Zhang Baobao, Zhang Jianjie, Yu Peibo, Cao Jianhui, Peng Yihang
College of Software, Xinjiang University, Urumqi 830091, China.
College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China.
Sensors (Basel). 2024 Oct 10;24(20):6510. doi: 10.3390/s24206510.
Predicting the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and availability of mechanical systems. Constructing health indicators (HIs) is a fundamental step in the methodology for predicting the RUL of rolling bearings. Traditional HI construction often involves determining the degradation stage of the bearing by extracting time-frequency domain features from raw data using a priori knowledge and setting artificial thresholds; this approach does not fully utilize the vibration information in the bearing data. In order to address the above problems, this paper proposes an Asymmetric Residual Shrinkage Convolutional Autoencoder (ARSCAE) model. The asymmetric structure of the ARSCAE model is characterized by the soft thresholding of signal features in the encoder part to achieve noise reduction. The decoder part consists of convolutional and pooling layers for data reconstruction. This model can directly construct HIs from the original vibration signals collected, and comparisons with other models show that it constructs better HIs from the original vibration signals. Finally, experiments on the FEMTO dataset show that the results indicate that the HIS constructed by the ARSCAE model has better lifetime prediction capability compared to other methods.
预测轴承的剩余使用寿命（RUL）对于维持机械系统的可靠性和可用性至关重要。构建健康指标（HI）是预测滚动轴承RUL方法中的一个基本步骤。传统的HI构建通常涉及利用先验知识从原始数据中提取时频域特征并设置人工阈值来确定轴承的退化阶段；这种方法没有充分利用轴承数据中的振动信息。为了解决上述问题，本文提出了一种非对称残差收缩卷积自动编码器（ARSCAE）模型。ARSCAE模型的非对称结构的特点是在编码器部分对信号特征进行软阈值处理以实现降噪。解码器部分由用于数据重建的卷积层和池化层组成。该模型可以直接从收集到的原始振动信号中构建HI，与其他模型的比较表明，它能从原始振动信号中构建出更好的HI。最后，在FEMTO数据集上的实验结果表明，与其他方法相比，ARSCAE模型构建的HI具有更好的寿命预测能力。
