Evaluation, Selection, and Application of Model-Based Diagnosis Tools and Approaches

Abstract

Model-based approaches have proven fruitful in the design and implementation of intelligent systems that provide automated diagnostic functions. A wide variety of models are used in these approaches to represent the particular domain knowledge, including analytic state-based models, input-output transfer function models, fault propagation models, and qualitative and quantitative physics-based models. Diagnostic applications are built around three main steps: observation, comparison, and diagnosis. If the modeling begins in the early stages of system development, engineering models such as fault propagation models can be used for testability analysis to aid definition and evaluation of instrumentation suites for observation of system behavior. Analytical models can be used in the design of monitoring algorithms that process observations to provide information for the second step in the process, comparison of expected behavior of the system to actual measured behavior. In the final diagnostic step, reasoning about the results of the comparison can be performed in a variety of ways, such as dependency matrices, graph propagation, constraint propagation, and state estimation. Realistic empirical evaluation and comparison of these approaches is often hampered by a lack of standard data sets and suitable testbeds. In this paper we describe the Advanced Diagnostics and Prognostics Testbed (ADAPT) at NASA Ames Research Center. The purpose of the testbed is to measure, evaluate, and mature diagnostic and prognostic health management technologies. This paper describes the testbed's hardware, software architecture, and concept of operations. A simulation testbed that

A Qualitative Event-Based Approach to Continuous Systems Diagnosis

“…We demonstrate the proposed diagnosis framework with experiments conducted on the ADAPT [15] deployed at NASA Ames Research Center. The testbed is functionally representative of a spacecraft's electrical power system, and consists of three subsystems: power generation (battery chargers), power storage (lead-acid batteries), and power distribution (relays, circuit breakers, dc to ac converters, dc and ac loads).…”

“…To study the performance of the diagnosis algorithms under different fault and noise conditions, we need to perform a large number of experiments. In addition to experiments from the actual testbed, we ran simulation experiments on the VIRTUAL ADAPT testbed [15], [40]. Simulation also allows us to introduce faults that cannot be injected into the actual system safely.…”

“…We demonstrate and experimentally verify our diagnosis approach on the Advanced Diagnostics and Prognostics Testbed (ADAPT) [15], deployed at NASA Ames Research Center. ADAPT represents the functionality of a spacecraft's electrical power system, which exhibits complex nonlinear behaviors and is prone to many different faults.…”

An Integrated Approach to Parametric and Discrete Fault Diagnosis in Hybrid Systems

“…We illustrate our integrated approach to parametric and discrete fault diagnosis on ADAPT [3] deployed at NASA Ames Research Center. The testbed is functionally representative of a spacecraft's electrical power generation, storage, and distribution subsystems.…”

“…The Advanced Diagnostics and Prognostics Testbed (ADAPT) [3], deployed at NASA Ames Research Center, is functionally representative of a spacecraft's electrical power system. Over fifty relays and circuit breakers configure the system into different modes of operation.…”