| 复杂生产过程的小故障检测与分类方法 |
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| 引用本文： | 张敏,程文明,刘娟. 复杂生产过程的小故障检测与分类方法[J]. 西南交通大学学报, 2014, 27(5): 842-847. DOI: 10.3969/j.issn.0258-2724.2014.05.016 |
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| 作者姓名： | 张敏 程文明 刘娟 |
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| 作者单位： | 1. 西南交通大学机械工程学院,四川成都,610031
2. 西南石油大学电气信息学院,四川成都,610500 |
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| 基金项目： | 中央高校基本科研业务费专项资金资助项目(2682014BR022)；国家自然科学基金资助项目(51205328，51175442)；教育部人文社会科学研究青年基金资助项目 |
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| 摘 要： | 为监测复杂生产过程的状态，根据多元统计过程控制方法和支持向量机理论，将累积和控制图原理扩展为多变量的形式对过程数据进行预处理，并通过主元分析方法提取复杂生产过程的关键信息，得到有效的小故障数据，进而构建计算正常数据的统计量阀值及故障数据的Hotelling T平方统计值（T2）和平方预测误差值，实现了复杂生产过程的小故障模式检测，并采用支持向量机多分类方法将检测到的故障进行了分类.沥青混合料生产过程的仿真研究表明:在集料均值发生小波动、周期性上升和下降3种小故障模式下，故障检测识别率均达到95%，与主元分析方法相比平均提高了75%；分类准确率达到92.5%，与BP神经网络方法相比提高了19.3%.
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| 关 键 词： | 故障检测与分类 主元分析 支持向量机 多变量累积和 |
| 收稿时间： | 2013-09-20 |
Small Fault Detection and Classification Method for Complex Production Process |
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| ZHANG Min,CHENG Wenming,LIU Juan. Small Fault Detection and Classification Method for Complex Production Process[J]. Journal of Southwest Jiaotong University, 2014, 27(5): 842-847. DOI: 10.3969/j.issn.0258-2724.2014.05.016 |
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| Authors: | ZHANG Min CHENG Wenming LIU Juan |
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| Abstract: | In order to monitor the condition of a complex production process, based on the multivariate statistical process control and support vector machine theory, the cumulative control chart principle was extended to multivariate form for data preprocessing, and the principal component analysis (PCA) was utilized to extract significant information from the complex production process. The effective data of small fault was obtained. Then, the statistical threshold values of normal data, statistic T2, and squared prediction error (SPE) of corresponding fault data were constructed to realize the small fault detection of the complex production process, and the support vector machine (SVM) multi-classification method was used to classify the detected fault modes. The simulation results of a hot mix asphalt production process show that in cases of small fluctuation, cyclical rise and fall respectively, the small fault detection recognition rate is about 95%, with an average improvement of 75% over the PCA method; the classification accuracy rate reaches 92.5%, improved 19.3% compared with the BP neural networks method. |
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| Keywords: | small fault detection and classification principal component analysis support vector machine multivariate cumulative sum |
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