Fault Detection Of Multimode Processes Research Articles

Double-weighted neighborhood standardization method with applications to multimode-process fault detection

Double-weighted neighborhood standardization method with applications to multimode-process fault detection

A Just-In-Time-Learning-Aided Canonical Correlation Analysis Method for Multimode Process Monitoring and Fault Detection

In this article, a just-in-time-learning (JITL)-aided canonical correlation analysis (CCA) is proposed for the monitoring and fault detection of multimode processes. A canonical correlation analysis (CCA)-based fault detection method has been applied to single-operating-mode processes. However, CCA has limitations in handling processes with multiple operating points. These limitations are illustrated by a numerical example. To reduce the time for searching relevant data, K -means is integrated into the JITL to build the local CCA model. Furthermore, the proposed method is compared with commonly used kernel-based methods in terms of computational complexity and interpretability of the results. Finally, the validity and efficacy of the proposed method are shown using an industrial benchmark process. Results show that the proposed method has better performance than conventional methods in terms of fault detection rate while still tracking changes in the system.

Multimode Process Fault Detection Based on Local Density Ratio-Weighted Support Vector Data Description

Industrial process fault detection plays an important role for process security, and data-based process monitoring is an effective way to detect fault. Industrial processes typically have multiple operating modes with different data distribution and outliers. This paper proposes a novel multimode process fault detection method based on local density ratio-weighted support vector data description (LDR-wSVDD) to address the multimodal process monitoring with different density and outliers in training samples. By considering both global density distribution and local data structure, this method provides a weight for each training sample based on its density information to make sure that the outliers with lower weight have less influence on the normal sample model. Meanwhile, it maintains the efficiency of SVDD single hypersphere model for multimode processes. The feasibility and validity of the LDR-wSVDD approach for multimode multidensity process monitoring are demonstrated through a numerical example and T...

Fault Detection of Multimode Processes Using Concurrent Projection to Latent Structures

Process monitoring based on Concurrent Projection to Latent Structures (CPLS) has been proposed recently as an efficient process monitoring tool to detect the input-relevant and output-relevant faults. In this paper, a novel multimode process monitoring approach is proposed to introduce external analysis into the CPLS monitoring framework. The process variables are divided into external variables, main variables and the output variables. The mode change is detected by monitoring the variation of the external variables through a mode-detection CPLS. The prediction of the main and quality variables by external variables is done by a PLS. After the influence of the external variables is removed from the main and quality variables, another process-monitoring CPLS is built to detect the main-variable-relevant faults and quality-variable-relevant faults. The proposed approach is illustrated with a simulation process.

Multimode Process Monitoring Based on Sparse Principal Component Selection and Bayesian Inference-Based Probability

According to the demand for diversified products, modern industrial processes typically have multiple operating modes. At the same time, variables within the same mode often follow a mixture of Gaussian distributions. In this paper, a novel algorithm based on sparse principal component selection (SPCS) and Bayesian inference-based probability (BIP) is proposed for multimode process monitoring. SPCS can be formulated as a just-in-time regression between all PCs and each sample. SPCS selects PCs according to the nonzero regression coefficients which indicate the compact expression of the sample. This expression is necessarilydiscriminative: amongst all subset of PCs, SPCS selects the PCs which most compactly express the sample and rejects all other possible but less compact expressions. BIP is utilized to compute the posterior probabilities of each monitored sample belonging to the multiple components and derive an integrated global probabilistic index for fault detection of multimode processes. Finally, to verify its superiority, the SPCS-BIP algorithm is applied to the Tennessee Eastman (TE) benchmark process and a continuous stirred-tank reactor (CSTR) process.

Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis

Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis (LNSA). In the proposed method, prior process knowledge is not required and only the multimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis (PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Finally a simulated continuous stirred tank system is used to demonstrate the effectiveness of the proposed method. The simulation results show that LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.

Multimode process monitoring with PCA mixture model

For multimode processes, Gaussian mixture model (GMM) has been applied to estimate the probability density function of the process data under normal-operational condition in last few years. However, learning GMM with the expectation maximization (EM) algorithm from process data can be difficult or even infeasible for high-dimensional and collinear process variables. To address this issue, a novel multimode process monitoring approach based on PCA mixture model is proposed. First, the PCA technique is directly applied to the covariance matrix of each Gaussian component to reduce the dimension of process variables and to obtain nonsingular covariance matrices. Then the Bayesian Ying-Yang incremental EM algorithm is adopted to automatically optimize the number of mixture components. With the obtained PCA mixture model, a novel process monitoring scheme is derived for fault detection of multimode processes. Three case studies are provided to evaluate the monitoring performance of the proposed method.

A novel local neighborhood standardization strategy and its application in fault detection of multimode processes

Complex modern industrial processes often have several operating regions, and the multimode process data would follow different distributions. However, most multivariate statistical process monitoring (MSPM) methods, such as principal component analysis (PCA) and partial least squares (PLS), have a fundamental assumption that the operating data follow a unimodal distribution. These data-based MSPM methods cannot perform well when directly applied to multimode processes because the assumption becomes invalid. In this paper, a novel local neighborhood standardization (LNS) strategy is proposed as a data preprocessing method to address the challenges caused by the multimode characteristic of operating data. After a thorough analysis of LNS, a new method called LNS-PCA is developed for fault detection in multimode processes. Multimode data can be scaled to follow one single distribution by using LNS, approximately. Based on these scaled operating data, the monitoring model can be built more accurately by utilizing local information. The advantages of LNS-PCA are that only one model is required for multimode process monitoring and no process knowledge is needed. Finally, the validity and effectiveness of LNS-PCA are illustrated through a numerical example and the Tennessee Eastman process. The results show that the proposed data preprocessing method is very suitable for multimode data normalization and LNS-PCA is superior to traditional PCA for fault detection.

Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models

AbstractFor complex industrial processes with multiple operating conditions, the traditional multivariate process monitoring techniques such as principal component analysis (PCA) and partial least squares (PLS) are ill‐suited because the fundamental assumption that the operating data follow a unimodal Gaussian distribution usually becomes invalid. In this article, a novel multimode process monitoring approach based on finite Gaussian mixture model (FGMM) and Bayesian inference strategy is proposed. First, the process data are assumed to be from a number of different clusters, each of which corresponds to an operating mode and can be characterized by a Gaussian component. In the absence of a priori process knowledge, the Figueiredo–Jain (F–J) algorithm is then adopted to automatically optimize the number of Gaussian components and estimate their statistical distribution parameters. With the obtained FGMM, a Bayesian inference strategy is further utilized to compute the posterior probabilities of each monitored sample belonging to the multiple components and derive an integrated global probabilistic index for fault detection of multimode processes. The validity and effectiveness of the proposed monitoring approach are illustrated through three examples: (1) a simple multivariate linear system, (2) a simulated continuous stirred tank heater (CSTH) process, and (3) the Tennessee Eastman challenge problem. The comparison of monitoring results demonstrates that the proposed approach is superior to the conventional PCA method and can achieve accurate and early detection of various types of faults in multimode processes. © 2008 American Institute of Chemical Engineers AIChE J, 2008