Distributed fault diagnosis using minimal structurally over-determined sets: Application to a water distribution network

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksDistributed fault diagnosis is becoming more and more common in industries, to diagnose faults in any large scale system. There are a lot of disadvantages using centralized fault diagnosis in large-scale systems, since in a centralized implementation all the information has to be collected in one location which is generally not possible or very difficult. Moreover, a centralized system needs a high performance centralized unit which generally in most cases is not available. Due to these difficulties in recent years distributed fault diagnosis techniques have been investigated [10]. In distributed fault diagnosis [1] [2], the global diagnoses for the complete system can be computed from the results in all agents and local diagnose is computed from the results of one agent. In distributed fault diagnosis [3] [8], a global coordination process is not necessary and each subsystem depends on a local diagnoser for local diagnosis tasks and communicating with the remaining local diagnosers until a global diagnosis is produced.Accepted versio

First International Diagnosis Competition - DXC'09

A framework to compare and evaluate diagnosis algorithms (DAs) has been created jointly by NASA Ames Research Center and PARC. In this paper, we present the first concrete implementation of this framework as a competition called DXC 09. The goal of this competition was to evaluate and compare DAs in a common platform and to determine a winner based on diagnosis results. 12 DAs (model-based and otherwise) competed in this first year of the competition in 3 tracks that included industrial and synthetic systems. Specifically, the participants provided algorithms that communicated with the run-time architecture to receive scenario data and return diagnostic results. These algorithms were run on extended scenario data sets (different from sample set) to compute a set of pre-defined metrics. A ranking scheme based on weighted metrics was used to declare winners. This paper presents the systems used in DXC 09, description of faults and data sets, a listing of participating DAs, the metrics and results computed from running the DAs, and a superficial analysis of the results

Diagnosability-Based Sensor Placement through Structural Model Decomposition

Systems health management, and in particular fault diagnosis, is important for ensuring safe, correct, and efficient operation of complex engineering systems. The performance of an online health monitoring system depends critically on the available sensors of the system. However, the set of selected sensors is subject to many constraints, such as cost and weight, and hence, these sensors must be selected judiciously. This paper presents an offline design-time sensor placement approach for complex systems. Our diagnosis method is built upon the analysis of model-based residuals, which are computed using structural model decomposition. Sensor placement in this framework manifests as a residual selection problem, and we aim to find the set of residuals that achieves single-fault diagnosability of the system, uses the minimum number of sensors, and corresponds to the best model decomposition for the best distribution of the diagnosis system. We present a set of algorithms for solving this problem and compare their performance in terms of computational complexity and optimality of solutions. We demonstrate the approach using a benchmark multi-tank system

Health Monitoring in Small Satellite Design

Presentation/lecture on systems health monitoring (diagnostics, prognostics, decision-making) with applications to the design phase of small satellite components and systems