Fault Detection And Isolation Approach Research Articles

Fault diagnosticability for an aerobic batch wastewater treatment process

This work is concerned with the evaluation of detectability and isolability for analytical- and signal-based fault detection and isolation (FDI). Based on a case study, an aerobic sequencing batch reactor (SBR) for wastewater treatment (WWT), and its known analytical process model, it is first shown that analytical FDI approaches are not feasible through application of structural analysis. An FDI approach based on the respiration signal is then demonstrated to be viable, using information from a novel application of sensitivity theory. These findings are validated with an FDI procedure that uses extracted signal characteristics and classification to successfully diagnose deviations of some model parameters.

Modelling and robust FDI of steam generator using uncertain bond graph model

In this paper, a bond graph model based approach for robust FDI (Fault Detection and Isolation) in the presence of parameter uncertainties is presented. Due to the energetic and structural properties of the bond graph, the whole of nonlinear model, structural analysis, residual with adaptive thresholds generations, and sensitivity analysis of the residuals can be synthesized using this same tool. This method is applied online on an industrial steam generator. Experimental results are given to support the theoretical development.

Robust Fault Diagnosis of Energetic System with Parameter Uncertainties

In this paper, a bond graph model based approach for robust FDI (Fault Detection and Isolation) in presence of parameter uncertainties is presented. Due to the energetic and multi physical properties of the Bond Graph, the whole of nonlinear model, structural analysis, residual with adaptive thresholds generations, and residual sensitivity analysis, can be synthesized using only one tool. This method is applied online for industrial steam generator. Experimental results are given to support the theoretical development.

Multiple fault disambiguations through parameter estimation: a bond graph model-based approach

To ensure safe operation of industrial processes, automated Fault Detection and Isolation (FDI) procedures are implemented in their supervision platforms. In the safety-critical and environmentally hazardous processes, it is impossible to introduce all kinds of faults and then to derive their consequences. Qualitative determination of consequences of different faults can be misleading in complex dynamical systems. Therefore, simulation of a prototype model turns out to be a practical and an economical solution for the development of a complete Knowledge-Base (KB). Consequently, the intelligence acquired by KB from the simulated models is used to fine-tune the Decision Support System (DSS) such that false alarms and misdetections are minimised. A method for model-based multiple FDI by using Analytical Redundancy Relations (ARRs) and parameter estimation is developed in this paper. Parameter estimation is an essential prerequisite for fault accommodation through system reconfiguration or Fault Tolerant Control (FTC). Bond graph modelling is used to describe the process models and then the model is used to derive the ARRs and fault candidates. Parameter values corresponding to the fault-subspace are estimated by minimising a function of the ARRs. Modelling uncertainties arising out of parameter estimation and sensor noise are taken care by using a passive approach for robust FDI. The developed technique is applied to monitor an open-loop non-linear thermo-fluid process.

Sensitivity bond graph approach to multiple fault isolation through parameter estimation

In model-based quantitative multiple fault detection and isolation (FDI), fault disambiguation is based on parameter estimation. In this paper, the fault hypothesis is generated by evaluating a set of analytical redundancy relations (ARRs) and parameter values corresponding to the unstructured part of the fault subspace are estimated by minimizing a function of the ARRs. Process and measurement uncertainties are handled by using a passive approach for robust FDI. Bond graph modelling is used to describe process models and to derive the ARRs. The bond graph model of the process is differentially causalled and it is then converted into a diagnostic bond graph form. The diagnostic bond graph is further converted into its corresponding sensitivity bond graph form, which gives the residual sensitivity to parametric changes. The developed algorithm provides quicker fault isolation because only a few parameters are estimated and it does not need several model simulations, thereby making it suitable for real-time process supervision.

Robust Dynamic Sensor Fault Detection and Isolation of Helical Coil Steam Generator Systems Using a Subspace Identification Technique

A design approach to sensor fault detection and isolation (FDI) of helical coil steam generator (HCSG) systems of the International Reactor Innovation Secure (IRIS) reactor is presented. In the design phase, a physical model is first developed to provide a realistic simulation and generate data characterizing the system dynamics. A subspace identification technique is then used to extract a low-order linear state-space model from the data. Finally, a robust dynamic parity space approach is utilized to design residual generators for FDI. This design approach is able to achieve fault isolation following a predetermined logic without the need to use data during fault conditions, which is an unrealistic assumption of many FDI approaches studied for nuclear power plants. The results of the HCSG application show that the approach is robust to not only measurement and process noises but also operation condition changes and has the capability of correct FDI during reactor power transients and during the propagation of sensor faults in a control loop.

FAULT TOLERANT CONTROL USING FUZZY MPC

This paper describes the application of fault-tolerant control (FTC) using fuzzy model-based predictive control. The proposed FTC approach is based on two steps, fault detection and isolation (FDI) and fault accommodation. This paper uses a model-based FDI approach using fuzzy modeling. Further, fault isolation is performed by using a fuzzy decision making approach. Fault accommodation uses a multiple model approach, where one fuzzy model is identified for each fault. This paper proposes the use of FDI information to select the model to be used in the controller. The selected model is then used in a model predictive control (MPC) scheme. The FTC approach was tested in a container gantry crane simulator. Two faults were simulated, and the respective fuzzy models were identified. The FTC scheme proposed in this paper was able to accommodate the simulated faults.

