Abstract-This paper deals with a method of faults detection and identification based on the clusterization of the multiple diagnostic signals. Various types of faults and character of their occurrence were simulated using DAMADICS Benchmark Process Control System. A great advantage of the applied approach based on self-organizing (Kohonen) maps is that even the smallest differences in signals allow for detection, isolation and identification of type of occurred faults with respect to the healthy condition of the investigated system based on the unsupervised learning. It was shown that in some cases the faults, which are undetectable during monitoring of simple heuristic and statistical parameters and other previously applied methods, are recognizable when the approach based on self-organizing maps is applied. The case studies presented in this paper show the faults detection procedure as well as clusterization of types and successful classification of almost all the unique faulty states of the investigated system.
Development of effective diagnostic systems for the recognition of technical conditions of complex objects or processes requires the use of knowledge from multiple sources. Gathering of diagnostic knowledge acquired from diagnostic experiments as well as independent experts in the form of an information system database is one of the most important stages in the process of designing diagnostic systems. The task can be supported through suitable modeling activities and diagnostic knowledge management. Briefly, this paper presents an example of an application of multimodal diagnostic statement networks for the purpose of knowledge representation. Multimodal statement networks allow for approximate diagnostic reasoning based on a knowledge that is imprecise or even contradictory in part. The authors also describe the software environment REx for the development and testing of multimodal statement networks. The environment is a system for integrating knowledge from various sources and from independent domain experts in particular.
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