基于多子空间加权移动窗主成分分析的全厂流程早期故障检测

Multiple subspace weighted moving window PCA for plant-wide process incipient fault detection

早期故障的特征不明显,在全厂流程中比常规故障难检测.为了提高全厂流程中早期故障的检测率和灵敏度,将检测视角由全局转移至局部,提出基于多子空间加权移动窗主成分分析(PCA)的早期故障检测方法.使用结合过程知识和数据驱动的双层子空间划分方法,将过程变量划分到不同子空间中.使用加权的移动窗口增大早期故障的偏移量,将局部离群因子(LOF)算法引入PCA,以便进一步关注数据的局部特征,在每个子空间中建立故障检测模型.通过贝叶斯推理融合法对各子空间的监测结果进行信息融合,获得分布式监测结果.通过工业实例验证所提方法的性能.结果表明,所提方法在全厂流程中有效提升了早期故障检测的准确率和灵敏度.

Incipient faults are difficult to detect in plant-wide processes compared to conventional faults due to the lack of distinctive features.An incipient fault detection method based on multiple subspace weighted moving window principal component analysis(PCA)was proposed by shifting the detection perspective from the global to the local to improve the detection rate and sensitivity of incipient faults in plant-wide processes.Process variables were partitioned into different subspaces using a two-layer subspace partitioning method that combines process knowledge and data-driven approaches.A weighted moving window was used to increase the offset of incipient faults,while a local outlier factor(LOF)algorithm was introduced into PCA to further focus on the local features of the data to model fault detection in each subspace.The monitoring results in each subspace were fused with information by the Bayesian inference fusion method to obtain distributed monitoring results.The proposed method was validated by industrial examples,and the results showed that the method effectively improved the accuracy and detection speed of incipient fault detection in plant-wide processes.