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| Monitoring and fault diagnosis based on PCA for multivariable control system |
| CHEN Yong, LIANG Jun, LU Hao |
| Institute of System Engineering，Zhejiang University，Hangzhou 310027，China |
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Abstract Principal component analysis(PCA)is an effective method for process monitoring and fault diagnosis．PCA produces a compressed statistical model that gives linear combinations of the original variables that describe the major trends in a data set，and produces new variables that are uncorrelated with each other and are linear combinations of the original variables．The experiments results show that PCA is an efficient method to monitor performance of the process，and can detect faults resulted in change of product quality exactly．
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Published: 28 December 2002
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基于PCA的多变量控制系统的故障监测与诊断
主元分析(PCA)是一种能够对过程生产进行监测和质量控制的有效方法，在保证数据信息丢失最少的情况下，大大降低了原始数据空间的维数．利用PCA分析建模可以消除变量间的非线性关联，降低噪声影响．通过对某食品厂蒸煮设备控制流程进行大量试验表明，PCA 故障诊断模型能够有效地对设备生产进行监测，并能较准确及时地诊断设备运行中发生的故障．
关键词：
主元分析,
多元统计分析,
故障诊断,
过程监测
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