Feature extraction of mechanical fault diagnosis based on MPE-LLTSA
ZHAO Jiangang1, NING Jing1,2, NING Yunzhi1, CHEN Chunjun1, LI Yanping1
1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China;
2. Sichuan Provincial Key Lab of Rail Transit Operation and Maintenance Technology and Equipment, Chengdu 610031, China
Abstract：A large number of signals collected by mechanical equipment monitoring system are usually nonlinear signals with multiple natural oscillation modes, so the single-scale feature extraction can’t characterize these nonlinear signals. For high dimensional feature matrix, its main lower dimensional features need to be further extracted. Here, to solve these two problems, a nonlinear feature extraction method combining multiscale permutation entropy and linear local tangent space alignment (MPE-LLTSA) was proposed. Firstly, signals were calculated using MPE to obtain multi-scale features with high dimensions. Then, LLTSA was used to excavate the embedded intrinsic structure, and realize low dimensional feature extraction. Finally, least squares support vector machine (LSSVM) was introduced to train and recognize low dimensional features. The test results showed that the proposed method has application potential in fields of mechanical pattern classification and fault recognition.
赵建岗1，宁静1,2，宁云志1，陈春俊1，李艳萍1. 基于多尺度排列熵和线性局部切空间排列的机械故障诊断特征提取[J]. 振动与冲击, 2021, 40(13): 136-145.
ZHAO Jiangang1, NING Jing1,2, NING Yunzhi1, CHEN Chunjun1, LI Yanping1. Feature extraction of mechanical fault diagnosis based on MPE-LLTSA. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(13): 136-145.
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