Abstract
This paper illustrates how the adoption of techniques typical of artificial intelligence (AI) could improve the performance of monitoring and control systems (MCSs). Traditional MCSs are designed according to a three-level architectural pattern in which intelligent devices are usually devoted to evaluate whether the data acquired by a set of sensors could be interpreted as anomalous or not. Possible mistakes in the evaluation process, due to faulty sensors or external factors, can cause the generation of undesirable false alarms. To solve this problem, the traditional three-tier architecture of MCSs has been extended with a fourth level, named the correlation level, where an intelligent module, usually a knowledge-based system, collects the local interpretations made by each evaluation device, building a global view of the monitored field. In this way, possible local mistakes are identified by the comparison with other local interpretations.
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Bandini, S., Sartori, F. Improving the effectiveness of monitoring and control systems exploiting knowledge-based approaches. Pers Ubiquit Comput 9, 301–311 (2005). https://doi.org/10.1007/s00779-004-0334-3
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DOI: https://doi.org/10.1007/s00779-004-0334-3