The design and analysis of fault diagnosis architectures using the model-based analytical redundancy approach has received considerable attention during the last two decades. One of the key issues in the design of such fault diagnosis schemes is the effect of modeling uncertainties on their performance. This paper describes a fault diagnosis algorithm for a class of nonlinear dynamic systems with modeling uncertainties when not all states of the system are measurable. The main idea behind this approach is to monitor the plant for any off-nominal system behavior due to faults utilizing a nonlinear on-line approximator with adjustable parameters. The on-line approximator only uses the system input and output measurements. A nonlinear estimation model and learning algorithm are described so that the on-line approximator provides an estimate of the fault. The robustness, sensitivity, stability and performance properties of the nonlinear fault diagnosis scheme are rigorously established und...

Stimulated by the growing demand for improving the reliability and performance of systems, fault-tolerant control has been receiving significant attention since its goal is to detect the occurrence of faults and achieve satisfactory system performance in the presence of faults. To develop an intelligent fault-tolerant control system, we begin by constructing a design model of the system using a hierarchical learning structure in the form of Takagi–Sugeno fuzzy systems. Afterwards, the fault-tolerant control scheme is designed based on stable adaptive fuzzy/neural control, where its on-line learning capabilities are used to capture the unknown dynamics caused by faults. Finally, the effectiveness of the proposed methods has been studied by extensive analysis of system zero dynamics and asymptotic tracking abilities for both indirect and direct adaptive control cases, and by “component level model” simulation of the General Electric XTE46 turbine engine.

Abstract—Multirobot systems are being increasingly used for a variety of tasks in manufacturing, surveillance, and space exploration. These systems can degrade or develop faults during operation, and, therefore, require online diagnosis algorithms to ensure safe operation. Centralized approaches to online diagnosis of robot formations do not scale well for two primary reasons: 1) the computational complexity of the algorithm grows significantly with the number of robots, and 2) the individual robots must communicate a large number of measurements to a central diagnoser. To overcome these problems, we present a distributed, model-based, qualitative fault-diagnosis approach for formations of mobile robots. The approach is based on a bond-graph modeling framework that can deal with multiple sensor types and isolate process, sensor, and actuator faults. The diagnosis scheme employs relative measurement orderings to discriminate among faults by exploiting the temporal order of measurement deviations. This increases the discriminatory power of the measurement set and produces a more efficient fault-isolation algorithm. We describe a distributed diagnoser design algorithm applied to robot formations. Experimental results demonstrate the improvement in both the discriminatory power of the measurements produced by the relative measurement orderings, and the computational efficiency achieved by the distributed-diagnosis approach. Index Terms—Mobile robots, model-based diagnosis, multirobot systems. I.

Fault detection and diagnosis for jet engines is complicated by the presence of engine-to-engine manufacturing differences and engine deterioration during normal operation, the complexity of an accurate engine model, and our inability to directly measure certain engine variables. Here, we work with a sophisticated component level model (CLM) simulation of a turbine engine (the General Electric XTE46) that can simulate the effects of manufacturing and deterioration differences,in addition to a variety of faults. To develop a fault diagnosis system we begin by using the CLM to generate data that is used by a Levenberg–Marquardt method to train a Takagi–Sugeno fuzzy system to represent the engine. The resulting nonlinear model provides a reasonably accurate representation of manufacturing differences,engine deterioration,and fault effects. We use multiple copies of this nonlinear model,each representing a different fault,to generate error residuals by comparing them to the engine output. In fact,we manage the composition of the set of models with a ‘‘supervisor’ ’ that ensures that the appropriate models are on-line,and processes the error residuals to detect and identify faults. Robustness and fault sensitivity of the proposed approach are studied in the paper and the component model level simulation of the XTE46 engine is used to illustrate the effectiveness of the fault diagnosis scheme.

Fault tolerant control for jet engines is complicated by the presence of engine-to-engine man-ufacturing differences and engine deterioration during normal operation, the complexity of an accurate engine model, and our inability to directly measure certain engine variables. Here, we work with a sophisticated component level model (CLM) simulation of a turbine engine (the General Electric XTE46) that can simulate the effects of manufacturing and deterioration differences, in addition to a variety of faults. To develop a fault tolerant control system we beginby using the CLM to generate data that is used by a Levenberg-Marquardt method to train a Takagi-Sugeno fuzzy system to represent the engine. The resulting nonlinear model provides areasonably accurate representation of manufacturing differences, engine deterioration, and fault effects. Stable indirect and direct adaptive controllers are applied to achieve fault tolerant en-gine control by using Takagi-Sugeno fuzzy systems to "learn " the unknown dynamics caused by faults and to accommodate faults by updating the controller, and this nonlinear model provides a priori knowledge about the nominal engine dynamics. We prove that both adaptive schemesachieve asymptotic tracking. The performance of the fault tolerant indirect and direct adaptive

Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Many

Abstract not found