An extended Tennessee Eastman simulation dataset for fault-detection and decision support systems

A multi-rate high-order dynamic twin-latent-variable probabilistic modeling and its process monitoring application

Quality-relevant process monitoring provides important guarantees for the safety of industrial operation, which is based on the assumption that data collection is complete and low-order autocorrelated. However, real industrial processes always exhibit complex characteristics such as multi-rate sampling and high-order dynamic, which pose great challenges for process monitoring. To this end, a multi-rate high-order dynamic twin-latent-variable probabilistic (MHDTVP) model is presented in this paper to extract data correlations among multi-rate measurements from quality-relevant and irrelevant perspectives. Moreover, to reveal the dynamics in the multi-rate sampling process, an autoregressive twin-latent-variable structure is designed to extract both quality-relevant and quality-irrelevant high-order dynamic features. In the MHDTVP model, parameters are trained through an efficient expectation maximization (EM) iteration framework. Finally, the performance conclusions of MHDTVP are validated with the Tennessee Eastman process (TEP) and Thermal Power Plant (TPP). The experimental results demonstrate that the proposed model exhibits superior monitoring efficiency for multi-rate dynamic processes compared to similar approaches.

Causal Plot: Causal-Based Fault Diagnosis Method Based on Causal Analysis

Fault diagnosis is crucial for realizing safe process operation when a fault occurs. Multivariate statistical process control (MSPC) has widely been adopted for fault detection in real processes, and contribution plots based on MSPC are a well-known fault diagnosis method, but it does not always correctly diagnose the causes of faults. This study proposes a new fault diagnosis method based on the causality between process variables and a monitored index for fault detection, which is referred to as a causal plot. The proposed causal plot utilizes a linear non-Gaussian acyclic model (LiNGAM), which is a data-driven causal inference algorithm. LiNGAM estimates a causal structure only from data. In the proposed causal plot, the causality of a monitored index of fault detection methods, in addition to process variables, is estimated with LiNGAM when a fault is detected with the monitored index. The process variables having significant causal relationships with the monitored indexes are identified as causes of faults. In this study, the proposed causal plot was applied to fault diagnosis problems of a vinyl acetate monomer (VAM) manufacturing process. The application results showed that the proposed causal plot diagnosed appropriate causes of faults even when conventional contribution plots could not do the same. In addition, we discuss the effects of the presence of a recycle flow on fault diagnosis results based on the analysis result of the VAM process. The proposed causal plot contributes to realizing safe and efficient process operations.

Plant health index as an anomaly detection tool for oil refinery processes

Early detection of significant abnormal changes is highly desirable for oil refinery processes, which consist of sophisticated unit operations handling hazardous and flammable inventories and operating at high temperature and pressure. Close monitoring and anomaly detection are vital for avoiding major accidents and losses and enable intervention before failure occurrence. A new big data analytics tool called Plant Health Index (PHI) is proposed in this work. PHI is a statistical anomaly detection software that trains its model using online normal plant operation, then uses statistical analytics to detect anomalies. For detecting the anomalies, a combined method of multivariate analysis of residuals and nonparametric models of the process is employed. The methodology provides a structured representation of the plant variables to ease the detection of problems along with the detection of operation changes of the system. The PHI system has been tested on a hydrotreating units in a refinery, which consists of catalytic reactors and separators. The current implementation tagged 170 process variables and proved effective in capturing the normal operational conditions of the plant. When placed online, PHI was able of detecting anomalies that are difficult to detect using the control system and before being detected by the alarm system.

Multimode Process Monitoring Based on Modified Density Peak Clustering and Parallel Variational Autoencoder

Clustering algorithms and deep learning methods have been widely applied in the multimode process monitoring. However, for the process data with unknown mode, traditional clustering methods can hardly identify the number of modes automatically. Further, deep learning methods can learn effective features from nonlinear process data, while the extracted features cannot follow the Gaussian distribution, which may lead to incorrect control limit for fault detection. In this paper, a comprehensive monitoring method based on modified density peak clustering and parallel variational autoencoder (MDPC-PVAE) is proposed for multimode processes. Firstly, a novel clustering algorithm, named MDPC, is presented for the mode identification and division. MDPC can identify the number of modes without prior knowledge of mode information and divide the whole process data into multiple modes. Then, the PVAE is established based on distinguished multimode data to generate the deep nonlinear features, in which the generated features in each VAE follow the Gaussian distribution. Finally, the Gaussian feature representations obtained by PVAE are provided to construct the statistics H2, and the control limits are determined by the kernel density estimation (KDE) method. The effectiveness of the proposed method is evaluated by the Tennessee Eastman process and semiconductor etching process.