Application of minimum entropy deconvolution in diagnosis of reciprocating compressor faults based on airborne acoustic analysis

Debanjan Mondal, Fengshou Gu, Andrew Ball

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The airborne acoustic signals from reciprocating compressors (RCs) exhibit an impulsive periodic transient response and are modulated due to several reasons, including structural and acoustic resonance. The occurrence of faults such as intercooler leakage, filter blockage and compound faults such as a combination of intercooler and discharge valve leakage can enhance the feature characteristics of the signal. As a result, the randomised periodic impulse and the presence of non-linearity due to valve fluttering can contribute to the series of harmonic components in the acquired signal. Thus, common methods have limitations in terms of identifying the characteristic features from signals submerged in high levels of background noise. In this paper, a deconvolution technique named minimum entropy deconvolution (MED) has been adopted to extract the features of the impulses, filtering out the non-transient components from the signal and providing a filtered output that only contains the periodic and transient components of the signal. The filtered signals are then analysed by estimating the root mean square (RMS) and entropy values under various operating pressures with the presence of different faults. The analysis result from the entropy of the filtered signal performs adequately to diagnose the conditions of the reciprocating compressor and hence finds suitable application of the method in diagnosis of compound faults using airborne acoustic signals, making it a remote and cost-effective condition monitoring technique.
Original languageEnglish
Title of host publicationSixteenth International Conference on Condition Monitoring and Asset Management (CM 2019)
PublisherBritish Institute of Non-Destructive Testing
Volume1
ISBN (Print)9781510889774
Publication statusPublished - 1 Aug 2019
EventSixteenth International Conference on Condition Monitoring and Asset Management - Glasgow, United Kingdom
Duration: 25 Jun 201927 Jun 2019
Conference number: 16
https://www.bindt.org/events/CM2019/ (Conference website. )

Conference

ConferenceSixteenth International Conference on Condition Monitoring and Asset Management
Abbreviated titleCM 2019
CountryUnited Kingdom
CityGlasgow
Period25/06/1927/06/19
Internet address

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  • Cite this

    Mondal, D., Gu, F., & Ball, A. (2019). Application of minimum entropy deconvolution in diagnosis of reciprocating compressor faults based on airborne acoustic analysis. In Sixteenth International Conference on Condition Monitoring and Asset Management (CM 2019) (Vol. 1). [139] British Institute of Non-Destructive Testing.