Dynamic Minimax Probability Machine-Based Approach for Fault Diagnosis Using Pairwise Discriminate Analysis
Dynamic Minimax Probability Machine-Based Approach for Fault Diagnosis Using Pairwise Discriminate Analysis
复制标题
使用成对判别分析进行故障诊断的动态最小最大概率机器方法
DOI：
10.1109/tcst.2017.2771732
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发表时间：
2019-03
影响因子：
4.8
通讯作者：
Huang Gao
中科院分区：
文献类型：
--
作者：
Jiang Benben;Guo Zhifeng;Zhu Qunxiong;Huang Gao
故障诊断在工业过程的安全高效运行中起着关键作用。随着大数据时代的兴起，基于概率表示的分析方法引起了越来越多的研究兴趣。在这篇简报中，一个动态的……（原文“a dynamic mi”不完整）
Fault diagnosis plays a key role in the safe and efficient operation of industrial processes. With the emerging big data era, the analytic methods based on probabilistic representations have attracted growing research interest. In this brief, a dynamic minimax probability machine (DMPM) approach based on the framework of probabilistic representations is proposed for diagnosing process faults, without imposing any assumptions on data distributions. In addition, an information criterion is put forward to determine the optimal dimensionality reduction order and time lags of DMPM. The proposed DMPM-based method allows for the enhanced performance of fault diagnosis due to the following advantages over conventional diagnostic approaches. First, DMPM maximizes the pairwise separation probability between each pair of faulty data sets, directly yielding improved discriminatory power in the projected space. Second, the proposed approach is less likely to be influenced by “outlier” classes since its objective function is a summation of probabilities, thereby enabling it to be beneficial for the classification of imbalanced data. Third, DMPM has superior capability on capturing dynamic information from the process data by augmenting observation vectors with time lags. The effectiveness of the proposed approach is demonstrated on the Tennessee Eastman process.
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