The techniques of artificial intelligence have been seen as potentially valuable for the condition monitoring of electrical machines because the underlying physics of machine operation and dynamics, as shown in Chapters 8 and 9, is so rich in fundamental rules, and these could be exploited by an expert system. This chapter shows that these techniques can be used to improve the signal-to noise ratio in a fault situation, particularly in the complex area of monitoring terminal conditions and the difficult subset of that, the detection and measurement of partial discharge activity. However, the development of artificial intelligence for electrical machine condition monitoring is still in its infancy and despite the considerable work that has been done in this area, much more is required to bring such techniques into the mainstream of condition monitoring. An abiding lesson from this chapter is to understand the importance in condition monitoring of relating various monitoring signals with one another, what was described in the first edition of this text as multi-parameter monitoring. It is clear, however, that the future of machine condition monitoring will be heavily affected by multi-parameter monitoring and by the application of artificial intelligence to that process.

Inspec keywords: electric machine analysis computing; condition monitoring; expert systems; electric machines; artificial intelligence; partial discharge measurement; fault diagnosis; computerised monitoring

Other keywords: monitoring signals; multiparameter monitoring; condition monitoring; partial discharge measurement; signal-to noise ratio; artificial intelligence techniques; electrical machines; expert system; fault diagnosis

Subjects: a.c. machines