Fault diagnosis in reciprocating compressor bearings: an approach using LAMDA applied on current signals

Mariela Cerrada, Douglas Montalvo, Xavier Zambrano, Diego Cabrera, René Vinicio Sánchez

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


Condition monitoring is one of the most important activities to implement predictive maintenance in industrial processes and perform fault diagnosis. Vibration is the most used signal for this purpose, however current signals arise as a non-intrusive alternative to condition monitoring. On the other hand, data driven approaches becomes as a way to develop fault classifiers by using Machine Learning. This paper proposes the development of a fault classifier for diagnosing failures in the bearings of a reciprocating compressor by using the current signals measured from the induction machine that power the mechanical device. The proposal applies cluster validity assessment for feature selection, and a LAMDA-based model for classification. Results show that this proposal can diagnose three failure modes with a precision over 90%.

Original languageEnglish
Pages (from-to)199-204
Number of pages6
Issue number19
StatePublished - 1 Jul 2022
Event5th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies, AMEST 2022 - Bogota, Colombia
Duration: 26 Jul 202229 Jul 2022

Bibliographical note

Funding Information:
Thanks to GIDTEC-UPS for supporting this work through the funds from related research projects.

Publisher Copyright:
Copyright © 2022 The Authors.


  • cluster validity index
  • Fault diagnosis
  • feature selection
  • fuzzy similarity
  • reciprocating compressors


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