An Evaluating Study of Using Thermal Imaging and Convolutional Neural Network for Fault Diagnosis of Reciprocating Compressors

Rongfeng Deng, Xiaoli Tang, Lin Song, Abdullahi Abdulmumeen, Fengshou Gu, Andrew D. Ball

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

Abstract

As an essential mechanical device in many industrial applications, reciprocating compressors may be subject to thermal performance failures, mechanical function failures and motor faults resulting in extremely severe catastrophic collapses. Generally, the presence of such faults affects the temperature field distribution of the device. Infrared thermography technology can detect the thermal radiation signal of an object and converts it into images, which is sensitive and reliable to monitor the condition of reciprocating compressor systems. In this paper, three kinds of faults are simulated in an uncontrolled temperature environment. The temperature distribution signal of a reciprocating compressor is captured by a remote infrared camera in the form of a heat map during the experimental process. A slight shaking window is employed to crop the photographed range of experimental equipment, and 30% of each type of images are flipped to prevent the image position information from affecting the classification results. A convolutional neural networks (CNN) is involved for evaluating the monitoring by classifying three common faulty operations. The results demonstrate that thermal images contains the full information and can be a promising technique to diagnose the faults of reciprocating compressors under various operating conditions with a classification accuracy of more than 98.59%.

Original languageEnglish
Title of host publicationAdvances in Asset Management and Condition Monitoring, COMADEM 2019
EditorsAndrew Ball, Len Gelman, B.K.N. Rao
PublisherSpringer, Cham
Pages1495-1503
Number of pages9
Volume166
ISBN (Electronic)9783030577452
ISBN (Print)9783030577445
DOIs
Publication statusPublished - 28 Aug 2020
Event32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference - University of Huddersfield, Huddersfield, United Kingdom
Duration: 3 Sep 20195 Sep 2019
Conference number: 32
http://www.comadem2019.com/ (Link to Conference Website)

Publication series

NameSmart Innovation, Systems and Technologies
PublisherSpringer
Volume166
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference
Abbreviated titleCOMADEM 2019
CountryUnited Kingdom
CityHuddersfield
Period3/09/195/09/19
Internet address

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