Wang Cong, Liu Chang, Liao Mengliang, Yang Qi
School of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650093, China.
Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Province, Kunming University of Science and Technology, Kunming 650093, China.
Math Biosci Eng. 2021 Feb 4;18(2):1670-1688. doi: 10.3934/mbe.2021086.
Aiming at the problems of data transmission, storage, and processing difficulties in the fault diagnosis of bearing acoustic emission (AE) signals, this paper proposes a weak fault feature enhancement diagnosis method for processing bearing AE signals in the compressed domain based on the theory of compressed sensing (CS). This method is based on the frequency band selection scheme of CS and particle swarm optimization (PSO) method. Firstly, the method uses CS technology to compress and sample the bearing AE signal to obtain the compressed signal; then, the compressed AE signals are decomposed by the compression domain wavelet packet decomposition matrix to extract the characteristic parameters of different frequency bands, and then the weighted sum of the characteristic parameters is carried out. At the same time, the PSO method is used to optimize the weight coefficient to obtain the enhanced fault characteristics; finally, a feature-enhanced-support vector machine (SVM) fault diagnosis model is established. Different feature parameters are feature-enhanced to form a feature set, which is used as input, and the SVM method is used for pattern recognition of different types and degrees of bearing faults. The experimental results show that the proposed method can effectively extract the fault features in the bearing AE signal while improving the efficiency of signal processing and analysis and realize the accurate classification of bearing faults.
针对轴承声发射（AE）信号故障诊断中数据传输、存储及处理困难的问题，本文基于压缩感知（CS）理论，提出一种在压缩域处理轴承AE信号的微弱故障特征增强诊断方法。该方法基于CS的频带选择方案和粒子群优化（PSO）方法。首先，利用CS技术对轴承AE信号进行压缩采样以获得压缩信号；然后，通过压缩域小波包分解矩阵对压缩后的AE信号进行分解，提取不同频带的特征参数，接着对特征参数进行加权求和。同时，采用PSO方法优化权重系数以获得增强的故障特征；最后，建立特征增强支持向量机（SVM）故障诊断模型。对不同特征参数进行特征增强以形成特征集，将其作为输入，采用SVM方法对不同类型和程度的轴承故障进行模式识别。实验结果表明，所提方法能有效提取轴承AE信号中的故障特征，同时提高信号处理与分析效率，实现轴承故障的准确分类。
