Cheng Yao, Wang Zhiwei, Zhang Weihua, Huang Guanhua
State key laboratory of Traction power, Southwest Jiaotong University, Chengdu 610031, People's Republic of China.
Beijing Haidongqing Electrical and Mechanical Equipment Co., Ltd, Beijing 101300, People's Republic of China.
ISA Trans. 2019 Jul;90:244-267. doi: 10.1016/j.isatra.2019.01.012. Epub 2019 Jan 16.
Extraction of the fault related impulses from the raw vibration signal is important for rolling element bearing fault diagnosis. Deconvolution techniques, such as minimum entropy deconvolution (MED), MED adjusted (MEDA) and maximum correlated kurtosis deconvolution (MCKD), optimal MED adjusted (OMEDA) and multipoint optimal MED adjusted (MOMEDA), are typical techniques for enhancing the impulse-like component in the fault signal. This paper introduces the particle swarm optimization (PSO) algorithm to solve the filter of deconvolution problem. The proposed approaches solve the filter coefficients of the deconvolution problems by the PSO algorithm, assisted by a generalized spherical coordinate transformation. Compared with MED, MEDA, and OMEDA, the proposed PSO-MED and PSO-OMEDA can effectively overcome the influence of large random impulses and tend to deconvolve a series of periodic impulses rather than a signal impulse. Compared with MCKD and MOMEDA, the proposed PSO-MCKD and PSO-MOMEDA can achieve good performances even when the fault period is inaccurate. The effectiveness of the proposed methods is validated by the simulated signals. The study of experimental bearing fault signal shows that the PSO based deconvolution methods delivered better performance for rolling element bearing fault detection than the traditional deconvolution methods. Additionally, the proposed methods are compared with the following two popular signal processing methods: the ensemble empirical mode decomposition (EEMD) and fast kurtogram, which are used to highlight the improved performance of the proposed methods.
从原始振动信号中提取与故障相关的脉冲对于滚动轴承故障诊断至关重要。反卷积技术，如最小熵反卷积（MED）、调整后的最小熵反卷积（MEDA）、最大相关峭度反卷积（MCKD）、最优调整后的最小熵反卷积（OMEDA）和多点最优调整后的最小熵反卷积（MOMEDA），是增强故障信号中类似脉冲成分的典型技术。本文引入粒子群优化（PSO）算法来解决反卷积问题的滤波器。所提出的方法通过PSO算法求解反卷积问题的滤波器系数，并辅以广义球坐标变换。与MED、MEDA和OMEDA相比，所提出的PSO - MED和PSO - OMEDA能够有效克服大随机脉冲的影响，倾向于对一系列周期性脉冲进行反卷积，而不是对单个信号脉冲进行反卷积。与MCKD和MOMEDA相比，所提出的PSO - MCKD和PSO - MOMEDA即使在故障周期不准确时也能取得良好的性能。通过模拟信号验证了所提方法的有效性。对实验轴承故障信号的研究表明，基于PSO的反卷积方法在滚动轴承故障检测方面比传统反卷积方法具有更好的性能。此外，将所提方法与以下两种流行的信号处理方法进行了比较：总体经验模态分解（EEMD）和快速峭度图，以突出所提方法的改进性能。
