An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study
Artikel i vetenskaplig tidskrift, 2020

Recent development in the predictive maintenance field has focused on incorporating artificial intelligence techniques in the monitoring and prognostics of machine health. The current predictive maintenance applications in manufacturing are now more dependent on data-driven Machine Learning algorithms requiring an intelligent and effective analysis of a large amount of historical and real-time data coming from multiple streams (sensors and computer systems) across multiple machines. Therefore, this article addresses issues of data pre-processing that have a significant impact on generalization performance of a Machine Learning algorithm. We present an intelligent approach using unsupervised Machine Learning techniques for data pre-processing and analysis in predictive maintenance to achieve qualified and structured data. We also demonstrate the applicability of the formulated approach by using an industrial case study in manufacturing. Data sets from the manufacturing industry are analyzed to identify data quality problems and detect interesting subsets for hidden information. With the approach formulated, it is possible to get the useful and diagnostic information in a systematic way about component/machine behavior as the basis for decision support and prognostic model development in predictive maintenance.

unsupervised learning

CRISP-DM methodology

predictive maintenance

industrial big data

dimensionality reduction

machine learning

data pre-processing and analysis

Prognostics and health management

Författare

Ebru Turanoglu Bekar

Chalmers, Industri- och materialvetenskap, Produktionssystem

Per Nyqvist

Chalmers, Industri- och materialvetenskap, Produktionssystem

Anders Skoogh

Chalmers, Industri- och materialvetenskap, Produktionssystem

Advances in Mechanical Engineering

1687-8132 (ISSN) 1687-8140 (eISSN)

Vol. 12 5 1687814020919207

Ämneskategorier

Tillförlitlighets- och kvalitetsteknik

Datavetenskap (datalogi)

Datorsystem

DOI

10.1177/1687814020919207

Mer information

Senast uppdaterat

2020-06-30