Parity Space Approach Research Articles

Fault Detection for Nonlinear Processes Based on Local Linear Fuzzy Models in Parallel and Series-Parallel Mode

In this contribution, a new approach for model-based fault detection and isolation (FDI) of sensor faults for non linear processes is presented. A local linear fuzzy model of the process is used for the generation of structured residual equations similar to the parity space approach. The model is run in parallel and series-parallel to the process which leads to residuals with different sensitivities. The practical applicability is illustrated on an industrial scale thermal plant. Here, five different faults can be detected and isolated continuously over all ranges of operation.

Fuzzy associative memories for instrument fault detection

A fuzzy logic instrument fault detection scheme is developed for systems having two or three redundant sensors. In the fuzzy logic approach the deviation between each signal pairing is computed and classified into three fuzzy sets. A rule base is created allowing the human perception of the situation to be represented mathematically. Fuzzy associative memories are then applied. Finally, a defuzzification scheme is used to find the centroid location, and hence the signal status. Real-time analyses are carried out to evaluate the instantaneous signal status as well as the long-term results for the sensor set. Instantaneous signal validation results are used to compute a best estimate for the measured state variable. The long-term sensor validation method uses a frequency fuzzy variable to determine the signal condition over a specific period. To corroborate the methodology synthetic data representing various anomalies are analyzed with both the fuzzy logic technique and the parity space approach.

A Parity Space Approach to Robust Fault Detection in Generalized State Space Systems

Many sophisticated approaches have been proposed for fault detection in dynamic systems using standard model representations. In this paper, redundancy relations for fault detection in systems represented by a generalized state space description are presented. Conditions for the existence of a redundancy relation and the simplest form this can take for the generalized system are obtained. The design of the redundancy relation in the presence of model uncertainties is also considered and conditions for the existence of a perfectly robust solution obtained. For cases where this does not exist a procedure is defined for calculating an optimally robust solution. A simulation of a three-shaft gas turbine is used to demonstrate the potential of the method.

Fault Detection of Flowrate and Temperature Control Loops using Estimated Parity Equations

Many systems with nonlinear behaviour are difficult to model. Consequently model based approaches to fault detection can not be applied easily. Additional problems are introduced in closed loops by the controversity of controller design and fault detection. For a certain class of processes with mainly constant input signals a combination of parameter estimation techniques and the parity space approach allows on-line fault detection with small signal models. Only few knowledge of the process is required: An idea about the dynamic model order and the static nonlinear characteristic curve. Using the proposed method fault detection is performed for temperature control loops in a thermic plant.

Supervision of Lateral Vehicle Motion Using a Discrete Parity Space Approach

An approach for supervision of vehicle dynamics is presented which may be used for intelligent vehicle control and state monitoring. In particular, the task is to determine automatically an altered road surface for vehicles (may be due to weather conditions) with possibly lower adhesion between tires and road. Fault detection methods like the parity space approach can be used to detect changes in technical processes. These changes indicate different process behaviour in a new operation range. This may be of special importance when the new behaviour is more dangerous for the driver and the passengers of a car. In a further step, suitable classification algorithms were used to detect the actual driving state by processing the residual output.

On-line fault detection in uncertain nonlinear systems using diagnostic observers: a survey

The paper discusses the principles of model-based fault detection and isolation (FDI) in nonlinear and time-varying uncertain dynamic systems. Such systems are typical for such complex plants as, for example, in the chemical process industries or in advanced transportation technology. For a model-based fault diagnosis in such situations, robust or even adaptive strategies are needed. In this paper the theory of robust linear observer-based residual generation for FDI is reviewed from a general point of view. The structural equivalence between the parity space approach and observer-based approach is shown in a new simple graphical way by showing that the observer-based FDI concept can easily be transformed into an equivalent extended parity space configuration, without claiming, however, equivalence of the underlying design techniques. The unknown input observer approach known as a most powerful and comprehensive framework for robust residual generation for FDI in uncertain linear systems is extended to cl...

Detection of Additive and Multiplicative Faults - Parity Space vs. Parameter Estimation

Faults which appear in technical processes can often be described as additive or multiplicative faults with respect to the process model. To detect and identify these kinds of faults two model based methods, the parity space approach with adaptive thresholds and the parameter estimation, are used.Starting with theoretical investigations on a simple example both schemes are applied to a d.c.motor. The results and the different properties are compared. Finally a combination of both methods is proposed to achieve optimal fault detection.

