scholarly journals Global Plus Local Projection to Latent Structures

2022 ◽  
pp. 147-172
Author(s):  
Jing Wang ◽  
Jinglin Zhou ◽  
Xiaolu Chen
Keyword(s):  
Industrial Process ◽  
Economic Loss ◽  
Fault Detection And Diagnosis ◽  
Statistical Process ◽  
Local Projection ◽  
Process Operation ◽  
Latent Structures ◽  
Detection And Diagnosis ◽  
Highly Correlated ◽  
Statistical Process Monitoring

AbstractOwing to the raised demands on process operation and product quality, the modern industrial process becomes more complicated when accompanied by the large number of process and quality variables produced. Therefore, quality-related fault detection and diagnosis are extremely necessary for complex industrial processes. Data-driven statistical process monitoring plays an important role in this topic for digging out the useful information from these highly correlated process and quality variables, because the quality variables are measured at a much lower frequency and usually have a significant time delay (Ding 2014; Aumi et al. 2013; Peng et al. 2015; Zhang et al. 2016; Yin et al. 2014). Monitoring the process variables related to the quality variables is significant for finding potential harm that may lead to system shutdown with possible enormous economic loss.

scholarly journals Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 227
Author(s):  
Jinlin Zhu ◽  
Muyun Jiang ◽  
Zhong Liu
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Process Monitoring ◽  
Industrial Process ◽  
Fault Detection And Diagnosis ◽  
Propagation Mechanism ◽  
Tennessee Eastman Process ◽  
Variational Autoencoder ◽  
Detection And Diagnosis ◽  
Detection Schemes

This work considers industrial process monitoring using a variational autoencoder (VAE). As a powerful deep generative model, the variational autoencoder and its variants have become popular for process monitoring. However, its monitoring ability, especially its fault diagnosis ability, has not been well investigated. In this paper, the process modeling and monitoring capabilities of several VAE variants are comprehensively studied. First, fault detection schemes are defined in three distinct ways, considering latent, residual, and the combined domains. Afterwards, to conduct the fault diagnosis, we first define the deep contribution plot, and then a deep reconstruction-based contribution diagram is proposed for deep domains under the fault propagation mechanism. In a case study, the performance of the process monitoring capability of four deep VAE models, namely, the static VAE model, the dynamic VAE model, and the recurrent VAE models (LSTM-VAE and GRU-VAE), has been comparatively evaluated on the industrial benchmark Tennessee Eastman process. Results show that recurrent VAEs with a deep reconstruction-based diagnosis mechanism are recommended for industrial process monitoring tasks.

Advances in Data-Driven Monitoring Methods for Complex Process

2013 ◽  
Vol 423-426 ◽  
pp. 2448-2451 ◽  
Author(s):  
Ru Qing Chen
Keyword(s):  
Gaussian Process ◽  
Process Monitoring ◽  
Performance Monitoring ◽  
Industrial Process ◽  
Nonlinear Process ◽  
Fault Detection And Diagnosis ◽  
Monitoring Methods ◽  
Process Data ◽  
Statistical Process ◽  
Non Gaussian

In modern industrial processes, effective performance monitoring and quality prediction are the key to ensure plant safety and enhance product quality. The research significance and background of process monitoring and fault diagnosis technologies are described and the current advances in data-based process monitoring methods are summed up in this paper. Then the multivariate statistical process control (MSPC) methods for process with single constraint, especially for single non-Gaussian process or nonlinear process are elaborated. As real industrial process data often show strong non-Gaussian and dynamic behaviors, study on monitoring technologies for dynamic non-Gaussian process is of great importance. Finally, some challenges such as non-Gaussian and dynamic process, fault detection and diagnosis as well as new MSPC methods are indicated.

scholarly journals A Combined Markov Chain Model and Generalized Projection Nonnegative Matrix Factorization Approach for Fault Diagnosis

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Niu Yuguang ◽  
Wang Shilin ◽  
Du Ming
Keyword(s):  
Markov Chain ◽  
Process Monitoring ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Markov Chain Model ◽  
Nonnegative Matrix ◽  
Industrial Process ◽  
Chain Model ◽  
Statistical Process ◽  
Statistical Process Monitoring

The presence of sets of incomplete measurements is a significant issue in the real-world application of multivariate statistical process monitoring models for industrial process fault detection. Since the missing data in the incomplete measurements are usually correlated with some of the available variables, these measurements can be used if an efficient algorithm is presented. To resolve the problem, a novel method combining Markov chain model and generalized projection nonnegative matrix factorization (MCM-GPNMF) is proposed to detect and diagnose the faults in industrial process. The basic idea of the approach is to use MCM-GPNMF to extract the dominant variables from incomplete process data and to combine them with statistical process monitoring techniques. TG2 and SPEG statistics are defined as online monitoring quantities for fault detection and corresponding contribution plots are also considered for fault isolation. The proposed method is applied to a 1000 MW unit boiler process. The simulation results clearly illustrate the feasibility of the proposed method.

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