An unsupervised learning method is developed for fault detection and diagnosis with missing data for chemical processes based on the multivariate extension of joint recurrence quantification analysis (JRQA) and clustering. The application of the proposed method is assessed in the presence and absence of imputation methods. To provide a comprehensive scheme, three different processes were utilized including, silica particle flocculation (SFP) as an unstable batch process, a chemical looping combustion (CLC) process, and the Tennessee Eastman process (TEP) as the control system design benchmark. The application of the developed method demonstrated that the JRQA method has the best performance in fault diagnosis of the complete dataset in all three processes compared to previously developed methods. Moreover, in the case of missing data, the sensitivity of the results can be adjusted by changing the length of the sub-series. The sensitivity of the proposed method is 33% lower for SFP, 30% for CLC and 32% for TEP, compared to the probabilistic kernel principal components analysis (PKPCA)-based method.

基于联合递归量化分析 (JRQA) 和聚类的多元扩展，开发了一种无监督学习方法，用于化学过程缺失数据的故障检测和诊断。在存在和不存在插补方法的情况下评估所提出方法的应用。为了提供全面的方案，使用了三种不同的工艺，包括作为不稳定间歇工艺的二氧化硅颗粒絮凝 (SFP)、化学循环燃烧 (CLC) 工艺和作为控制系统设计基准的田纳西伊士曼工艺 (TEP)。所开发方法的应用表明，与先前开发的方法相比，JRQA 方法在所有三个过程中对完整数据集的故障诊断具有最佳性能。此外，在缺失数据的情况下，可以通过改变子系列的长度来调整结果的灵敏度。与基于概率核主成分分析 (PKPCA) 的方法相比，所提出方法的灵敏度对于 SFP、CLC 和 TEP 分别低 33%、30% 和 32%。