98.33%的信息。基于CAE构建HI的方法有效，所构建HI随时间的变化过程能真实反映滚动接触疲劳试样性能的退化过程；该方法相对于自动编码（AE）网络和高斯混合模型（GMM）这两种常用的HI构建方法具有明显优势。基于TCN的模型能够准确预测滚动接触疲劳试样的性能退化。与基于长短期记忆（LSTM）网络和门控循环单元（GRU）的预测模型相比，基于TCN的模型具有更好的性能和更高的预测精度。预测步长为3时的均方根误差和平均绝对误差分别为0.0146和0.0105。总体而言，所提方法具有普遍意义，可应用于预测其他机械设备/部件的性能退化趋势。
