Dynamic mutual information similarity based transient process identification and fault detection

Abstract

Industrial process status can be modified according to continuous operations, which are required by different production specifications. Commonly, in a multimode process, attention was always paid to stable modes, while transitions were neglected. In a transition process, the process may be externally time varying or nonstationary so that the identification and the modelling are intractable to implement using traditional statistical methods. In this article, a transition identification and process monitoring method is proposed to handle the above problems based on a novel dynamic mutual information similarity (DMIS) analysis. Firstly, a multimode process is represented by a series of overlapping moving windows to consider the local information. In order to extract the corresponding dynamic information, each of these windows is modelled using the dynamic partial least squares (DPLS) method. Then, the mutual information algorithm is introduced to calculate the similarity between different latent variables. The hierarchical clustering method is employed to transfer the similarity information into a visualized dendrogram where the whole process, including a transition, is divided into several segments. The statistical characteristics in each segment are relatively stable and can be characterized by conventional multivariate statistical process control methods. Online identification and fault detection are then carried out for the multimode process through a series of DPLS models, established in the offline steps. The feasibility and effectiveness of the proposed method are validated by the Tennessee Eastman (TE) benchmark and a real process. The results of the proposed methods have shown superior performance compared to previous works.

Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes

“…Over the past few decades, various approaches have been proposed to solve the problem using multivariate statistical process monitoring (MSPM) methods, such as multiway principal component analysis (MPCA) and multiway partial least squares (MPLS) [8,9]. These traditional data-based methods are developed for monitoring purposes based on historical batch data [10,11]. Other approaches have also been proposed for quality-relevant monitoring of batch processes to improve performance [12,13].…”

Batch process monitoring using multiway Laplacian autoencoders

“…For the sake of high process safety and product quality, it is extremely necessary to establish a monitoring system to detect abnormal conditions in batch process quickly and effectively 1–3 . Data‐driven multivariate statistical process monitoring (MSPM) techniques have been widely applied in batch process 4–10 …”

Fault Classification in Dynamic Processes Using Multiclass Relevance Vector Machine and Slow Feature Analysis

“…In recent years, many multivariate statistical process monitoring algorithms have been proposed for large-scale industrial processes. He et al proposed a transition identification and process monitoring method for multimode processes using a dynamic mutual information similarity analysis [9]. A supervised non-Gaussian latent structure was introduced by He et al to model the relationship between predictor and quality variables [10].…”