An Imbalanced Multifault Diagnosis Method Based on Bias Weights AdaBoost

Xue Jiang, Yuan Xu, Wei Ke, Yang Zhang, Qun Xiong Zhu, Yan Lin He

研究成果: Article同行評審

31 引文 斯高帕斯(Scopus)


Fault diagnosis plays an important role in ensuring process safety. It is noted that imbalance between fault data and normal data always exists, and multifault obviously outranges a single fault in common, which leads to more challenges to fault diagnosis. In this article, an imbalanced multifault diagnosis method based on bias weights AdaBoost (BW-AdaBoost) is proposed. First, majority normal samples are under-sampled by K-nearest neighbor (KNN) to collect the boundary samples between majority normal samples and minority fault samples, and then the bias datasets are formed by under-sampled majority samples and minority samples. Second, different weak classifiers with adaptive weights are constructed based on the bias datasets and are integrated into a strong classifier, which is taken as the base classifier. While constructing the weak classifier, higher weights are given to the items corresponding to the minority class in the loss function to enhance the influence of minority class samples. Third, to solve the multifault problem, the base classifiers are integrated into a multiclassification model by the hierarchical structure which needs fewer classifiers and less computational expense. Finally, through simulation experiment, the comparison results show that the proposed imbalanced multifault diagnosis method based on BW-AdaBoost can effectively improve the diagnosis accuracy and F1 score.


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