Luo Songrong, Yang Wenxian, Luo Youxin
Hunan Provincial Cooperative Innovation Center for the Construction & Development of Dongting Lake Ecological Economic Zone, Changde 415000, China.
College of Mechanical Engineering, Hunan University of Arts and Science, Changde 415000, China.
Entropy (Basel). 2020 Mar 25;22(4):375. doi: 10.3390/e22040375.
Condition monitoring and fault diagnosis of a rolling bearing is crucial to ensure the reliability and safety of a mechanical system. When local faults happen in a rolling bearing, the complexity of intrinsic oscillations of the vibration signals will change. Refined composite multiscale dispersion entropy (RCMDE) can quantify the complexity of time series quickly and effectively. To measure the complexity of intrinsic oscillations at different time scales, adaptive sparest narrow-band decomposition (ASNBD), as an improved adaptive sparest time frequency analysis (ASTFA), is introduced in this paper. Integrated, the ASNBD and RCMDE, a novel-fault diagnosis-model is proposed for a rolling bearing. Firstly, a vibration signal collected is decomposed into a number of intrinsic narrow-band components (INBCs) by the ASNBD to present the intrinsic modes of a vibration signal, and several relevant INBCs are prepared for feature extraction. Secondly, the RCMDE values are calculated as nonlinear measures to reveal the hidden fault-sensitive information. Thirdly, a basic Multi-Class Support Vector Machine (multiSVM) serves as a classifier to automatically identify the fault type and fault location. Finally, experimental analysis and comparison are made to verify the effectiveness and superiority of the proposed model. The results show that the RCMDE value lead to a larger difference between various states and the proposed model can achieve reliable and accurate fault diagnosis for a rolling bearing.
滚动轴承的状态监测与故障诊断对于确保机械系统的可靠性和安全性至关重要。当滚动轴承出现局部故障时，振动信号固有振荡的复杂性会发生变化。精细复合多尺度分散熵（RCMDE）能够快速有效地量化时间序列的复杂性。为了测量不同时间尺度下固有振荡的复杂性，本文引入了自适应稀疏窄带分解（ASNBD），它是一种改进的自适应稀疏时频分析（ASTFA）。将ASNBD和RCMDE相结合，提出了一种用于滚动轴承的新型故障诊断模型。首先，通过ASNBD将采集到的振动信号分解为多个固有窄带分量（INBC），以呈现振动信号的固有模式，并准备几个相关的INBC进行特征提取。其次，计算RCMDE值作为非线性度量，以揭示隐藏的故障敏感信息。第三，使用基本的多类支持向量机（multiSVM）作为分类器来自动识别故障类型和故障位置。最后，通过实验分析和比较来验证所提模型的有效性和优越性。结果表明，RCMDE值在不同状态之间导致更大的差异，并且所提模型能够对滚动轴承实现可靠且准确的故障诊断。
