Hybrid reasoning for prognostic learning in CBM systems

Reasoning systems that integrate explicit knowledge with implicit information are essential for high performance decision support in condition-based maintenance and prognostic health management applications. Such reasoning systems must be capable of learning the specific features of each machine during its life cycle. In this paper, a hybrid reasoning approach that is capable of integrating domain knowledge and test and operational data from the machine is described. This approach is illustrated with an industrial gearbox example. In this approach explicit domain knowledge is expressed as a rule-base and used to train a feed-forward neural network. The training process results in a parsimonious representation of the explicit knowledge by combining redundant rules. A significant added practical benefit of this process is that it also is able to identify logical inconsistencies in the rule-base. Such inconsistencies are notorious in causing deadlock in large-scale expert systems. The neural network can be periodically updated with test and operational data to adapt the network to each specific machine. The flexibility and efficiency of this hybrid approach make it very suitable for practical health management systems designed to operate in a distributed environment.