Applying cognitive computing to maintainer-collected data

Research output: Chapter in Book/Conference paperConference paperpeer-review

5 Citations (Scopus)


Companies are investing heavily in predictive maintenance algorithms without considering how the predictions will be validated. When components are removed, the observations of the maintenance technicians about their state (failed or not) and the failure mode are crucial to this validation process and to developing accurate component reliability distributions. Despite years of effort to get maintenance technicians to collect data that is usable and useful to engineers, either by trying to enforce the use of codes or apply management controls, little progress has been made. Advances in cognitive computing processes such as text mining, natural language processing and knowledge representation hold the key to solving this problem. The purpose of this paper is to explain key concepts in text mining, knowledge representation and ontology development in a way that is accessible to reliability and maintenance engineers. We illustrate, using a conveyor system as an example, how these concepts can be applied. Our aim is to convince the reader of the value of investing time to understand and develop cognitive computing methods. Sooner, rather than later, these concepts will be used to translate manually entered maintenance work order data to support validation of condition based maintenance predictions and generation of near real time reliability distributions.

Original languageEnglish
Title of host publication2017 2nd International Conference on System Reliability and Safety, ICSRS 2017
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages9
ISBN (Electronic)9781538633229
Publication statusPublished - 29 Jan 2018
Event2nd International Conference on System Reliability and Safety, ICSRS 2017 - Milan, Italy
Duration: 20 Dec 201722 Dec 2017


Conference2nd International Conference on System Reliability and Safety, ICSRS 2017


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