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.

中文翻译：

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基于综合的基于多类支持向量机的复杂退化系统健康评估

准确的健康评估对于复杂降解系统的可靠运行至关重要。尽管传统的机器学习方法（例如人工神经网络（ANN）和支持向量机（SVM））已被广泛使用，但基于单一分类模型的状态评估方案仍然遭受多类分类效率低，方差和偏差高的困扰。为了解决这些问题，本文提出了一种基于堆叠集成学习和广义多类支持向量机（GMSVM）算法的健康评估方法。提出的健康评估框架包括三个部分：1）对多个传感器数据进行异常值消除和缺失值处理；2）从观测数据中提取统计特征，并将皮尔逊相关系数用于特征选择；3）集成广义多类支持向量机（EGMSVM）被用来评估降解系统的健康状况。与二元分类器和基于深度学习的分类器不同，EGMSVM利用基于堆栈的方法将多个GMSVM作为子模型，将随机森林作为元模型进行组合，并且元模型将子模型的结果组合在一起以获得满意的性能。与传统的基于SVM和ANN的算法相比，EGMSVM在处理多类问题时实现了高效率，同时方差和偏差也很低。使用液压试验台对提出的方法进行了验证。