Rajamani Doraiswami

Performance monitoring in expert control systems

Abstract A general structure of a real-time expert controller (EC) is proposed in this paper. The controller consists of (i) a knowledge-base, (ii) a real-time inference engine, (iii) a real-time information pre-processor, and (iv) control mechanisms. A real-time information pre-processor is developed. It contains a set of digital signal processing schemes which are used to monitor the overall system performance and diagnose potential control system component failures. Both forward-chaining and backward-chaining inference mechanisms are used in the inference engine. The knowledge-base and the inference engine are constructed using PROLOG, and the C language is used to implement the information pre-processor. The EC is implemented in real-time on a microcomputer. The performance of a prototype EC using the designed information pre-processor is evaluated by simulating in real-time various types of failures in a hydraulic turbine generator governor system.

An FPGA-Based Singular Value Decomposition Processor

A two-sided rotation Jacobi SVD algorithm is used to compute the SVD and is implemented on a two million gate FPGA. A mesh-connected array structure is proposed based on Brent, Luk, and Van Loans idea of an expandable square systolic array of simple 2x2 processors to compute the SVD of a large matrix, so as to shorten the iteration time and thus increase the implementation speed. The array consists of an n/2xn/2 array of 2x2 processor elements to compute the SVD of an nxn matrix. The trigonometric functions and the vector multiplication in the algorithm are tailored to the use of CORDIC (coordinate rotation digital computer) algorithms for hardware-efficient solutions. Two SVD processors, the basic SVD processor and the extended SVD processor, were developed. The algorithms to decompose the matrix were first evaluated in Matlab and then the processors were implemented using the Virtex-II FPGA from Xilinx as the target device. The basic SVD processor utilizes the proposed mesh-connected array structure and CORDIC algorithm. The implementation concentrates on utilizing the features of the FPGA to speed up operations and reduce the area required. In order to compute a large SVD without increasing the size of the FPGA, the extended SVD processor was developed to reuse the SVD array of the basic SVD processor. These two processors were successfully implemented on the FPGA device. Speed data and comparisons are presented

A robust influence matrix approach to fault diagnosis

A robust scheme is proposed to detect faults, isolate them, and estimate their severity. The feature vector, which is a vector formed of the coefficients of the system transfer function, is estimated using a robust two-stage identification scheme: 1) a higher-order model is estimated using a singular value decomposition-based batch least-squares algorithm; and 2) a reduced-order model is derived by filtering-out the noise artifacts. The system is decomposed into functional units characterized by physical parameters. The influence of these physical parameters on the feature vector is captured in a vector termed the influence vector. The distance between, the inner product of the feature vector, and the influence vector are analyzed for diagnose faults. The proposed scheme is evaluated both on a simulated as well as an actual control system.

A New Diagnostic Model for Identifying Parametric Faults

This paper presents a new approach to failure detection and isolation (FDI) for systems modeled as an interconnection of subsystems that are each subject to parametric faults. This paper develops the concept of a diagnostic model and the concept of a fault emulator which are used to model and parameterize subsystem faults. There are two stages to the FDI scheme. In the first stage there is a requirement to identify the diagnostic model. Once identified, the diagnostic model is used in the second stage to generate a residual. Artifacts within the measured residual are then used as a basis for identifying parametric faults. The scheme is distinct from others as it does not require an online recursive least squares type identifier.

Autonomous control systems: Monitoring, diagnosis, and tuning

A systematic and unified approach which accomplishes performance monitoring, performance improvement and fault prediction in control systems is proposed. The feature vector which is a vector formed of the coefficients of the estimate of the sensitivity function and the influence matrix which is the Jacobian of the feature vector with respect to the physical parameter are shown to contain the relevant information to realize an autonomous control system. The feature vector is estimated using a robust, accurate and reliable linear predictive coding algorithm. The influence matrix is computed by perturbing the physical parameters one at a time and estimating the feature vectors for each case. The proposed scheme is evaluated both on simulated as well as on actual control systems.

Performance monitoring and fault prediction using a linear predictive coding algorithm

Abstract A systematic and unified approach to monitor performance and to predict fault is proposed based on a robust Linear Predictive Coding Algorithm (LPCA) implemented as part of an expert system. An Auto-Regressive and Moving Average (ARMA) model of the measured output is estimated in real-time and the model estimate is used to monitor performance and predict faults. The expert system is comprised of a knowledge-base which is represented in the form of frames and rules, and the rules are fired in the order of decreasing importance and increasing computation. The proposed scheme is evaluated both on simulated and physical control systems.

