Deng Feiyue, Liu Chao, Liu Yongqiang, Hao Rujiang
State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China.
School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China.
Sensors (Basel). 2021 Sep 8;21(18):6025. doi: 10.3390/s21186025.
Fault detection of axle bearings is crucial to promote the safe, efficient, and reliable running of high-speed trains. In recent decades, time-frequency analysis (TFA) techniques have been widely used in mechanical equipment fault diagnoses. Time-reassigned multisynchrosqueezing transform (TMSST), as a novel time-frequency representation (TFR) algorithm, is more suitable for dealing with strong frequency-varying signals. However, TMSST TFR results are subject to noise interference. It is difficult to extract the accurate time-frequency (TF) fault feature of the axle bearing under a complex working environment. In addition, determination of the TMSST algorithm parameters depends on the personnel's subjective experience. Therefore, the TMSST result has a great randomicity and has the disadvantage of having a poor reliability. To address the above issues, a hybrid SVD-based denoising and self-adaptive TMSST is proposed for axle bearing fault detection in this paper. The main improvements of the proposed algorithm include the following two aspects: (1) An SVD-based denoising method using the maximum SV mean to determine the reasonable SV order is adopted to eliminate noise interference and to reserve useful fault impulse information. (2) A new evaluation metric, named time-frequency spectrum permutation entropy (TFS-PEn), is put forward for the quantitative evaluation of the performance of TFR for the TMSST, and then a water cycle algorithm (WCA)-based optimized TMSST can adaptively determine the optimal algorithm parameters. In both the simulation and experimental tests, the superiority and effectiveness of the proposed method is compared with the TMSST, short-time Fourier transform (STFT), MSST, wavelet transform (WT), and Hilbert-Huang transform (HHT) methods. The results show that the proposed algorithm has a better performance for extracting the weak fault features of axle bearing under a strong background noise environment.
轴箱轴承的故障检测对于促进高速列车的安全、高效和可靠运行至关重要。近几十年来，时频分析（TFA）技术已广泛应用于机械设备故障诊断。时间重分配多同步挤压变换（TMSST）作为一种新颖的时频表示（TFR）算法，更适合处理强时变信号。然而，TMSST的TFR结果容易受到噪声干扰。在复杂的工作环境下，难以提取轴箱轴承准确的时频（TF）故障特征。此外，TMSST算法参数的确定依赖于人员的主观经验。因此，TMSST结果具有很大的随机性，可靠性较差。为了解决上述问题，本文提出了一种基于奇异值分解（SVD）的混合去噪和自适应TMSST方法用于轴箱轴承故障检测。所提算法的主要改进包括以下两个方面：（1）采用基于SVD的去噪方法，利用最大奇异值均值确定合理的奇异值阶数，以消除噪声干扰并保留有用的故障脉冲信息。（2）提出了一种新的评估指标，即时频谱排列熵（TFS-PEn），用于定量评估TMSST的TFR性能，然后基于水循环算法（WCA）优化的TMSST可以自适应地确定最优算法参数。在仿真和实验测试中，将所提方法与TMSST、短时傅里叶变换（STFT）、多同步挤压变换（MSST）、小波变换（WT）和希尔伯特-黄变换（HHT）方法进行了比较。结果表明，所提算法在强背景噪声环境下提取轴箱轴承微弱故障特征方面具有更好的性能。
