Belarmino Pulido

Possible conflicts: a compilation technique for consistency-based diagnosis

Consistency-based diagnosis is one of the most widely used approaches to model-based diagnosis within the artificial intelligence community. It is usually carried out through an iterative cycle of behavior prediction, conflict detection, candidate generation, and candidate refinement. In that process conflict detection has proven to be a nontrivial step from the theoretical point of view. For this reason, many approaches to consistency-based diagnosis have relied upon some kind of dependency-recording. These techniques have had different problems, specially when they were applied to diagnose dynamic systems. Recently, offline dependency compilation has established itself as a suitable alternative approach to online dependency-recording. In this paper we propose the possible conflict concept as a compilation technique for consistency-based diagnosis. Each possible conflict represents a subsystem within system description containing minimal analytical redundancy and being capable to become a conflict. Moreover, the whole set of possible conflicts can be computed offline with no model evaluation. Once we have formalized the possible conflict concept, we explain how possible conflicts can be used in the consistency-based diagnosis framework, and how this concept can be easily extended to diagnose dynamic systems. Finally, we analyze its relation to conflicts in the general diagnosis engine (GDE) framework and compare possible conflicts with other compilation techniques, especially with analytical redundancy relations (ARRs) obtained through structural analysis. Based on results from these comparisons we provide additional insights in the work carried out within the BRIDGE community to provide a common framework for model-based diagnosis for both artificial intelligence and control engineering approaches.

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.

A New Fault Diagnosis Algorithm that Improves the Integration of Fault Detection and Isolation

This work proposes a new model-based fault diagnosis method that improves the integration of the fault detection and isolation tasks. A new interface between fault detection and fault isolation is presented that contains information about the degree of fault signal activation and the occurrence time of fault signals. A combination of five fault signature matrices is used for the fault isolation process. The matrices store knowledge about faulty system behavior: boolean fault signal occurrence, signs of residual violation, sensitivities, time of fault signal activation and fault signal occurrence order. Finally, the new method is applied to the well-known two-tanks benchmark problem.

A Decomposition Method for Nonlinear Parameter Estimation in TRANSCEND

Fault isolation and identification are necessary components for system reconfiguration and fault adaptive control in complex systems. However, accurate and timely on-line fault identification in nonlinear systems can be difficult and computationally expensive. In this paper, we improve the quantitative fault identification scheme in the TRANSCEND diagnosis approach. First, we propose to use possible conflicts (PCs) to find the set of minimally redundant subsystems that can be used for parameter estimation. Second, we introduce new algorithms for computing PCs from the temporal causal graph model used in TRANSCEND. Third, we use the minimal estimators to decompose the system model into smaller, independent subsystems for the parameter estimation task. We demonstrate the feasibility of this method by running experiments on a simulated model of the reverse osmosis subsystem of the advanced water recovery system developed at the NASA Johnson Space Center. Our results show a considerable reduction in parameter estimation time without loss of accuracy and robustness in the estimation.

Diagnosability Analysis Considering Causal Interpretations for Differential Constraints

This paper is focused on structural approaches to study diagnosability properties given a system model taking into account, both simultaneously or separately, integral and differential causal interpretations for differential constraints. We develop a model characterization and corresponding algorithms, for studying system diagnosability using a structural decomposition that avoids generating the full set of system analytical redundancy relations. Simultaneous application of integral and differential causal interpretations for differential constraints results in a mixed causality interpretation for the system. The added power of mixed causality is demonstrated using a Reverse Osmosis Subsystem from the Advanced Water Recovery System developed at the NASA Johnson Space Center. Finally, we summarize our work and provide a discussion of the advantages of mixed causality over just derivative or just integral causality.

