Wang Junfeng, Zheng Jinde, Pan Haiyang, Tong Jinyu, Liu Qingyun
School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China.
School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China.
ISA Trans. 2024 Sep;152:371-384. doi: 10.1016/j.isatra.2024.07.008. Epub 2024 Jul 4.
Rolling bearing is the key component of rotating machinery, and its vibration signal usually exhibits nonlinear and nonstationary characteristics when failure occurs. Multiscale permutation entropy (MPE) is an effective nonlinear dynamics analysis tool, which has been successfully applied to rolling bearing fault diagnosis in recent years. However, MPE ignores the deep amplitude information when measuring the complexity of the time series and the original multiscale coarse-graining is insufficient, which requires further research and improvement. In order to protect the integrity of information structure, a novel nonlinear dynamic analysis method termed refined composite multiscale slope entropy (RCMSlE) is proposed in this paper, which introduced the concept of refined composite to further boost the performance of MPE in nonlinear dynamical complexity analysis. Furthermore, RCMSlE utilizes a novel symbolic representation that takes full account of mode and amplitude information, which overcomes the weaknesses in describing the complexity and regularity of bearing signals. Based on this, a GWO-SVM multi-classifier is introduced to fulfill mode recognition, and then a new intelligent fault diagnosis method for rolling bearing based on RCMSlE and GWO-SVM is proposed. The experimental results show that the proposed method can not only accurately identify different fault types and degrees of rolling bearing, but also has a short computation time and better performance than other comparative methods.
滚动轴承是旋转机械的关键部件，其在发生故障时振动信号通常呈现非线性和非平稳特性。多尺度排列熵（MPE）是一种有效的非线性动力学分析工具，近年来已成功应用于滚动轴承故障诊断。然而，MPE在衡量时间序列复杂度时忽略了深层幅值信息，且原始多尺度粗粒化不足，这有待进一步研究和改进。为保护信息结构的完整性，本文提出一种新型非线性动力学分析方法——精细复合多尺度斜率熵（RCMSlE），该方法引入精细复合概念以进一步提升MPE在非线性动力学复杂度分析中的性能。此外，RCMSlE采用一种充分考虑模态和幅值信息的新型符号表示，克服了在描述轴承信号复杂度和规律性方面的不足。基于此，引入灰狼优化支持向量机（GWO - SVM）多分类器进行模式识别，进而提出一种基于RCMSlE和GWO - SVM的滚动轴承智能故障诊断新方法。实验结果表明，该方法不仅能准确识别滚动轴承的不同故障类型和程度，而且计算时间短，性能优于其他对比方法。