Sensor Fault Detection and Isolation on a Pilot Heat Exchanger

A model based fault detection and isolation (FDI) system for sensor failures is designed on a pilot heat exchanger. Using the parity space approach, applied to ARX models of the plant, and using an observer based approach, we demonstrate three points - the feasibility of FDI systems based on black box models whose parameters have been identified using experimental data, - the way of using a bank of FDI systems to accommodate set point changes, - the dependence of the tuning of the FDI system on the nature of the fault to be detected.

Enhancement of robustness in observer-based fault detection†

A prerequisite for the feasibility of the technique of observer-based fault detection and isolation (FDI) in dynamic systems is a satisfactory robustness with respect to modelling uncertainties. This paper surveys the most relevant methods to increase the robustness in both the stage of residual generation and residual evaluation. Among these methods are the generalized observer scheme, the robust parity space check, the unknown input and H ∞ observer scheme, the decorrelation filter, and the concept of adaptive threshold selection. It is pointed out that the unknown input observer concept, which provides perfect decoupling from the modelling errors or its optimal approximation with the aid of H ∞ techniques, constitutes a general framework of robust residual generation that generalizes and unifies numerous other approaches, among them the parity space and detection filter approach. It is further shown that this FDI method can even be applied to a certain class of nonlinear systems.

Review of parity space approaches to fault diagnosis for aerospace systems

This paper provides a tutorial review of the state of the art in parity space fault diagnosis approaches with particular emphasis on aerospace systems. The basic concepts and definitions are given and a consistent framework is presented to draw together the important links amongst the known methods for fault diagnosis. Residual generation in the parity space has been recognized as a core element in this framework. The robustness and isolation problems are the main focus of the paper. Recent research topics on robust fault diagnosis are outlined, and new ideas as to how the parity space approach can be used to deal with robustness are discussed.

On residual generation by observer and parity space approaches

A unifying framework for observer design by eigenstructure assignment is described. A new technique for structured residual generation, based on this framework, is proposed. One of the main features of this technique is that the equivalence between residual generation by means of observers or parity space devices are made conspicuous. More precisely, from a computational point of view, the equations to solve in both cases are shown to be similar, and for implementation, both devices are shown to be identical. A necessary and sufficient condition for the existence of structured residuals is given. Finally the sensitivity of structured residuals to nondecoupled failures is studied and a procedure for maximizing this sensitivity is described. >

Generation of Optimal Structured Residuals in the Parity Space

This paper proposes an approach for the improvement of the performances of Failure Detection and Isolation (FDI) systemsStructural specifications of the expected results lead to optimize the robustness of the system with respect to the modelling uncertainties and unknown inputs. Most of the solutions of this optimization problem have been developed through the transformation of an observer - based or a parity space approachWe show in this paper how the parity space approach allows the direct generation of residuals which respond to the given specifications.

Principles of Model-Based Fault Detection

The paper outlines the principles of fault detection and isolation (FDI) in dynamic systems using a mathematical model of the system. Implemented on a digital computer, these model-based algorithms can efficiently be applied to signal validation, on-line detection of abrupt faults and early fault diagnosis in a long-term system supervision. The basic concept of the model-based approach to fault detection and isolation is described, and the different methods of residual generation developed in the past two decades are reviewed. Among the methods considered are the parameter identification approach, the different observer-based concepts, the parity space approach and the knowledge-based strategy. It is also shown how structured residuals for the localization of the faults can be generated with the aid of observer schemes. As an issue of great practical relevance the robustness with respect to modeling errors is taken into consideration. Here emphasis is placed upon the unknown input observer approach and its application to instrument, actuator and component fault detection.

Multivalued Logic Voting Scheme for Residual Evaluation in Failure Detection and Isolation Systems

This paper deals with Failure Detection and Isolation procedures using the generalized Parity Space Approach. The decision scheme uses the structural properties of the residuals and leads to the analysis of fault events signatures. In a classical approach, these signatures are binary words. We propose a multivalued voting scheme, which allows to follow some specifications connected with false alarms and miss-isolation. The performances of the proposed approach are compared with these of the classical one using two examples.

