Dong Zhilin, Zheng Jinde, Huang Siqi, Pan Haiyang, Liu Qingyun
School of Mechanical Engineering, Anhui University of Technology, Ma'anshan 243032, China.
Anhui Key Laboratory of Mine Intelligent Equipment and Technology, Anhui University of Science & Technology, Huainan 232001, China.
Entropy (Basel). 2019 Jun 25;21(6):621. doi: 10.3390/e21060621.
Multi-scale permutation entropy (MPE) is an effective nonlinear dynamic approach for complexity measurement of time series and it has been widely applied to fault feature representation of rolling bearing. However, the coarse-grained time series in MPE becomes shorter and shorter with the increase of the scale factor, which causes an imprecise estimation of permutation entropy. In addition, the different amplitudes of the same patterns are not considered by the permutation entropy used in MPE. To solve these issues, the time-shift multi-scale weighted permutation entropy (TSMWPE) approach is proposed in this paper. The inadequate process of coarse-grained time series in MPE was optimized by using a time shift time series and the process of probability calculation that cannot fully consider the symbol mode is solved by introducing a weighting operation. The parameter selections of TSMWPE were studied by analyzing two different noise signals. The stability and robustness were also studied by comparing TSMWPE with TSMPE and MPE. Based on the advantages of TSMWPE, an intelligent fault diagnosis method for rolling bearing is proposed by combining it with gray wolf optimized support vector machine for fault classification. The proposed fault diagnostic method was applied to two cases of experimental data analysis of rolling bearing and the results show that it can diagnose the fault category and severity of rolling bearing accurately and the corresponding recognition rate is higher than the rate provided by the existing comparison methods.
多尺度排列熵（MPE）是一种用于时间序列复杂度测量的有效非线性动力学方法，已广泛应用于滚动轴承的故障特征表征。然而，随着尺度因子的增加，MPE中的粗粒化时间序列变得越来越短，这导致排列熵的估计不准确。此外，MPE中使用的排列熵没有考虑相同模式的不同幅度。为了解决这些问题，本文提出了时移多尺度加权排列熵（TSMWPE）方法。通过使用时移时间序列优化了MPE中粗粒化时间序列的不足过程，并通过引入加权运算解决了概率计算过程中不能充分考虑符号模式的问题。通过分析两种不同的噪声信号研究了TSMWPE的参数选择。还通过将TSMWPE与TSMPE和MPE进行比较研究了其稳定性和鲁棒性。基于TSMWPE的优点，将其与灰狼优化的支持向量机相结合用于故障分类，提出了一种滚动轴承智能故障诊断方法。将所提出的故障诊断方法应用于滚动轴承的两例实验数据分析，结果表明该方法能够准确诊断滚动轴承的故障类别和严重程度，相应的识别率高于现有比较方法。
