This paper presents a method for identifying when maintenance interventions have been performed on industrial components. The knowledge of the maintenance intervention times is essential in many situations, such as accident analysis, maintenance planning and development of prognostics and health management models. The proposed method is based on the application of a spectral clustering algorithm to raw data signals collected during the component life. A novel measure of similarity, based on Pearson's correlation coefficient, is used to build the similarity graph. The method is shown able to correctly identify the maintenance intervention times of nuclear power plants steam generators and cutting machines used in the packaging industry.

An unsupervised method for the reconstruction of maintenance intervention times

Pinciroli L.;Baraldi P.;Zio E.
2020-01-01

Abstract

This paper presents a method for identifying when maintenance interventions have been performed on industrial components. The knowledge of the maintenance intervention times is essential in many situations, such as accident analysis, maintenance planning and development of prognostics and health management models. The proposed method is based on the application of a spectral clustering algorithm to raw data signals collected during the component life. A novel measure of similarity, based on Pearson's correlation coefficient, is used to build the similarity graph. The method is shown able to correctly identify the maintenance intervention times of nuclear power plants steam generators and cutting machines used in the packaging industry.
Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019
978-981-11-2724-3
Degradation
Maintenance Intervention time
Spectral Clustering
Unsupervised Learning
File in questo prodotto:
File Dimensione Formato  
0710.pdf

Accesso riservato

: Publisher’s version
Dimensione 849.17 kB
Formato Adobe PDF
849.17 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1160358
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact