School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China.
Sensors (Basel). 2012 Oct 12;12(10):13694-719. doi: 10.3390/s121013694.
Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches.
轴承不仅是最重要的元件，也是旋转机械中常见的失效源。近年来，轴承故障预测技术越来越受到关注，特别是因为它在避免事故发生方面发挥着越来越重要的作用。其中，从原始特征中提取轴承加速度计传感器信号的故障特征（FFE）对于机械故障诊断和预测来说，是突出轴承状况代表性特征的关键。本文提出了一种基于谱回归（SR）的方法，用于从轴承加速度计传感器信号的时间、频率和时频域特征等原始特征中提取故障特征。SR 是一种新颖的回归框架，用于高效正则化子空间学习和特征提取技术，它使用最小二乘法来获得最佳投影方向，而不是计算特征的密度矩阵，因此它在降维方面也具有优势。通过将采集到的振动信号数据应用于轴承，实验验证了基于 SR 的方法的有效性。实验结果表明，SR 可以降低计算成本，并保留更多关于不同轴承故障和严重程度的结构信息，并且证明了所提出的特征提取方案比其他类似方法具有优势。