On-line frequency-and time-domain identification of a linear multivariable system

Abstract A method for the on-line identification of a linear multivariable plant subject to both deterministic and stochastic disturbances is proposed. The identification scheme rests on the use of a sum of sinusoids of distinct frequencies as probing-signal inputs and on the employment of linear time-varying filters to filter the plant inputs and the plant outputs. The time-varying filters are essentially banks of narrow-band filters tuned to the probing-signal frequencies. The filtered plant inputs and the filtered plant outputs yield an estimate of the plant transfer function matrix at the probing-signal frequencies. The filtered data are further processed using a recursive least-squares algorithm and a time-domain model estimate is obtained in terms of the coefficients of the difference equation relating each input-output pair. The identification algorithm is decoupled in the sense that the estimate of the transfer function or difference equation between the ith input and jth output is unaffected by o...

Identification of Physical Systems: Applications to Condition Monitoring, Fault Diagnosis, Soft Sensor and Controller Design

Develops a systematic and a unified approach to the problem of physical system identification and its practical applicationsThere is a need for a book which develops a systematic and a unified approach to the problem of physical system identification and its practical applications. Identification of Physical Systems addresses this need, developing identification theory using a coherent, simple and yet rigorous approach. Starting with a least-squares method, the author develops various schemes to address the issues of accuracy, variation in the operating regimes, closed loop and interconnected subsystems. He presents a non-parametric signal or data-based scheme to identify a system to provide a quick macroscopic picture of the system to complement the precise microscopic picture given by the parametric model based scheme. Finally, he develops a sequential integration of totally different schemes such as non-parametric, Kalman filter and parametric model to meet the speed and accuracy requirement of mission critical systems.Identification of Physical Systems includes case studies for the application of identification on physical laboratory scale systems, as well as number of illustrative examples throughout the book. Provides a clear understanding of theoretical and practical issues in identification and its applications, enabling the reader to grasp a clear understanding of the theory and apply it to practical problems Offers a self contained guide by including the background necessary to understand this interdisciplinary subject Includes case studies for the application of identification on physical laboratory scale systems, as well as number of illustrative examples throughout the book Applications of this methodology include the emerging areas of fault diagnosis, performance monitoring, condition-based maintenance, software-based sensors and autonomous systems.Primary: Graduate students and practicing engineers in electrical computer, biomedical, chemical and mechanical engineering.Secondary: Computer science students developing software for performance monitoring, fault diagnosis, condition-based monitoring or autonomous operation.

A reliable composite classification strategy

A composite classification scheme is proposed by combining several classifiers with distinctly different design methodologies. The classifiers are selected from a number of state of the art pattern classification schemes with a view to obtain superior performance. In this scheme, no a priori information except a set of pre-classified data is assumed to be available. By using distinctly different classifiers, the common mode data misclassification is reduced. Traditionally, after the design and evaluation phase, the pre-classified data is discarded. In this scheme, however, the misclassified data from each classifier in the training set is tagged and stored with a view to weight the decisions of the classifiers. If a given test sample is close to a misclassified data cluster of a particular classifier, then the decision made by this classifier is given a lower weighting. The final decision is made by analysing the weighted combination of individual classifier decisions. The proposed algorithm is evaluated on both simulated data and on a biological cell classification problem and it is shown that improved accuracy is obtained when compared to that of the most accurate classifier.

A novel two-stage identification of unstable systems

Issues in identification of unstable plant operating in a closed loop configuration are analyzed. It is shown that the unstable poles of the plant can be identified from the zeros of the input sensitivity function (transfer function relating the reference input to the plant input). A two-stage MIMO subspace identification scheme is proposed to identify the plant and the input sensitivity function. In the first stage, the input sensitivity function is identified using the reference input as the input, and the plant output and the plant input as outputs. In the second stage, the plant is identified using the input as the estimated plant input, the output the estimated plant output, where the estimates are obtained from the first stage. The proposed scheme is evaluated on a both simulated as well as on a physical laboratory scale magnetic levitation system.