A paradigm shift is emerging in system reliability and maintainability. The military and industrial sectors are moving away from the traditional breakdown and scheduled
maintenance to adopt concepts referred to as Condition Based Maintenance (CBM) and Prognostic Health Management (PHM). In addition to signal processing and subsequent diagnostic and prognostic algorithms these new technologies require storage of large volumes of both quantitative and qualitative information to carry out maintenance tasks effectively. From the volumes of data that can be obtained today, information extraction has been a challenging task and organizing this information, so that it can be considered useful knowledge, is yet another level of abstraction. This not only requires research and development in advanced technologies but also the means to store, organize and access this knowledge in a timely and efficient fashion.
Knowledge-based expert systems have been recently shown to possess capabilities to manage vast amounts of knowledge, but an intelligent systems approach calls for attributes like self-evaluation (feedback), self evolution (learning) and self-organization (maintenance) to build truly autonomous systems for CBM. Furthermore, an intelligent reasoner is required that can make judicious use of this knowledge and provide a substantial support in the decision making process.
