Quality-relevant Monitoring Research Articles

A novel quality-relevant fault detection method based on MICA-SOM multi-subspace partitioning for non-Gaussian industrial processes

BackgroundEnsuring safe production and process quality is important for chemical processes. Therefore, a multi-subspace partitioning method based on modified independent component analysis (MICA) and self-organizing map (SOM) is proposed for quality-relevant fault detection. MICA can extract several independent components from the input data, where the mixing matrix implies relationship between independent components and input data. MethodsAccording to the mixing matrix and SOM, the measured variables are classified into quality-relevant variables and quality-irrelevant variables. The quality-relevant variables are mapped into quality-relevant independent component space (Qr-ICS) and the quality-irrelevant independent component space (Qir-ICS) by modified independent component regression and feature decomposition. Then, Qr-ICS is used for quality-relevant fault detection. Quality-irrelevant variables are divided into several quality-irrelevant subspaces based on the correlations among variables. MICA models are established for the quality-irrelevant subspaces. Combined with the Qir-ICS, quality-irrelevant fault detection can be realized. Significant FindingsThe application in the TE process shows that the proposed method has better quality-relevant monitoring reliability and quality-irrelevant monitoring performance than several comparison methods. The application in an actual industrial verifies the advantages and effectiveness of the proposed method over several traditional methods in practical applications.

Concurrent Multilayer Fault Monitoring with Nonlinear Latent Variable Regression

Latent variable regression (LVR) constructs the latent structure by maximizing the projection of quality variables on the latent spaces of the process variables, which has demonstrated its effectiveness over other multivariate statistical methods in terms of quality-relevant monitoring. However, it cannot be applied to model most real-world industrial processes due to its implicit linear assumption on the data. To overcome this issue, the kernel trick is designed to develop its nonlinear counterpart. Further, a novel concurrent kernel LVR (CKLVR) method is proposed for multilayer modeling with subsequent decomposition, which separates the process and data spaces comprehensively into five subspaces. The corresponding monitoring statistics are also developed for each subspace extracted by CKLVR, with each serving a particular monitoring purpose. Case studies including a numerical simulation, Dow’s Refining Process, and the Tennessee Eastman Process are employed to illustrate the performance of the proposed algorithm.

Online Quality-Relevant Monitoring with Dynamic Weighted Partial Least Squares

Partial least squares (PLS) is a multivariate statistical analytical method which can be used to extract valuable information from high-dimensional and correlated data effectively. To model the ine...

Learning soft sensors using time difference-based multi-kernel relevance vector machine with applications for quality-relevant monitoring in wastewater treatment.

Considering the time-varying, uncertain and non-linear properties of the wastewater treatment process (WWTPs), a novel multi-kernel relevance vector machine (MRVM) soft sensor based on time difference (TD) is proposed to predict the quality-relevant but difficult-to-measure variable. Firstly, a novel dimension reduction technique is introduced to reduce data dimension and model complexity. Secondly, the parameters of the kernel model are optimized by the intelligent optimization algorithm (PSO). Besides, the TD strategy is introduced to enhance the robustness of MRVM when exposing to dynamic environments. Finally, the proposed model was assessed through two simulation studies and a real WWTP with the results demonstrating the effectiveness of the proposed model. Graphical abstract Framework of Lasso-TD-MRVM soft sensor model.

A Dual Robustness Projection to Latent Structure Method and Its Application

The robust performance of traditional partial least squares (PLS) method is poor because the objective function of PLS is to maximize the square of L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm between the input and output data spaces, and this method is sensitive to outliers but insensitive to local features and causes nuisance alarms. Conversely, the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm can perfectly maintain the signal relative size in global statistical features extraction, and its feature extraction results are robust to outliers. Therefore, in this article a novel dual robustness projection to latent structure method, which includes the robustness of feature extraction and regression coefficients, based on the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm (L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -PLS) is proposed. Then, more attention is paid to the robustness of both the input and output spaces and the robustness relationship between those spaces in the dual robustness L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> PLS method. The corresponding quality-relevant monitoring strategy is also established. Finally, the robustness of the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -PLS method is illustrated with the Tennessee Eastman process simulation platform. The results show that the proposed method is insensitive to outliers and maintains the signal relative size, and the fault monitoring results are consistent with the actual situation.