FDI approach to the DAMADICS benchmark problem based on qualitative reasoning coupled with fuzzy neural networks

A computer-assisted fault detection and isolation (FDI) based on a fuzzy qualitative simulation algorithm used for fault detection purposes, coupled with a hierarchical structure of fuzzy neural networks used to perform the fault isolation task, is presented. The DAMADICS benchmark actuator system has been used as test bed of the current FDI system. Single abrupt and incipient faults, as well as multiple simultaneous faults have been considered to test the overall system robustness. The results obtained prove the efficiency of the proposed FDI system.

Combining FDI and AI Approaches Within Causal-Model-Based Diagnosis

This paper presents a model-based diagnostic method designed in the context of process supervision. It has been inspired by both artificial intelligence and control theory. AI contributes tools for qualitative modeling, including causal modeling, whose aim is to split a complex process into elementary submodels. Control theory, within the framework of fault detection and isolation (FDI), provides numerical models for generating and testing residuals, and for taking into account inaccuracies in the model, unknown disturbances and noise. Consistency-based reasoning provides a logical foundation for diagnostic reasoning and clarifies fundamental assumptions, such as single fault and exoneration. The diagnostic method presented in the paper benefits from the advantages of all these approaches. Causal modeling enables the method to focus on sufficient relations for fault isolation, which avoids combinatorial explosion. Moreover, it allows the model to be modified easily without changing any aspect of the diagnostic algorithm. The numerical submodels that are used to detect inconsistency benefit from the precise quantitative analysis of the FDI approach. The FDI models are studied in order to link this method with DX component-oriented reasoning. The recursive on-line use of this algorithm is explained and the concept of local exoneration is introduced.

Isolation of parametric faults in continuous-time multivariable systems: a sampled data-based approach

A novel approach is proposed towards on-line and real-time detection and isolation of parametric faults in a multivariable linear continuous-time (CT) system. The problem of fault detection and isolation (FDI) is formulated in terms of a CT state space model. Since parameters in a CT model usually have simple relationships with physical parameters of the system, isolating parametric faults in the CT model can lead to the isolation of undesired changes in the physical parameters. Isolating parametric faults is very challenging, because even in a linear time-invariant system, the fault model can be time-varying and random. To obtain a constant fault model, many existing parametric FDI schemes have to make unrealistical assumptions. Our proposed FDI approach can generate an optimal primary residual vector (PRV), in which the fault model is constant without making any assumptions. To isolate faults, the PRV is transformed into a set of structured residual vectors (SRVs), where one SRV is made insensitive to a specified subset of faults, but most sensitive to other faults. The proposed approach is successfully applied to detection and isolation of undesired changes in the physical parameters of a simulated continuously stirred tank process.

Modelling for Fault Detection and Isolation versus Modelling for Control

The main purpose of this paper is to emphasize the particularities of models needed for model-based fault detection and isolation (FDI) in contrast to the models used for control. Of special interest is the question of complexity of the model, which is of great importance for the practical implementation. This, of course, depends basically on the given situation such as the kind of plant, the measurements, the kind and number of faults to be detected and the demands for fault isolation and robustness. However, the paper shows that diagnostic models, in contrast to the wide-spread opinion that those have always to be more complex than the functional models for control, may be even less complex, because they are restricted to only those parts of the system in which the faults occur. The issue of model complexity is discussed in terms of different model-based FDI approaches analytical, knowledge-based and data-based. The ideas are illustrated in a case study, where several types of model-based FDI techniques are compared with the same plant, the amira three tank system.

Modelling for fault detection and isolation versus modelling for control

The goal of this paper is to emphasize both the particularities of models needed for model-based fault detection and isolation (FDI) and the differences with respect to the models used in control. Of special interest is the question of complexity. This depends basically on the given situation such as the kind of plant, the kind and number of faults to be detected, the demands for fault isolation, robustness and the measurements available. However, in contrast to the wide-spread opinion that models for FDI have always to be more complex than those for control, the paper shows that diagnostic models for controllable and observable plants comprise only a partial description of the input/output model and are therefore less complex than those for control. This issue is discussed in terms of different model-based FDI approaches — analytical, data- and knowledge-based. As for the analytical approaches the necessary order of the diagnostic model is that of the transfer operator from the fault vector to the system output.

Detection and Isolation of Sensor Faults for a Heat Exchanger Based on Local Linear Fuzzy Models

The development of a reliable fault detection and isolation (FDI) scheme for nonlinear processes is often laborious and difficult to achieve due to the complexity of the system. Neural networks and fuzzy models, able to approximate nonlinear dynamic functions offer a powerful tool to cope with this problem. In this paper, a multi-model approach for FDI of sensor faults on a highly nonlinear real world processes is introduced. The approach is based local linear fuzzy models and provides residuals which are structured in a way that detection of six and isolation of five different sensor faults over all ranges of operation becomes possible. The approach enables on-line supervision of the process. Furthermore, a sensitivity analysis of the residuals to different faults can be made, based on the parameters of the fuzzy models.