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Intelligent Sensor Auditing by Interfacing Knowledge-Based Systems and Multivariate Spm Tools
Abstract
An intelligent sensor monitoring and auditing environment has been developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring sensor status in multivariable processes. The real-time KBS development environment G2 is integrated with an MSPM method that can monitor multivariable dynamic processes with autocorrelated data. The MSPM Inethod is based on a canonical variate state space (CVSS) process model of a dynamic process and it is used for auditing sensor status for bias, drift and excessive noise affecting the sensors of multivariable continuous processes. Changes in the magnitudes of means and variances of residuaLs between measured and predicted process variables are used to detect and discriminate sensor abnormalities. The statistical model that describes the in-control variability of sensor readings is based on a CVSS model. The CV state variables obtained from the state space model are linear combinations of the past process measurements which explain the variability of the future measurements the most, and they are regarded as the principal dynamic dimensions. The method can detect and discriminate between bias change, drift, and variations in noise levels of process sensors based on the analysis of data batches. The MSPM modules are developed in Matlab, cOnverted to C, and linked with G2. The presentation will focus on the structure and performance of the integrated system and illustration of the methodology by simulation studies.
Multivariate statistical process monitoring (MSPM), contribution plots, and parity space fault diagnosis (FD) techniques are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The methods are illustrated by monitoring the critical control points (CCP) and diagnosing causes of abnormal operation of a pilot pasteurization plant. An empirical model of the process is developed by using subspace state space system identification methods and normal process data. The process data collected under the influence of different magnitude and duration of faults in sensors and actuators are used to validate the MSPM and FD techniques. T2 and squared prediction error (SPEN) charts are used as MSPM charts. A parity space technique for dynamic stochastic systems and dynamic trends in contribution plots of T2 and SPEN statistics are used for FD. The detection and FD by these techniques show significant improvements over univariate methods.
A statistical process monitoring method based on a state space model of a dynamic process is introduced for auditing sensor status for bias, drift and excessive noise affecting the sensors of multivariable continuous processes. Changes in the magnitudes of means and variances of residuals between measured and predicted process variables are used to detect and discriminate sensor abnormalities. The statistical model that describes the in-control variability is based on a canonical variate state space (CVSS) model. The CV state variables obtained from the state space model are linear combinations of the past process measurements which explain the variability of the future measurements the most, and they are regarded as the principal dynamic dimensions. The method can detect and discriminate between bias change, drift, and variations in noise levels of process sensors based on the analysis of data batches. An experimental application to a high-temperature short-time (HTST) milk pasteurization process illustrates the proposed methodology.
Industrial continuous processes may have a large number of process variables and are usually operated for extended periods at fixed operating points under closed-loop control, yielding process measurements that are autocorrelated, cross-correlated, and collinear. A statistical process monitoring (SPM) method based on multivariate statistics and system theory is introduced to monitor the variability of such processes. The statistical model that describes the in-control variability is based on a canonical-variate (CV) state-space model that is an equivalent representation of a vector autoregressive moving-average time-series model. The CV state variables obtained from the state-space model are linear combinations of the past process measurements that explain the variability of the future measurements the most. Because of this distinctive feature, the CV state variables are regarded as the principal dynamic directions A T2 statistic based on the CV state variables is used for developing an SPM procedure. Simple examples based on simulated data and an experimental application based on a high-temperature short-time milk pasteurization process illustrate advantages of the proposed SPM method.
Industrial continuous processes are usually operated under closed-loop control, yielding process measurements that are autocorrelated, cross correlated, and collinear. A statistical process monitoring (SPM) method based on state variables is introduced to monitor such processes. The statistical model that describes the in-control variability is based on a canonical variate (CV) state space model. The CV state variables are linear combinations of the past process measurements which explain the variability of the future measurements the most, and they are regarded as the principal dynamic dimensions. A T2 statistic based on the CV state variables is utilized for developing the SPM procedure. The CV state variables are also used for monitoring sensor reliability. An experimental application to a high temperature short time (HTST) pasteurization process illustrates the proposed methodology.
The wealth of process information generated from sensor readings can be used to detect bias changes, drift and/or higher levels of noise in various process sensors. New multivariate statistical techniques permit frequent audits of process sensors. These methods are based on the evaluation of residuals generated by utilizing plant models developed with principal components analysis (PCA) or partial least squares (PLS) methods. The fact that the prediction of each variable in the process involves all the other process variables (PLS) and even itself (PCA), may cause false alarms even though the related sensors function properly. A multipass PLS regression technique is proposed to eliminate the false alarms. The sensor with the highest corruption is discarded from both the calibration and the test data when a sensor failure is detected. This eliminates the effect of the corrupted data on the prediction of the remaining process variables and prevents false alarms. The technique is applied to a High Temperature Short Time (HTST) Pasteurization Pilot plant with six temperature and one flow rate measurements.
Very general reduced order filtering and modeling problems are phased in terms of choosing a state based upon past information to optimally predict the future as measured by a quadratic prediction error criterion. The canonical variate method is extended to approximately solve this problem and give a near optimal reduced-order state space model. The approach is related to the Hankel norm approximation method. The central step in the computation involves a singular value decomposition which is numerically very accurate and stable. An application to reduced-order modeling of transfer functions for stream flow dynamics is given.
Recently a great deal of attention has been given to numerical algorithms for subspace state space system identification (N4SID). In this paper, we derive two new N4SID algorithms to identify mixed deterministic-stochastic systems. Both algorithms determine state sequences through the projection of input and output data. These state sequences are shown to be outputs of non-steady state Kalman filter banks. From these it is easy to determine the state space system matrices. The N4SID algorithms are always convergent (non-iterative) and numerically stable since they only make use of QR and Singular Value Decompositions. Both N4SID algorithms are similar, but the second one trades off accuracy for simplicity. These new algorithms are compared with existing subspace algorithms in theory and in practice.
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