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

To implement the quality-relevant monitoring scheme for batch processes with multiple output modes, this paper presents a novel methodology based on stochastic programming. Bringing together tools from stochastic programming and ensemble learning, the developed methodology focuses on the robust monitoring of process quality-relevant variables by taking the stochastic nature of batch process parameters explicitly into consideration. To handle the problem of missing data and lack of historical batch data, a bagging approach is introduced to generate individual quality-relevant sub-datasets, which are used to construct the corresponding monitoring sub-models. For each model, stochastic programming is used to construct an optimal quality trajectory, which is regarded as the reference for online quality monitoring. Then, for each sub-model, a corresponding control limit is obtained by computing historical residuals between the actual output and the optimal trajectory. For online monitoring, the current sample is examined by all sub-models, and whether the monitoring statistic exceeds the control limits is recorded for further analysis. The final step is ensemble learning via Bayesian fusion strategy, which is under the probabilistic framework. The implementation and effectiveness of the developed methodology are demonstrated through two case studies, including a numerical example, and a simulated fed-batch penicillin fermentation process.

Supervised Diagnosis of Quality and Process Faults with Canonical Correlation Analysis

Concurrent monitoring schemes that achieve simultaneous process and quality-relevant monitoring have recently attracted much attention. In this Article, we formulate a supervised fault diagnosis fr...

Concurrent Monitoring and Diagnosis of Process and Quality Faults with Canonical Correlation Analysis

Partial least squares and canonical correlation analysis are latent variable models suitable for quality-relevant monitoring based on process and quality data. Recently, concurrent monitoring schemes are proposed to achieve simultaneous process and quality monitoring. This paper defines and analyzes quality-relevant monitoring based on these popular latent structure modeling methods, and the associated quality-relevant monitoring statistics are defined. Additionally, contribution plots and reconstruction-based contribution diagnosis methods are developed for concurrent fault diagnosis. Multi-dimensional quality-relevant faults can be diagnosed in the same reconstruction framework. Finally, a detailed case study on Tennessee Eastman process is shown to illustrate the diagnosis of process and quality faults and the prognosis of quality-relevant faults.

Quality prediction and quality-relevant monitoring with multilinear PLS for batch processes

The multilinear regression method is applied for quality prediction and quality-relevant monitoring in batch processes. Four multilinear partial least squares (PLS) models are investigated, including three higher-order PLS (HOPLS) models, termed as HOPLS-Tucker, HOPLS-RTucker and HOPLS-CP, and the N-way PLS (N-PLS) model. These multilinear PLS methods have two advantages as compared to the unfold-PLS method. Firstly, they retain the inherent three-way representation of batch data and avoid the disadvantages caused by data unfolding, resulting in more stable process models. Secondly, they summarize the main information on each mode of data and describe the three-way interactions between them, and therefore have better modeling accuracy and intuitive interpretability. Online quality prediction and quality-relevant monitoring methods are developed by combining multilinear PLS with the moving data window technique. These methods are tested in a fed-batch penicillin fermentation process. The results indicate that the multilinear PLS method has higher predictive accuracy, better anti-noise capability and monitoring performance than the unfold-PLS method.

Quality-Relevant Monitoring and Diagnosis with Dynamic Concurrent Projection to Latent Structures

In this paper, a data-driven dynamic concurrent projection to latent structures (DCPLS) approach is proposed for quality-relevant fault diagnosis of dynamic processes. First, a novel DCPLS algorithm is proposed for dynamic modeling which captures the dynamic correlations between quality variables and process variables. Quality-specific variations, process-specific variations, and quality-process covariations of dynamic processes are monitored respectively. Secondly, a multi-block extension of DCPLS is designed to compute the contributions according to block partition of the lagged variables, in order to help localize faults. Finally, the application results on strip-thickness relevant fault diagnosis for a practical cold rolling continuous annealing process (CAP) demonstrate the effectiveness of the proposed methods.