Artificial Intelligence
Artificial Intelligence
Compositional modeling: finding the right model for the job
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Modeling digital circuits for troubleshooting
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Readings in model-based diagnosis
Readings in model-based diagnosis
Reasoning about model accuracy
Artificial Intelligence
Fault diagnosis of machines via parameter estimation and knowledge processing: tutorial paper
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Reasoning with multiple abstraction models
Recent advances in qualitative physics
Artificial Intelligence
Incremental state based diagnosis
Advanced Engineering Informatics
Qualitative system identification from imperfect data
Journal of Artificial Intelligence Research
A model-based approach to robot fault diagnosis
Knowledge-Based Systems
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In this paper the issue of utilising multiple models for diagnosis of dynamic systems is explored. Models are defined by their properties; which are selected from the three categories: variables, relations, and structures. Also, Model‐based Diagnosis is generally perceived to consist of three tasks: fault detection, fault isolation, and fault identification, dealing with the existence, location, and degree of a fault, respectively. In order to utilise multiple models for diagnosis it is necessary to have a correlation between the model properties and the diagnostic tasks. This provides a coherent means of guiding the switching process according to the task to be performed. A proof of concept of the process is demonstrated with reference to fault identification of a laboratory scale process system rig.