Hao Yansong, Song Liuyang, Ke Yanliang, Wang Huaqing, Chen Peng
College of Mechanical & Electrical Engineering, Beijing University of Chemical Technology, Chao Yang District, Beijing 100029, China.
Graduate School of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu, Mie 514-8507, Japan.
Sensors (Basel). 2017 Jun 6;17(6):1307. doi: 10.3390/s17061307.
Compound faults often occur in rotating machinery, which increases the difficulty of fault diagnosis. In this case, blind source separation, which usually includes independent component analysis (ICA) and sparse component analysis (SCA), was proposed to separate mixed signals. SCA, which is based on the sparsity of target signals, was developed to sever the compound faults and effectively diagnose the fault due to its advantage over ICA in underdetermined conditions. However, there is an issue regarding the vibration signals, which are inadequately sparse, and it is difficult to represent them in a sparse way. Accordingly, to overcome the above-mentioned problem, a sparsity-promoted approach named wavelet modulus maxima is applied to obtain the sparse observation signal. Then, the potential function is utilized to estimate the number of source signals and the mixed matrix based on the sparse signal. Finally, the separation of the source signals can be achieved according to the shortest path method. To validate the effectiveness of the proposed method, the simulated signals and vibration signals measured from faulty roller bearings are used. The faults that occur in a roller bearing are the outer-race flaw, the inner-race flaw and the rolling element flaw. The results show that the fault features acquired using the proposed approach are evidently close to the theoretical values. For instance, the inner-race feature frequency 101.3 Hz is very similar to the theoretical calculation 101 Hz. Therefore, it is effective to achieve the separation of compound faults utilizing the suggest method, even in underdetermined cases. In addition, a comparison is applied to prove that the proposed method outperforms the traditional SCA method when the vibration signals are inadequate.
复合故障经常出现在旋转机械中，这增加了故障诊断的难度。在这种情况下，人们提出了盲源分离方法，该方法通常包括独立成分分析（ICA）和稀疏成分分析（SCA），用于分离混合信号。基于目标信号稀疏性的SCA由于在欠定条件下比ICA具有优势，被开发用于处理复合故障并有效诊断故障。然而，存在一个关于振动信号的问题，即这些信号的稀疏性不足，难以用稀疏方式表示。因此，为了克服上述问题，一种名为小波模极大值的稀疏增强方法被应用于获取稀疏观测信号。然后，利用势函数基于稀疏信号估计源信号的数量和混合矩阵。最后，可以根据最短路径法实现源信号的分离。为了验证所提方法的有效性，使用了模拟信号和从故障滚动轴承测量得到的振动信号。滚动轴承中出现的故障有外圈缺陷、内圈缺陷和滚动体缺陷。结果表明，使用所提方法获取的故障特征明显接近理论值。例如，内圈特征频率101.3Hz与理论计算值101Hz非常相似。因此，即使在欠定情况下，利用所提方法实现复合故障的分离也是有效的。此外，通过对比证明，当振动信号稀疏性不足时，所提方法优于传统的SCA方法。
