Diagnosis of multiple and unknown faults using the causal map and multivariate statistics

Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis

“…From the analysis of such process maps, causality directions can be readily established and translated into computational code to be integrated in IPM. One approach for codifying this information is through Causal Maps [94][95][96][97]. Other qualitative and semi-quantitative descriptions for incorporating the process causal structure include bond graphs [98,99], signed digraphs (SGDs) [100][101][102][103], parity relations [104], gray-box models [105] and Bayesian Networks [98,106,107].…”

Variable selection method for fault isolation using least absolute shrinkage and selection operator (LASSO)

“…To give some examples, Lee et al conducted root cause fault diagnosis of this process based on system decomposition and dynamic partial least squares [34], Zhao and Gao tested the monitoring performance of the fault-relevant PCA also using this process [35], Lau et al proposed to use multi-scale PCA and adaptive neuro-fuzzy inference system to detect and diagnose the faults contained in the TE data [36], Chiang et al used this process to demonstrate the utilization of causal analysis [37], and Rato and Reis conducted fault detection in the TE process using dynamic principal components analysis based on decorrelated residuals [38]. In the survey papers, Kano et al [39] and Yin et al [40] studied the performances of various statistical process monitoring methods through their applications to the TE process.…”

Evaluation of diagnosis methods in PCA-based Multivariate Statistical Process Control

“…Other data driven approaches suggest the application of causal maps to identify the root causes of an anomalous observation. The authors present an evaluation of the methods proposed in their works, including several cases of study on a Tennessee Eastman plant simulation [16] [17].…”