期刊
IEEE-ASME TRANSACTIONS ON MECHATRONICS
卷 25, 期 5, 页码 2230-2240出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMECH.2020.3009449
关键词
Support vector machines; Feature extraction; Degradation; Kernel; IEEE transactions; Mechatronics; Training; Degradation system; generalized multiclass support vector machine (GMSVM); health evaluation; signal processing; stacking-based ensemble learning
类别
资金
- National Key R&D Program of China [2018YFB1702300]
- National Natural Science Foundation of China [5187522]
- Key-Area Research and Development Program of Guangdong Province [2019B090916001]
- Key R&D Program of Henan Province [202102210301]
Accurate health evaluation is crucial to reliable operation of complex degradation systems. Although traditional machine learning methods such as artificial neural network (ANN) and support vector machine (SVM) have been used widely, state assessment schemes based on a single classification model still suffer from low multiclass classification efficiency, high variance, and deviation. To solve these problems, this article proposes a novel health evaluation method based on stacking ensemble learning and generalized multiclass support vector machine (GMSVM) algorithm. The proposed health evaluation framework includes three parts: 1) abnormal value elimination and missing value processing are applied for multiple sensor data; 2) statistical features are extracted from the observed data and the Pearson correlation coefficient is applied for feature selection; and 3) ensemble generalized multiclass support vector machines (EGMSVMs) are utilized to evaluate the health situation of a degradation system. Unlike the binary classifiers and deep-learning-based classifiers, EGMSVMs utilize the stacking-based method to combine several GMSVMs as submodels and random forest as a metamodel, and the metamodel ensembles the results of submodels to reach a satisfied performance. Compared to traditional SVM- and ANN-based algorithms, EGMSVMs, in processing multiclass problems, achieve high efficiency and, meanwhile, low variance and deviation. The proposed method is verified using a hydraulic test rig. The experimental results show the feasibility and applicability of the proposed health evaluation framework.
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