Techniques for Anomalies Detection

Adolfo Crespo Márquez ()
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Adolfo Crespo Márquez: University of Seville

Chapter Chapter 10 in Digital Maintenance Management, 2022, pp 117-132 from Springer

Abstract: Abstract Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence pretending to generate new business data knowledge transforming sets of “raw data” into business value. To be able to implement these advanced techniques requires, first, a comprehensive and non-trivial process to identify understandable patterns from data. Within this process, the main difficulty is to identify valid and correct data for the analysis from the different sources in the company. Second, efforts must be developed to create analytic models that provide value by improving performance. Third, a cultural change has to be embraced for companies to facilitate the implementation of the analytical results. In addition to this, since accumulation of data is too large and complex to be processed by traditional database management tools (the definition of “big data” in the Merriam–Webster dictionary), new tools to manage big data must be taking into consideration. In this Chapter, interesting examples of the use of different predictive analytics techniques in emerging business processes will be presented. These are examples where the use of these new methods and techniques could successfully be translated into an increase in company profits. An example of baseline predictive analytics for a process of train bearings anomalies detection is presented.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-030-97660-6_10

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DOI: 10.1007/978-3-030-97660-6_10

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