Louise Travé-Massuyès

Conflicts versus analytical redundancy relations: a comparative analysis of the model based diagnosis approach from the artificial intelligence and automatic control perspectives

Two distinct and parallel research communities have been working along the lines of the model-based diagnosis approach: the fault detection and isolation (FDI) community and the diagnostic (DX) community that have evolved in the fields of automatic control and artificial intelligence, respectively. This paper clarifies and links the concepts and assumptions that underlie the FDI analytical redundancy approach and the DX consistency-based logical approach. A formal framework is proposed in order to compare the two approaches and the theoretical proof of their equivalence together with the necessary and sufficient conditions is provided.

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

Gas-turbine condition monitoring using qualitative model-based diagnosis

Gas turbines are critical to the operation of most industrial plants, and their associated maintenance costs can be extremely high. To reduce those costs and increase the availability of their gas turbines, plant operators have for many years relied on routine preventative maintenance-routinely checking and solving small problems before they grow into major ones. Recently, however, the power industry has moved sharply toward condition-based maintenance and monitoring. In this approach, intelligent computerized systems monitor gas turbines to establish maintenance needs based on the turbines condition rather than on a fixed number of operating hours. By integrating several AI technologies-including qualitative model-based reasoning-the Tiger system significantly cuts costs and improves performance by using control-system information to perform condition monitoring for gas-turbine engines.

Telephone Network Traffic Overloading Diagnosis and Evolutionary Computation Techniques

Traffic supervision in telephone networks is a task which needs to determine streams responsible for call losses in a network by comparing their traffic values to nominal values. However, stream traffic values are not measured by the on-line data acquisition system and, hence, have to be computed. We perform this computation by inverting an approximate knowledge based model of stream propagation in circuit-switched networks. This inversion is computed thanks to three evolutionary computation techniques (multiple restart hill-climbing, population-based incremental learning and genetic algorithms) for which both a binary version and a real variant have been experimented with several fitness measures. The final results first point out how the fitness measure choice can impact on their quality. They also show that, in this case, real variants of the algorithms give significantly better results than binary ones.

Hybrid Systems Diagnosis by Coupling Continuous and Discrete Event Techniques

Abstract This paper deals with the problem of diagnosing systems that exhibit both continuous and discrete event dynamics. The proposed approach combines techniques from both continuous and discrete event diagnosis fields. On the on hand, an extension of the parity space approach is used to associate signatures to every operational mode of the system. On the other hand, signature switches arising from the transition from one mode to another are abstracted in the form of a set of events that capture the continuous dynamics. These events are merged into the original discrete dynamic model of the system, allowing us to apply the well-known discrete-event-systems diagnoser approach. This is illustrated on an example that shows the diagnosability improvement of the hybrid approach.

AI and Automatic Control Approaches of Model-Based Diagnosis: Links and Underlying Hypotheses

Abstract Two distinct communities have been working along the Model-Based Diagnosis approach. This paper clarifies and links the concepts and hypotheses that underly the FDI analytical redundancy approach and the DX consistency-based logical approach. This work results from the collaboration existing within the French IMALAIA group supported by the French National Programs on Automatic Control GDR Automatique and on Artificial Intelligence GDR 13.

Mathematical foundations of qualitative reasoning

We examine different formalisms for modeling qualitatively physical systems and their associated inferential processes that allow us to derive qualitative predictions from the models. We highlight the mathematical aspects of these processes along with their potential and limitations. The article then bridges to quantitative modeling, highlighting the benefits of qualitative reasoning-based approaches in the framework of system identification, and discusses open research issues.

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.

Diagnosis of Complex Systems: Bridging the Methodologies of the FDI and DX Communities

Two distinct and parallel research communities have been working along the lines of the ModelBased Diagnosis approach: the FDI community and the DX community that have evolved in the fields of Automatic Control and Artificial Intelligence, respectively. This paper, which details and extends (Cordier et al., 2000a, 2000b), clarifies and links the concepts and assumptions that underlie the FDI analytical redundancy approach and the DX logical approach. The formal match of the two approaches is proved and the theoretical proof of their equivalence together with the necessary and sufficient conditions is provided. This work results from the collaboration existing within the French IMALAIA group supported by the French National Programs on Automatic Control GDR-Automatique and on Artificial Intelligence GDR-I3, and AFIA.

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.