Abstract
The objective of this paper is to set the context for the potential application of rough sets in prognostics. Prognostics is a field of engineering, which deals with predicting faults and failures in technical systems. Engineering solutions to respective problems embrace the use of multiple Artificial Intelligence (AI) techniques. The authors, first, review selected AI techniques used in prognostics and then propose the application of rough sets to build the system health prognostication model.
Highlights
This paper builds on a previous limited survey of Artificial Intelligence (AI) techniques in prognostics [1]
Since the first comprehensive survey of AI methods used for prognostics, completed in 2007 [2], a number of new algorithms based on various AI approaches have been either developed or applied to prognostics problems
We discuss briefly one such technique, Support Vector Machines, and pursue toward description of a new technique based on rough sets, which to our knowledge has not been used in prognostics before
Summary
This paper builds on a previous limited survey of AI techniques in prognostics [1]. Since the first comprehensive survey of AI methods used for prognostics, completed in 2007 [2], a number of new algorithms based on various AI approaches have been either developed or applied to prognostics problems. The entire discipline of Prognostics and Health Management (PHM) has been significantly developed during this time, and even though no specific new survey paper has been published, perhaps with one exception [3], several articles appeared, which summarize the accomplishments far, or set up the scene for the future [4,5,6,7,8].
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