Fault detection and diagnosis in water resource recovery facilities using incremental PCA (original) (raw)

Multivariate process monitoring and fault diagnosis by multi-scale PCA

Computers & Chemical Engineering, 2002

Chemical process plant safety, production specifications, environmental regulations, operational constraints, and plant economics are some of the main reasons driving an upward interest in research and development of more robust methods for process monitoring and control. Principal component analysis (PCA) has long been used in fault detection by extracting relevant information from multivariate chemical data. The recent success of wavelets and multi-scale methods in chemical process monitoring and control has catalyzed an interest in the investigation of wavelets based methods for fault detection. In the present work, multi-scale principal component analysis (MSPCA) is used for fault detection and diagnosis. MSPCA simultaneously extracts both, cross correlation across the sensors (PCA approach) and auto-correlation within a sensor (wavelet approach). Using wavelets, the individual sensor signals are decomposed into approximations and details at different scales. Contributions from each scale are collected in separate matrices, and a PCA model is then constructed to extract correlation at each scale. The multi-scale nature of MSPCA formulation makes it suitable to work with process data that are typically non-stationary and represent the cumulative effect of many underlying process phenomena, each operating at a different scale. The proposed MSPCA approach is able to outperform the conventional PCA based approach in detecting and identifying real process faults in an industrial process, and yields minimum false alarms. Additionally, the advantage of MSPCA, over the traditional PCA approach for sensor validation, is also demonstrated on an industrial boiler data set.

Data–Driven Models for Fault Detection Using Kernel Pca: A Water Distribution System Case Study

2013

Kernel Principal Component Analysis (KPCA), an example of machine learning, can be considered a non-linear extension of the PCA method. While various applications of KPCA are known, this paper explores the possibility to use it for building a data-driven model of a non-linear system—the water distribution system of the Chojnice town (Poland). This model is utilised for fault detection with the emphasis on water leakage detection. A systematic description of the system’s framework is followed by evaluation of its performance. Simulations prove that the presented approach is both flexible and efficient.

Fault detection for large scale systems using dynamic principal components analysis with adaptation

First IFAC Workshop on Applications of Large Scale Industrial Systems, 2006, 2006

The Dynamic Principal Component Analysis is an adequate tool for the monitoring of large scale systems based on the model of multivariate historical data under the assumption of stationarity, however, false alarms occur for non-stationary new observations during the monitoring phase. In order to reduce the false alarms rate, this paper extends the DPCA based monitoring for non-stationary data of linear dynamic systems, including an on-line means estimator to standardize new observations according to the estimated means. The effectiveness of the proposed methodology is evaluated for fault detection in a interconnected tanks system.

Improved process monitoring using nonlinear principal component models

International Journal of Intelligent Systems, 2008

This paper presents two new approaches for use in complete process monitoring. The first concerns the identification of nonlinear principal component models. This involves the application of linear principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation.The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented. © 2008 Wiley Periodicals, Inc.

New Adaptive Kernel Principal Component Analysis for Nonlinear Dynamic Process Monitoring

International Journal of Applied Mathematics and Information Sciences

In this paper a new algorithm for adaptive kernel principal component analysis (AKPCA) is proposed for dynamic process monitoring. The proposed AKPCA algorithm combine two existing algorithms, the recursive weighted PCA (RWPCA) and the moving window kernel PCA algorithms. For fault detection and isolation, a set of structured residuals is generated by using a partial AKPCA models. Each partial AKPCAmodel is performed on subsets of variables. The structured residuals are utilized in composing an isolation scheme, according to a properly designed incidence matrix. The results for applying this algorithm on the nonlinear time varying processes of the Tennessee Eastman shows its feasibility and advantageous performances.

Fault detection using dynamic principal component analysis by average estimation

One of the most popular multivariate statistical methods used for signals based process monitoring and data compression is the Dynamic Principal Component Analysis. This method computes the orthogonal principal directions assuming stationarity in the time series of the process, however, if observations are not stationary, false alarms could be generated during the fault detection and isolation task. To reduce the false alarms rate, this paper extends the dynamic principal component analysis for the case on non stationary data. This is achieved including in the monitoring procedure an on-line mean estimator and standardizing the time series data of the process according to the values generated by the estimator. As study case the detection of faults in a flow control valve has been used, in which it is assumed that the control signal, stem displacement and flow are measured signals. Simulator data are used to adjust the procedure and show the improvement of the novel dynamical principal component analysis methodology.

Adaptive kernel principal component analysis for nonlinear dynamic process monitoring

2013 9th Asian Control Conference (ASCC), 2013

In this paper a new algorithm for adaptive kernel principal component analysis (AKPCA) is proposed for dynamic process monitoring. The proposed AKPCA algorithm combine two existing algorithms, the recursive weighted PCA (RWPCA) and the moving window kernel PCA algorithms. For fault detection and isolation, a set of structured residuals is generated by using a partial AKPCA models. Each partial AKPCA model is performed on subsets of variables. The structured residuals are utilized in composing an isolation scheme, according to a properly designed incidence matrix. The results for applying this algorithm on the nonlinear time varying processes of the Tennessee Eastman shows its feasibility and advantageous performances.

A REFINEMENT OF DYNAMIC PRINCIPAL COMPONENT ANALYSIS FOR FAULT DETECTION

For process monitoring the Dynamic Principal Component Analysis (DPCA) models multivariate historical data under the assumption of stationarity, however, false alarms result for non-stationary new observations during the monitoring task. In order to reduce the false alarms rate, this paper extends the DPCA based monitoring for non-stationary data of linear dynamic systems, including an on-line means estimator and standardizing new observations according to the estimated means. The effectiveness of the proposed methodology is evaluated for faults detection in a control valve benchmark, assuming as measured signals the control signal, stem displacement and flow.

Nonlinear process monitoring using kernel principal component analysis

Chemical Engineering Science, 2004

In this paper, a new nonlinear process monitoring technique based on kernel principal component analysis (KPCA) is developed. KPCA has emerged in recent years as a promising method for tackling nonlinear systems. KPCA can e ciently compute principal components in high-dimensional feature spaces by means of integral operators and nonlinear kernel functions. The basic idea of KPCA is to ÿrst map the input space into a feature space via nonlinear mapping and then to compute the principal components in that feature space. In comparison to other nonlinear principal component analysis (PCA) techniques, KPCA requires only the solution of an eigenvalue problem and does not entail any nonlinear optimization. In addition, the number of principal components need not be speciÿed prior to modeling. In this paper, a simple approach to calculating the squared prediction error (SPE) in the feature space is also suggested. Based on T 2 and SPE charts in the feature space, KPCA was applied to fault detection in two example systems: a simple multivariate process and the simulation benchmark of the biological wastewater treatment process. The proposed approach e ectively captured the nonlinear relationship in the process variables and showed superior process monitoring performance compared to linear PCA. ?