An Alternative Approach to Dependency-Recording Engines in Consistency-Based Diagnosis

Consistency-based diagnosis is a main research area in Model-based diagnosis. Many approaches to consistency-based diagno- need to compute the set of conflicts to generate diagnosis candidates. Possible conflicts are introduced as an alternative to dependency-record- ing engines for conflict calculation. Given a qualitative representation of system description, then search for those subsystems capable to gener- ate predictions, and hence, capable to become conflicts. We define this concept for static systems, and later on we extend the definition to deal with continuous dynamic environments. Moreover, we explain how to do consitency-based diagnosis using possible conflicts.Consistency-based diagnosis is a main research area in Model-based diagnosis. Many approaches to consistency-based diagnosis need to compute the set of conflicts to generate diagnosis candidates. Possible conflicts are introduced as an alternative to dependency-recording engines for conflict calculation. Given a qualitative representation of system description, then search for those subsystems capable to generate predictions, and hence, capable to become conflicts. We define this concept for static systems, and later on we extend the definition to deal with continuous dynamic environments. Moreover, we explain how to do consitency-based diagnosis using possible conflicts.

An event-based distributed diagnosis framework using structural model decomposition

Complex engineering systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis approaches are centralized, but these solutions do not scale well. Also, centralized diagnosis solutions are difficult to implement on increasingly prevalent distributed, networked embedded systems. This paper presents a distributed diagnosis framework for physical systems with continuous behavior. Using Possible Conflicts, a structural model decomposition method from the Artificial Intelligence model-based diagnosis (DX) community, we develop a distributed diagnoser design algorithm to build local event-based diagnosers. These diagnosers are constructed based on global diagnosability analysis of the system, enabling them to generate local diagnosis results that are globally correct without the use of a centralized coordinator. We also use Possible Conflicts to design local parameter estimators that are integrated with the local diagnosers to form a comprehensive distributed diagnosis framework. Hence, this is a fully distributed approach to fault detection, isolation, and identification. We evaluate the developed scheme on a four-wheeled rover for different design scenarios to show the advantages of using Possible Conflicts, and generate on-line diagnosis results in simulation to demonstrate the approach.

Enhancing Consistency Based Diagnosis with Machine Learning Techniques

This paper proposes a diagnosis architecture that integrates consistency based diagnosis with induced time series classifiers, trying to combine the advantages of both methods. Consistency based diagnosis allows fault detection and localization without prior knowledge of the device fault modes. Machine learning techniques are able to induce time series classifiers that may be used to identify fault modes of a dynamic systems. The diagnostician performs fault detection and localization resorting to consistency based diagnosis through possible conflicts. Then, a time series classifier, induced from simulated examples, generates a sequence of faults modes, coherent with the result of the fault localization stage, and ordered by fault modes confidence. Finally, to simplify the diagnosis task, it is considered as a subtask of a supervisory system, who is in charge of identifying the working conditions for the physical system.

Early fault classification in dynamic systems using case-based reasoning

In this paper we introduce a system for early classification of several fault modes in a continuous process. Early fault classification is basic in supervision and diagnosis systems, since a fault could arise at any time, and the system must identify the fault as soon as possible. We present a computational framework to deal with the problem of early fault classification using Case-Based Reasoning. This work illustrates different techniques for case retrieval and reuse that have been applied at different times of fault evolution. The technique has been tested for a set of fourteen fault classes simulated in a laboratory plant.

Basic tasks for knowledge-based supervision in process control

Abstract A new tasks taxonomy for knowledge-based global supervision (GS) of continuous industrial processes is introduced in this work. Possible required tasks are specified together with the analysis of their dimensions, which should be useful in the selection of the final capabilities of supervision. Moreover, these dimensions would help end-users and designers when comparing different systems. Several methodologies based on concepts such as generic task, generic operation or heuristic classification have been proposed to transform knowledge-based system (KBS) development in a systematic knowledge engineering activity. These approaches have been quite successful in domains such as medicine or mineral prospecting, identifying a large number of tasks that experts in the domain articulate to solve the problem. However, this was not the case in the process control area. The selection of tasks and their capabilities is the first step to be taken, even before choosing a KBS analysis and design methodology. Authors found a lack of facilities to do this selection in the aforementioned approaches when they tried to develop a global supervision tool in a beet sugar factory in Spain. Hence, this article describes an attempt to fill this gap. Moreover, it shows how this taxonomy supported the analysis and design stages of a supervision tool in the mentioned industrial application.