Abstract not found

Multi-robot 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: (i) the computational complexity of the algorithm grows significantly with the number of robots, and (ii) 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.

Wear and tear from sustained operations cause systems to degrade and develop faults. Online fault diagnosis schemes are necessary to ensure safe operation and avoid catastrophic situations, but centralized diagnosis approaches have large memory and communication requirements, scale poorly, and create single points of failure. To overcome these problems, we propose an online, distributed, model-based diagnosis scheme for isolating abrupt faults in large continuous systems. This paper presents two algorithms for designing the local diagnosers and analyzes their time and space complexity. The first algorithm assumes the subsystem structure is known and constructs a local diagnoser for each subsystem. The second algorithm creates a partition structure and local diagnosers simultaneously. We demonstrate the effectiveness of our approach by applying it to

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

Most software quality research has focused on identifying faults (i.e. information is incorrectly recorded in an artifact). Because software still exhibits incorrect behavior, a different approach is needed. This paper presents a systematic literature review to understand whether using information about the source of a fault (i.e. and error) can be helpful. Once the usefulness of errors is established, then it is important to identify and classify errors. The review identified 149 papers from software engineering, psychology and human cognition that provided information about the sources of requirements faults. A major result of this paper is a categorization of the sources of faults into a formal taxonomy that provides a starting point for future research into error-based approaches to improving software quality.

provides the ability to maintain system health and performance over the life of a system. For safety-critical systems, ISHM must maintain safe operations while increasing availability by preserving functionality and minimizing downtime. This paper discusses a model-based approach to ISHM that combines fault detection, isolation and identification, fault-adaptive control, and prognosis into a common framework. At the core of this framework are a set of component oriented physical system models. By incorporating physics of failure models into component models the dynamic behavior of a failing or degrading system can be derived by simulation. Current state information predicts future behavior and performance of the system to guide decision making on system operation and maintenance. 1,2 We demonstrate our approach on the fluid loop of a secondary

Abstract. The correct detection of a fault can save worthy resources or even prevent the destruction of key equipment, but, mainly, the correct detection of a single fault can save lives, as, for example, in the case of spaceships, aircraft and nuclear plants. In this work a new fault detection method, based on the learning of a Bayesian network, is applied to the longitudinal motion of the 747 aircraft. A linearized model of the 747 flying under the control of an autopilot and subjected to gusts of wind is used and faults at the altitude sensor are simulated. Such faults, if not detected, could make the autopilot lower the flight level causing a collision. A Bayesian network is learned from data collected from the aircraft flying under normal conditions and is used in the fault detection. The simulation results comparing the correct fault detection ratio, the false alarm ratio and the average time of detection of the proposed method and of the Luenberger observer residue approach show a clear advantage of the application of proposed method.

Abnormal event management (AEM) in large manufacturing plants has evolved as a higher and increasingly vital function of process control. In this paper, an intelligent information management and control system is introduced. The different computational agents (i.e., modules) of the system are embodied in a three-layered cognitive hierarchy, which offers intelligent behavior at the system level, as well as at the level of specialized task agents. At the lower level, agents generate goal-seeking reactive behavior. Three different fault detection and isolation agents (i.e., three complementary techniques) are embedded to generate three different assessments and to enhance the fault isolation process. Other utility agents are also incorporated to address topics such as data reconciliation, process model identification and optimization. At the middle layer, agents enable decision making, planning, and deliberative behavior. Two case-based reasoning agents are incorporated; the first manages the system in normal operation, while the other handles faulty process situations. A meta-management agent at the highest level monitors and coordinates other agents so as to make the whole system performance more robust and coherent. 1.

Early detection and isolation of faults is crucial for ensuring system safety and efficiency. Online diagnosis schemes are usually integrated with fault adaptive control schemes to mitigate these fault effects, and avoid catastrophic consequences. These diagnosis approaches must be robust to uncertainties, such as sensor and process noise, to be effective in real world applications. Also, diagnosis schemes must address the drawbacks of centralized diagnosis schemes, such as large memory and computational requirements, single points of failure, and poor scalability. Finally, to be effective, fault diagnosis schemes must be capable of diagnosing different fault types, such as incipient (slow) and abrupt (fast) faults in system parameters. This dissertation addresses the above problems by developing: (i) a unified qualitative diagnosis framework for incipient and abrupt faults in system parameters; (ii) a distributed, qualitative diagnosis approach, where each diagnoser generates globally correct diagnosis results without a centralized coordinator and communicates minimal measurement information and no partial diagnosis results with other diagnosers; (iii) a centralized Bayesian diagnosis scheme that combines our qualitative diagnosis approach with a Dynamic Bayesian network (DBN)-based diagnosis scheme; and

Abstract Anaerobic digestion (AD) plants are highly efficient wastewater treatment processes with possible energetic valorisation. Despite these advantages, many industries are still reluctant to use them because of their instability in front of changes in operating conditions. To face this major drawback and to enhance the industrial use of anaerobic digestion, one solution is to develop and to implement knowledge base (KB) systems that are able to detect and to assess in real-time the quality of operating conditions of the processes. Case-based techniques and heuristic approaches have been already tested and validated on AD processes but two major properties were lacking: modularity of the system (the knowledge base system should be easily tuned on a new process and should still work if one or more inputs are added or removed) and uncertainty management (the assessment of the KB system should remain relevant even in case of too poor or conflicting information sources). This paper addresses these two points and presents a modular KB system where an uncertain reasoning formalism, the Dempster-Shafer theory, is used to combine partial and complementary fuzzy diagnosis modules. Keywords On-line diagnosis, uncertainty, Dempster-Shafer theory, telemonitoring, industrial plant.