We present a methodology for the selection of candidate generation and prediction techniques for model-based diagnostic systems (MBDS). We start by describing our taxonomy of the solution space based upon the three main functional blocks of a top-level MBDS architecture (the predictor, the candidate generator and the diagnostic strategist). We divide the corresponding problem space into user requirements and system constraints which are further subdivided into task and fault requirements, and plant and domain knowledge constraints respectively. Finally we propose a set of guidelines for selecting tools and techniques in the solution space given descriptions of diagnostic tasks in the problem space. © 1997 Elsevier Science Limited.
|Number of pages||18|
|Journal||Artificial Intelligence in Engineering|
|Publication status||Published - Jan 1998|
- Model based diagnosis