Fault detection and diagnosis using Principal Component Analysis of vibration data from a reciprocating compressor

M. Ahmed, M. Baqqar, F. Gu, A. D. Ball

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

50 Citations (Scopus)


This paper investigates the use of time domain vibration features for detection and diagnosis of different faults from a multi stage reciprocating compressor. Principal Component Analysis (PCA) is used to develop a detection and diagnosis framework in that the effective diagnostic features are selected from PCA of 14 potential features and a PCA model based detection method using Hotelling's T2 and Q statistics is subsequently developed to detect various faults including suction valve leakage, inter-cooler leakage, loose drive belt, and combinations of discharge valve leakage with suction valve leakage, suction valve leakage with intercooler leakage and discharge valve leakage with intercooler leakage. A study of Q -contributions has found two original features: Histogram Lower Bound and Normal Negative log-likelihood which allow full classification of different simulated faults.

Original languageEnglish
Title of host publicationProceedings of the 2012 UKACC International Conference on Control, CONTROL 2012
Number of pages6
ISBN (Electronic)9781467315609
ISBN (Print)9781467315593
Publication statusPublished - 22 Oct 2012
EventUKACC International Conference on Control 2012 - Cardiff, United Kingdom
Duration: 3 Sep 20125 Sep 2012
Conference number: 9
http://wikicfp.com/cfp/servlet/event.showcfp?eventid=22012 (Link to Conference Information)


ConferenceUKACC International Conference on Control 2012
Abbreviated titleCONTROL 2012
Country/TerritoryUnited Kingdom
OtherUnited Kingdom Automatic Control Council
Internet address


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