Teresa Escobet

Diagnosability Analysis Based on Component-Supported Analytical Redundancy Relations

It is commonly accepted that the requirements for maintenance and diagnosis should be considered at the earliest stages of design. For this reason, methods for analyzing the diagnosability of a system and determining which sensors are needed to achieve the desired degree of diagnosability are highly valued. This paper clarifies the different diagnosability properties of a system and proposes a model-based method for: 1) assessing the level of discriminability of a system, i.e., given a set of sensors, the number of faults that can be discriminated, and its degree of diagnosability, i.e., the discriminability level related to the total number of anticipated faults; and 2) characterizing and determining the minimal additional sensors that guarantee a specified degree of diagnosability. The method takes advantage of the concept of component-supported analytical redundancy relation, which considers recent results crossing over the fault detection and isolation and diagnosis communities. It uses a model of the system to analyze in an exhaustive manner the analytical redundancies associated with the availability of sensors and performs from that a full diagnosability assessment. The method is applied to an industrial smart actuator that was used as a benchmark in the Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems European project

Passive Robust Fault Detection of Dynamic Processes Using Interval Models

Model-based fault detection relies on the use of a model to check the consistency between the predicted and the measured (or observed) behavior of a system. However, there is always some mismatch between the modeled and the real process behavior. Then, any model-based fault detection algorithm should be robust against modeling errors. One possible approach to take into account modeling uncertainty is to include all the uncertainty in system parameters using an interval model that allows generating an adaptive threshold. In this paper, the use of interval models in robust fault detection considering three schemes (simulation, prediction, or observation) is presented and discussed. The main contribution is to present a comparative study that allows identifying the benefits and drawbacks of using each scheme. This study will provide a guideline for the use of the proposed fault detection schemes in real applications. Finally, an intelligent servoactuator, proposed as a benchmark in the context of European Research Training Network DAMADICS, is used to illustrate the application and the comparative study of these interval-based fault detection schemes.

PASSIVE ROBUST FAULT DETECTION APPROACHES USING INTERVAL MODELS

Abstract The problem of robustness in fault detection has been treated basically using two kinds of approaches: actives and passives. Most of the literature in robust fault detection is focused on the problem of active approach based on decoupling the effects of the uncertainty from the effects of the faults on the residual. On the other hand, the passive approach is based of propagating the effect of the uncertainty on the residuals and then using adaptive thresholds. In this paper, the passive approach based on adaptive thresholds produced using a model with uncertain parameters bounded in intervals, also known as an “ interval model ”, will be presented in the context of parity equations and observers methodologies, deriving their corresponding interval versions. Finally, an example based on an industrial actuator used as a FDI benchmark in the European project DAMADICS will be used for testing and comparing the proposed approaches.

Fault Diagnosis Using a Timed Discrete-Event Approach Based on Interval Observers: Application to Sewer Networks

This paper proposes a fault diagnosis method using a timed discrete-event approach based on interval observers that improves the integration of fault detection and isolation tasks. The interface between fault detection and fault isolation considers the activation degree and the occurrence time instant of the diagnostic signals using a combination of several theoretical fault signature matrices that store the knowledge of the relationship between diagnostic signals and faults. The fault isolation module is implemented using a timed discrete-event approach that recognizes the occurrence of a fault by identifying a unique sequence of observable events (fault signals). The states and transitions that characterize such a system can directly be inferred from the relation between fault signals and faults. The proposed fault diagnosis approach has been motivated by the problem of detecting and isolating faults of the Barcelonas urban sewer system limnimeters (level meter sensors). The results obtained in this case study illustrate the benefits of using the proposed approach in comparison with the standard fault detection and isolation approach.

Minimal Structurally Overdetermined Sets for Residual Generation: A Comparison of Alternative Approaches

The issue of residual generation using structural analysis has been studied by several authors. Structural analysis does not permit to generate the analytical expressions of residuals since the model of the system is abstracted by its structure. However, it determines the set of constraints from which residuals can be generated and it provides the computation sequence to be used. This paper presents and compares four recently proposed algorithms that solve this problem.

Diagnosability Analysis Based on Component Supported Analytical Redundancy Relations

Abstract It is commonly accepted that the requirements for maintenance and diagnosis should be considered at the earliest stages of design. For this reason, methods for analysing the diagnosability of a system and determining which instrumentation is needed to achieve the desired level of diagnosability, are highly valued. This paper enhances the model based method proposed in Trave-Massuyes, et al. (2001) based on the concept of component supported analytical redundancy relations, which considers recent results crossing over the FDI and DX communities.

Optimal sensor placement for model-based fault detection and isolation

The problem of optimal sensor placement for FDI consists in determining the set of sensors that minimizes a pre-defined cost function satisfying at the same time a pre- established set of FDI specifications for a given set of faults. The main contribution of this paper is to propose an algorithm for model-based FDI sensor placement based on formulating a mixed integer optimization problem. FDI specifications are translated into constraints of the optimization problem considering that the whole set of ARRs has been generated, under the assumption that all candidate sensors are installed. To show the effectiveness of this approach, an application based on a two-tanks system is proposed.

Fault Diagnosis of an Advanced Wind Turbine Benchmark Using Interval-Based ARRs and Observers

This paper proposes a model-based fault diagnosis (FD) approach for wind turbines and its application to a realistic wind turbine FD benchmark. The proposed FD approach combines the use of analytical redundancy relations (ARRs) and interval observers. Interval observers consider an unknown but bounded description of the model parametric uncertainty and noise using the the so-called set-membership approach. This approach leads to formulate the fault detection test by means of checking if the measurements fall inside the estimated output interval, obtained from the mathematical model of the wind turbine and noise/parameter uncertainty bounds. Fault isolation is based on considering a set of ARRs obtained from the structural analysis of the wind turbine model and a fault signature matrix that considers the relation of ARRs and faults. The proposed FD approach has been validated on a 5-MW wind turbine using the National Renewable Energy Laboratory FAST simulator. The obtained results are presented and compared with that of other approaches proposed in the literature.

Efficient optimal sensor placement for model-based FDI using an incremental algorithm

The problem of optimal sensor placement for FDI consists in determining the set of sensors that minimizes a pre-defined cost function satisfying at the same time a pre-established set of FDI specifications for a given set of faults. Existing approaches are mainly based on formulating an optimization problem once the sets of all possible ARRs has been generated, considering all possible candidate sensors installed. However, the associated computational complexity is exponential with the number of possible sensors. The main goal of this paper is to propose an incremental algorithm for FDI sensor placement that tries to avoid the computational burden. To show the effectiveness of this approach, an application based on a fuel-cell system is proposed.

A class of uncertain linear interval models for which a set based robust simulation can be reduced to few pointwise simulations

Set based robust simulation of uncertain models allows us to analyze robustness properties of a system from a time domain perspective. In this work, a well known result from the study of the wrapping effect in the field of validated solution of ODEs is used to characterize a class of interval uncertain models with nominal linear behavior for which a robust simulation can be reduced to two conventional pointwise simulations. As a side effect, this work raises the relevance of the study of the wrapping effect for the time domain study of robustness properties of models.