This paper presents methods and tools which can be used within the framework of diagnostics and prognostics to accommodate imprecision of real systems. We outline the advantages and disadvantages of the different techniques and show how they can be used in a hybrid fashion to complement each other. We conclude the paper with a number of successful real world examples.
A framework of new unified neural and neuro-fuzzy approaches for integrating implicit and explicit knowledge in neuro-symbolic systems is proposed. In the developed hybrid system, training data set is used for building neurofuzzy modules, and represents implicit domain knowledge. On the other hand, the explicit domain knowledge is represented by fuzzy rules, which are directly mapped into equivalent neural structures. Three methods to combine the explicit and implicit knowledge modules are proposed.
Phone: (518) 387-7423 Abstract A common limitation of diagnostic systems is their dependence on the training data. It is essential that the diagnostic system should be maintainable over time. In most practical applications the data used for training a diagnostic system does not cover the entire spectrum of faults that a system could encounter. Thus the system should be able to generate rules for new "unknown" faults. An effective methodology for a minimally supervised diagnostic system is developed and explained in this paper.
Integrated Systems Health Management includes as key elements fault detection, fault diagnostics, and failure prognostics. Whereas fault detection and diagnostics have been the subject of considerable emphasis in the Artificial Intelligence (AI) community in the past, prognostics has not enjoyed the same attention. The reason for this lack of attention is in part because prognostics as a discipline has only recently been recognized as a game-changing technology that can push the boundary of systems health management. This paper provides a survey of AI techniques applied to prognostics. The paper is an update to our previously published survey of data-driven prognostics.
Large manufacturing companies are considering to deliver to their customer base "guaranteed uptime" instead of the conventional service contracts. Modern industry is concerned about extending the lifetime of its critical processes and maintaining them only when required. Significant aspects of these trends include the ability to diagnose impending failures, prognose the remaining useful lifetime of the process and schedule maintenance operations so that uptime is maximized. Prognosis is probably the most difficult of the three issues leading to conditionbased maintenance. This paper attempts to address this challenging problem with intelligence-oriented techniques, specifically dynamic wavelet neural networks. Dynamic wavelet neural networks incorporate temporal information and storage capacity into their functionality so that they can predict into the future, carrying out fault prognostic tasks. An example is presented in which a trained dynamic wavelet neural network successfully prognoses a defective bearing with a crack in its inner race.