Anibal Bregon

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 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 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.

Distributed Prognostics Based on Structural Model Decomposition

Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system, and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into computationally-independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Computationally independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability.

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.

A Common Framework for Compilation Techniques Applied to Diagnosis of Linear Dynamic Systems

The systems dynamics and control engineering (FDI) and the artificial intelligence diagnosis (DX) communities have developed complementary approaches that exploit structural relations in the system model to find efficient solutions for the residual generation and residual evaluation steps in fault detection and isolation in dynamic systems. This paper compares three different structural fault diagnosis techniques, two from the DX community and one from the FDI community. To simplify our comparison, we start with bond graphs as the common system modeling language and develop a graph-based framework using temporal causal graphs as the basis for analyzing the three fault isolation approaches. This framework allows for systematic comparison of the diagnosability properties of the three algorithms. The three-tank system is used as a running example to illustrate our concepts and algorithms.

Analytic Redundancy, Possible Conflicts, and TCG-based Fault Signature Diagnosis applied to Nonlinear Dynamic Systems

Abstract The FDI and DX communities have developed complementary approaches that exploit structural relations in the system model to find efficient solutions for the residual generation and residual evaluation steps in fault detection and isolation in dynamic systems. This paper compares three different structural techniques, two from the DX community and one from the FDI community. To simplify our comparison, we start with a common modeling approach that employs bond graphs. We describe the residual generation methods used by the three approaches, and apply them to a standard three tank configuration to demonstrate their diagnostic ability for continuous, nonlinear systems.

Integration of Simulation and State Observers for Online Fault Detection of Nonlinear Continuous Systems

The development of efficient and reliable fault detection approaches is necessary to improve performance, safety, and reliability in engineering systems. Moreover, these approaches have to be simple enough to provide quick diagnosis results and to reduce development and maintenance costs. Consistency-based diagnosis using possible conflicts (PCs) relies upon the simulation of numerical models to provide a simple and efficient fault diagnosis approach. However, simulation approaches need to know the initial state, and this assumption is not easily fulfilled in real systems, even in the presence of measurements related to state variables due to noise and parameter uncertainties. In this paper, we develop an approach where PCs are used to automatically compute structural models which can be implemented as simulation and state observer models. Using these models, we propose a framework which integrates those state observers to estimate the initial states for simulation within the consistency-based diagnosis framework. Then, both the simulation models and the state observers are used to provide quick detection decisions without increasing the complexity of the diagnoser. Our integration proposal is open to different kinds of state observers, except for the structural model, and different fault detection configurations. The proposal has been tested on a thermohydraulic reconfigurable laboratory plant using real data with satisfactory results.

A structural model decomposition framework for systems health management

Systems health management (SHM) is an important set of technologies aimed at increasing system safety and reliability by detecting, isolating, and identifying faults; and predicting when the system reaches end of life (EOL), so that appropriate fault mitigation and recovery actions can be taken. Model-based SHM approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems. Improvement in scalability and efficiency can be achieved by decomposing the system model into smaller local submodels and operating on these submodels instead. In this paper, the global system model is analyzed offline and structurally decomposed into local submodels. We define a common model decomposition framework for extracting submodels from the global model. This framework is then used to develop algorithms for solving model decomposition problems for the design of three separate SHM technologies, namely, estimation (which is useful for fault detection and identification), fault isolation, and EOL prediction. We solve these model decomposition problems using a three-tank system as a case study.