Model-Based Diagnosability Analysis for Web Services
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Sensor placement for fault isolation in linear differential-algebraic systems
Automatica (Journal of IFAC)
Another Point of View on Diagnosability
Proceedings of the 2008 conference on STAIRS 2008: Proceedings of the Fourth Starting AI Researchers' Symposium
An algorithm based on structural analysis for model-based fault diagnosis
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Coupling Continuous and Discrete Event System Techniques for Hybrid System Diagnosability Analysis
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Computation of Minimal Sensor Sets for Conditional Testability Requirements
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Robust codiagnosability of discrete event systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new efficient and flexible algorithm for the design of testable subsystems
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
Automated design of an FDI system for the wind turbine benchmark
Journal of Control Science and Engineering
Fault diagnosability utilizing quasi-static and structural modelling
Mathematical and Computer Modelling: An International Journal
A method for quantitative fault diagnosability analysis of stochastic linear descriptor models
Automatica (Journal of IFAC)
Minimizing test-point allocation to improve diagnosability in business process models
Journal of Systems and Software
An event-based distributed diagnosis framework using structural model decomposition
Artificial Intelligence
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It is commonly accepted that the requirements for maintenance and diagnosis should be considered at the earliest stages of design. For this reason, methods for analyzing the diagnosability of a system and determining which sensors are needed to achieve the desired degree of diagnosability are highly valued. This paper clarifies the different diagnosability properties of a system and proposes a model-based method for: 1) assessing the level of discriminability of a system, i.e., given a set of sensors, the number of faults that can be discriminated, and its degree of diagnosability, i.e., the discriminability level related to the total number of anticipated faults; and 2) characterizing and determining the minimal additional sensors that guarantee a specified degree of diagnosability. The method takes advantage of the concept of component-supported analytical redundancy relation, which considers recent results crossing over the fault detection and isolation and diagnosis communities. It uses a model of the system to analyze in an exhaustive manner the analytical redundancies associated with the availability of sensors and performs from that a full diagnosability assessment. The method is applied to an industrial smart actuator that was used as a benchmark in the Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems European project