By Diego Galar Pascual
Artificial Intelligence instruments: selection aid platforms in tracking and Diagnosis discusses numerous white- and black-box methods to fault analysis in situation tracking (CM). This critical resource:
- Addresses nearest-neighbor-based, clustering-based, statistical, and data theory-based techniques
- Considers the advantages of every method in addition to the problems linked to real-life application
- Covers category equipment, from neural networks to Bayesian and help vector machines
- Proposes fuzzy good judgment to give an explanation for the uncertainties linked to diagnostic tactics
- Provides facts units, pattern indications, and MATLAB® code for set of rules testing
Artificial Intelligence instruments: determination help structures in tracking and analysis delivers a radical evaluate of the newest AI instruments for CM, describing the commonest fault analysis suggestions used and the information got while those suggestions are applied.
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Additional info for Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and DIagnosis
We must be able to accept with confidence any inferences drawn from modeling and simulation (Birta and Ozmizrak, 1996). Confidence is guaranteed through verification and validation. • Verification involves checking the consistency of a simulation with the lumped model from which it is derived. The transformation from an abstract representation (the conceptual model) to the program code (the simulation model) must be accurate, in that the program code must reflect the behavior of the conceptual model.
The utility of the simulations, of course, depends on the precision of the model in reproducing the behavior of the actual system; the etymology of simulation (Latin simulare = to pretend) suggests possible ambiguities. 4 Model Construction The unavoidable use of approximation in model construction puts modeling into a gray area; it is not pure science but must be based on results and methodologies offered by the abstract science of mathematics. 7 Massive Field Data Collection In addition, as the models described above have limited ability to solve specific problems, there is a need to construct special-purpose models.
The identification problem may have a number of possible solutions, the choice amongst which is dependent on other factors. Even when the system is deterministic, there may be several models whose responses are identical to those observed. Generally, all models will not be of equal status and there will be a preference ordering among them such that if two are of equal validity, one is preferred to the other; it is convenient to call this preference ordering one of simplicity, or its converse, of complexity.