Principles of model-based fault detection

The paper outlines the principles of fault detection and isolation (PDI) in dynamic systems using a mathematical model of the system. Implemented on a digital computer, these model-based algorithms can efficiently be applied to signal validation, on-line detection of abrupt faults and early fault diagnosis in a long-term system supervision. The basic concept of the model-based approach to fault detection and isolation is described, and the different methods of residual generation developed in the past two decades are reviewed. Among the methods considered are the parameter identification approach, the different observer-based concepts, the parity space approach and the knowledge-based strategy. It is also shown how structured residuals for the localization of the faults can be generated with the aid of observer schemes. As an issue of great practical relevance the robustness with respect to modeling errors is taken into consideration. Here emphasis is placed upon the unknown input observer approach and its application to instrument, actuator and component fault detection.

Multivalued logic voting scheme for residual evaluation in failure detection and isolation systems

This paper deals with Failure Detection and Isolation procedures using the generalized Parity Space Approach. The decision scheme uses the structural properties of the residuals and leads to the analysis of fault events signatures. In a classical approach, these signatures are binary words. We propose a multivalued voting scheme, which allows to follow some specifications connected with false alarms and miss-isolation. The performances of the proposed approach are compared with these of the classical one using two examples.

A Review of Parity Space Approaches to Fault Diagnosis

Abstract This paper reviews the state of the art in fault detection and isolation for dynamic systems, based on the parity space concept. Some important definitions are provided and tutorial examples are given to illustrate the theory. The main aim has been to draw together the important links between parity space approaches; in particular the important links between open-loop and closed-loop strategies. The parity space is used as a new re-statement to unify residual generation methods. The robustness and isolation problems in fault diagnosis is mainly addressed in the paper. Moreover, an important definition for robustness is made and the recent works on robust fault diagnosis methods are described. Some new design methods are given to illustrate the potential of robust residual generation using closed-loop parity space ideas.

A Model-based Fault Detection Concept for Mechanical Systems

Abstract This paper discusses an approach to fault detection and isolation in linear mechanical multibody systems. This is done by combining parameter estimation and state estimation methods. First, the so-called differential identification is considered which is based on a linearization of the links between discrete-time and continuous-time system descriptions using the special properties of mechanical canonical forms. It allows direct estimation of changes in the physical plant parameters and therefore it is well-suited for on-line isolation of incipient faults. Parameter estimation methods imply some difficulties if time-varying systems with disturbed measurement data are concerned. To improve early and safe fault detection they should be assisted by complementary methods like the parity space approach which is strongly related to a dead-beat observer design. This parity space concept results in a residual that serves as a fault indicator which is maximally sensitive to the faults that have to be detected while remaining insensitive to disturbances in the measurement data. Simulation results show the successful implementation.

Enhancement of Robustness in Observer-Based Fault Detection

A prerequisite for the feasibility of the technique of observer-based fault detection and isolation (FDI) in dynamic systems is a satisfactory robustness with respect to modeling uncertainties. This paper surveys the most relevant methods to increase the robustness in both the stage of residual generation and residual evaluation. Among them are the generalized observer scheme, the robust parity space check, the unknown input and observer scheme, the decorrelation filter, and the concept of adaptive threshold selection. It is pointed out that the unknown input and H∞ observer concept that provides perfect decoupling from the modeling errors, or its optimal approximation constitutes a general framework of robust residual generation that generalizes and unifies numerous other approaches, among them the parity space and detection filter approach. It is further shown that this FDI method can even be applied to a certain class of nonlinear systems.

Detection and location of gross errors in instruments using parity space technique

Before improving the control of a process, we must make sure of information coherence obtained from instrument lines or sensors. In fact, this information can be corrupted by errors and can also deviate from the optimum functioning range. The operator must take precautions not to be outside of this range. The detection of errors is used to point out the deviations. The detection, the location, the different error characterizations and the estimation of true values are steps in the data reconciliation problem. Process measurements are subject to two types of errors: (1) random errors generally taken to be independent and gaussian with zero mean, and (2) gross errors which are caused by non-random events such as malfunctioning sensors, instrument biases and inexact process models. Various methods for detection and location of gross errors in process data have been proposed in recent years including the parity space approach, the standardized least-squares residuals approach and the standardized imbalance residuals approach. In this paper these methods are applied to instrument and analytic redundances; the performances of parity space approach are compared to the two approached mentioned above by varying different parameters. We restrict our study to the presence of one, two and three gross errors and we consider that all streams are measured. We also demonstrate the equivalence between the parity space approach and the use of normalized residuals approach. Once the location has been made and the measurements containing gross errors have been deleted, we proceed with data reconciliation.