Parity Space Method Research Articles

STRUCTURED FAULT DIAGNOSIS IN MILDLY NONLINEAR SYSTEMS: PARITY SPACE AND INPUT-OUTPUT FORMULATIONS

Abstract. Concerned is residual generation in systems with mild nonlinearities, that is, nonlinearities which involve the inputs, outputs and faults but not the state. It is shown how structured residuals can be designed by the parity space method, in discrete time, if mild nonlinearities are present. Next a number of fundamental links between the parity space and the input-output representations are explored. Utilizing these results, a new design method is developed which finds structured residuals for the mildly nonlinear system entirely in the input-output framework. It is also shown that the two approaches, parity space and input-output, lead to identical residuals.

Fault detection for multirate sampled-data systems with time delays

In this paper, problems of fault detection in multirate sampled-data (MSD) systems with time delays are studied. The background of our study is the increasing demands for fault detection in complex, distributed process control systems, where the plant, controllers, sensors and actuators are networked by standardized bus systems. The basic idea behand the problem solutions is to derive the parity relations of the MSD system with time delays by taking into account the multirate nature and the multiple time delays. Having introduced two operators, the fault detection problem can be formulated as an optimization problem in the framework of the well-known parity space method. Application and advantages of the approaches proposed in this paper are finally illustrated by simulation examples and by comparison with other indirect design approaches.

Extension of the parity-space method to fault diagnosis of bilinear systems

In this paper, the parity-space method for linear fault diagnosis is extended to bilinear systems. By including bilinear terms into the system matrix, a linear time-varying model with known time-varying feature is obtained. The parity-space method can then be applied to generate residuals in each sampling interval. A recursive algorithm is developed for calculating the matrices in the parity equation, so that the computing time is greatly reduced. Fault isolation is achieved at the residual stage using one parity check instead looking at a bank of parity equations. Component, actuator and sensor faults are isolated by applying an elementary transformation and the singular value decomposition techniques. A numerical example is used to demonstrate the effectiveness of the method.

Extension of the parity-space method to fault diagnosis of bilinear systems

In this paper, the parity-space method for linear fault diagnosis is extended to bilinear systems. By including bilinear terms into the system matrix, a linear time-varying model with known time-varying feature is obtained. The parity-space method can then be applied to generate residuals in each sampling interval. A recursive algorithm is developed for calculating the matrices in the parity equation, so that the computing time is greatly reduced. Fault isolation is achieved at the residual stage using one parity check instead looking at a bank of parity equations. Component, actuator and sensor faults are isolated by applying an elementary transformation and the singular value decomposition techniques. A numerical example is used to demonstrate the effectiveness of the method.

Model Based Fault Detection with Parity Space Relations for a Direct Drive Valve

In modern smart actuator concepts the actuator control electronic includes built-in test procedures for fault detection and isolation (FDI). To improve the test depth this paper presents analytic methods which allow a precise and comprehensive fault detection. The application discusses parity space methods for a modern direct drive valve (DDV) which comprises strong non-linearities. The feasibility and onboard application are also exposed.

Diagnosing simulated faults for an industrial furnace based on bilinear model

Fault diagnosis for a gas-fired industrial furnace is described. First, an input-output bilinear model is estimated using real data. The model is shown to be much more precise than a linear model. Then, two actuator and two sensor faults are simulated on the bilinear model, and are detected using a bilinear fault detection observer. Since the sensor faults cannot be isolated from one another using the observer, the linear parity space method is extended to bilinear systems and applied to the furnace. The effectiveness of the extended parity space method in both detection and isolation of the faults in the furnace is demonstrated.

Some Enhancements to the Parity Space Method for FDI

The problem of robust residual generation in linear dynamic systems for fault detection and isolation is addressed in this paper. Some extensions to the traditional parity space methods are proposed. The properties of the Hankel matrices used in the classical optimization problem are studied in order to generate a residual more sensitive to faults characterized by a non zero mean. Then, a more general method based on the same principle as the parity space approach is proposed to avoid the problems due to the parity space definition. Combining these two improvements allow to decrease the detection delay and to distinguish easier the faults from the disturbances. A simulation example illustrates the proposed approach and the results are compared with the traditional parity space method.

An Extended Parity Space Method and a Case Study on an Industrial Furnace

The linear parity space method is extended to bilinear systems in this paper. A recursive algorithm for calculating the matrices in the parity equation is developed. Fault isolation is achieved by transforming residual matrix such that the effects of different faults are decoupled. The proposed method is applied to an estimated model of a gas-fired high temperature furnace. Two component faults and two sensor faults are simulated and tested. The simulation results indicate the effectiveness of the method.

Quantitative Approaches for Fault Diagnosis Based on Bilinear Systems

fault detection and isolation (FDI) of bilinear systems is considered. Two main quantitative methods are outlined. A bilinear fault detection observer is proposed which focuses on the problem of decoupling the unknown inputs from the residuals. Secondly, a parity space method is developed for bilinear systems. Robust issues are addressed throughout the paper. Related methods are briefly discussed and two applications are considered.

Fault detection and isolation by a continuous parity space method

The well-known parity space method for fault detection and isolation is generalized to a continuous-time case. As in the discrete-time case, it is shown that the parity space equations can be implemented using a number of time delays, and are equivalent to a continuous deadbeat observer. Algorithms for isolation of sensor and actuator faults are derived and illustrated by a numerical example.

Some Extension to the Generalized Parity Space Method for FDI

This paper describes some new development in the method of failure detection and isolation (FDI) in uncertain systems. The new development is based on the generalized parity space method, however in a closed-loop setting. Residual signals generated by parity checks under this setting are shown to have enhanced robustness with respect to modeling errors and disturbances. An iterative algorithm is developed for optimizing the design of residual signal generators.

Improvement of Flexibility and Reliability of Automobile Actuators by Model-Based Algorithms

Automobile actuators have to work very reliable. In extension to redundancy schemes the use of model-based fault detection methods is advantageous for the early detection of incipient faults in the process or in the signals. Two different approaches, the parity-space method and the parameter estimation approach, are selected for the investigations for the example of an automobile actuator driven by a d.c. motor. After a brief description of the theoretical basis the experimental application follows to several faults.

Signal validation techniques and power plant applications

Computerized signal validation is a software technique for integrating information from redundant and from functionally diverse sensors to provide highly reliable information to operating crews and to auto matic controllers. High reliability results from greater and more comprehensive use of existing sensor redundancy, and from inherent fault detection and isolation (FDI) of faulty sensors. Operating crews at power plants have always performed some measure of manual signal validation. With the current thrust to introduce digital computers to support plant monitoring and operator aid functions# signal validation has become generally recognized as a key element in the interface between plant sensors and algorithms using sensor data. For any computerized system to be robust and to yield a false alarm rate that is credibly low, high information reliability must be assured at the front end. Progress of research in computerized signal validation techniques has benefited the U.S. utilities by improving the reliability of safety parameter display systems (SPDS) implemented in the nuclear power plants. Conventional signal validation techniques have been combined with more robust parity space and analytic redundancy methods to provide reliable and real-time SPDS monitoring information to PWR and BWR operators. The technical details of the methods, application to PWR and BWR SPDS, and the extension of the techniques to plant control functions are reviewed in this paper.