Abstract: Because of uncertainty factors in the burning process of solid waste，it is impossible to execute fault diagnosis by using first principle models. Principal Component Analysis (PCA) was introduced in this paper for incipient fault diagnosis of a waste solid incinerator (WSI). Based on historical data，a PCA model was built to represent its normal states. Fault detection was then realized based on two statistical variables T2 and SPE. Residual Subspace Contribution Factor graphs were then utilized to assist the fault diagnosis. The research result indicated that PCA was an effective approach to incipient fault detection and diagnosis of WSIs.

摘要： 介绍了主元分析法(PCA)及其在垃圾焚烧炉故障诊断中的应用。通过分析历史数据获取主元模型，对运行数据进行在线分析，采用T2和SPE统计法对控制系统进行故障检测，并用故障贡献图对故障进行诊断。研究结果表明，PCA能有效地进行固体垃圾焚烧炉早期故障检测及诊断。