Tian Jing, Liu Lili, Zhang Fengling, Ai Yanting, Wang Rui, Fei Chengwei
Liaoning Key Laboratory of Advanced Test Technology for Aeronautical Propulsion System, Shenyang Aerospace University, Shenyang 110136, China.
Department of Power and Energy, Northwestern Polytechnical University, Xi'an 710129, China.
Entropy (Basel). 2019 Dec 31;22(1):57. doi: 10.3390/e22010057.
Inter-shaft bearing as a key component of turbomachinery is a major source of catastrophic accidents. Due to the requirement of high sampling frequency and high sensitivity to impact signals, AE (Acoustic Emission) signals are widely applied to monitor and diagnose inter-shaft bearing faults. With respect to the nonstationary and nonlinear of inter-shaft bearing AE signals, this paper presents a novel fault diagnosis method of inter-shaft bearing called the multi-domain entropy-random forest (MDERF) method by fusing multi-domain entropy and random forest. Firstly, the simulation test of inter-shaft bearing faults is conducted to simulate the typical fault modes of inter-shaft bearing and collect the data of AE signals. Secondly, multi-domain entropy is proposed as a feature extraction approach to extract the four entropies of AE signal. Finally, the samples in the built set are divided into two subsets to train and establish the random forest model of bearing fault diagnosis, respectively. The effectiveness and generalization ability of the developed model are verified based on the other experimental data. The proposed fault diagnosis method is validated to hold good generalization ability and high diagnostic accuracy (~0.9375) without over-fitting phenomenon in the fault diagnosis of bearing shaft.
轴间轴承作为透平机械的关键部件，是灾难性事故的主要来源。由于对冲击信号有高采样频率和高灵敏度的要求，声发射（AE）信号被广泛应用于监测和诊断轴间轴承故障。针对轴间轴承AE信号的非平稳性和非线性，本文提出了一种融合多域熵和随机森林的轴间轴承故障诊断新方法——多域熵-随机森林（MDERF）方法。首先，进行轴间轴承故障的模拟试验，模拟轴间轴承的典型故障模式并采集AE信号数据。其次，提出多域熵作为特征提取方法，提取AE信号的四个熵。最后，将构建集中的样本分为两个子集，分别用于训练和建立轴承故障诊断的随机森林模型。基于其他实验数据验证了所开发模型的有效性和泛化能力。所提出的故障诊断方法在轴承轴故障诊断中具有良好的泛化能力和较高的诊断准确率（约0.9375），且无过拟合现象。
