{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T04:26:51Z","timestamp":1769833611165,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T00:00:00Z","timestamp":1592438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Guangdong Province, China","award":["2017A030313291"],"award-info":[{"award-number":["2017A030313291"]}]},{"name":"Innovating major training projects of Beijing Institute of Technology, Zhuhai","award":["XKCQ-2019-06"],"award-info":[{"award-number":["XKCQ-2019-06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As an essential mechanical device in many industrial applications, reciprocating compressors have a high demand for operating efficiency and availability. Because the temperature of each part of a reciprocating compressor depends considerably on operating conditions, faults in any parts will cause the variation of the temperature distribution, which provides the possibility to distinguish the fault type of reciprocating compressors by differentiating the distribution using infrared thermal imaging. In this paper, three types of common fault are laboratory experimented in an uncontrolled temperature environment. The temperature distribution signals of a reciprocating compressor are captured by a non-contact infrared camera remotely in the form of heat maps during the experimental process. Based on the temperature distribution under baseline condition, temperature fields of six main components were selected via Hue-Saturation-Value (HSV) image as diagnostic features. During the experiment, the average grayscale values of each component were calculated to form 6-dimension vectors to represent the variation of the temperature distribution. A computational efficient multiclass support vector machine (SVM) model is then used for classifying the differences of the distributions, and the classification results demonstrate that the average temperatures of six main components aided by SVM is a promising technique to diagnose the faults of reciprocating compressors under various operating conditions with a classification accuracy of more than 99%.<\/jats:p>","DOI":"10.3390\/s20123436","type":"journal-article","created":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T11:00:56Z","timestamp":1592478056000},"page":"3436","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors"],"prefix":"10.3390","volume":"20","author":[{"given":"Rongfeng","family":"Deng","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology, Zhuhai 519088, China"},{"name":"Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, West Yorkshire HD1 3DH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0107-4058","authenticated-orcid":false,"given":"Yubin","family":"Lin","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Zhuhai 519088, China"},{"name":"Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, West Yorkshire HD1 3DH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijie","family":"Tang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Zhuhai 519088, China"},{"name":"Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, West Yorkshire HD1 3DH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengshou","family":"Gu","sequence":"additional","affiliation":[{"name":"Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, West Yorkshire HD1 3DH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8593-6830","authenticated-orcid":false,"given":"Andrew","family":"Ball","sequence":"additional","affiliation":[{"name":"Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, West Yorkshire HD1 3DH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.24874\/jsscm.2017.11.02.01","article-title":"Structural and electrodynamic analysis of rotary transfer machines by Finite Element model","volume":"11","author":"Martini","year":"2017","journal-title":"J. Serb. Soc. Comput. Math."},{"key":"ref_2","first-page":"28","article-title":"Upgrade of an automated line for plastic cap manufacture based on experimental vibration analysis","volume":"3","author":"Martini","year":"2016","journal-title":"Case Stud. Mech. Syst. Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1016\/j.ymssp.2019.02.051","article-title":"Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review","volume":"126","author":"Wang","year":"2019","journal-title":"Mech. Syst. Sig. Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11063-017-9697-0","article-title":"Stochastic support vector machine for classifying and regression of random variables","volume":"48","author":"Abaszade","year":"2018","journal-title":"Neural Process. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1109\/TR.2015.2459684","article-title":"Intelligent condition based monitoring using acoustic signals for air compressors","volume":"65","author":"Verma","year":"2015","journal-title":"IEEE Trans. Reliab."},{"key":"ref_6","first-page":"566","article-title":"Updating temperature monitoring on reciprocating compressor connecting rods to improve reliability","volume":"19","author":"Townsend","year":"2016","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"092605","DOI":"10.1115\/1.4038765","article-title":"Experimental Investigation of Vibrational and Acoustic Phenomena for Detecting the Stall and Surge of a Multistage Compressor","volume":"140","author":"Munari","year":"2018","journal-title":"J. Eng. Gas Turbines Power"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.asej.2019.01.003","article-title":"Observations of changes in acoustic emission parameters for varying corrosion defect in reciprocating compressor valves","volume":"10","author":"Ali","year":"2019","journal-title":"Ain Shams Eng. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"24301","DOI":"10.1016\/j.ijhydene.2019.07.147","article-title":"A non-destructive fault diagnosis method for a diaphragm compressor in the hydrogen refueling station","volume":"44","author":"Li","year":"2019","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107315","DOI":"10.1016\/j.measurement.2019.107315","article-title":"Detection and estimation of valve leakage losses in reciprocating compressor using acoustic emission technique","volume":"152","author":"Sim","year":"2020","journal-title":"Measurement"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106268","DOI":"10.1016\/j.ymssp.2019.106268","article-title":"A new method for nondestructive fault diagnosis of reciprocating compressor by means of strain-based p\u2013V diagram","volume":"133","author":"Li","year":"2019","journal-title":"Mech. Syst. Sig. Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.ymssp.2015.12.033","article-title":"An investigation of the orthogonal outputs from an on-rotor MEMS accelerometer for reciprocating compressor condition monitoring","volume":"76","author":"Feng","year":"2016","journal-title":"Mech. Syst. Sig. Process."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Haba, U., Shaeboub, A., Mones, Z., Gu, F., and Ball, A. (2017, January 5\u20136). Diagnosis of Compound Faults in Reciprocating Compressors Based on Modulation Signal Bispectrum of Current Signals. Proceedings of the 2nd International Conference on Maintenance Engineering, IncoME-II 2017, Manchester, UK.","DOI":"10.1080\/21642583.2017.1331769"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.simpat.2017.10.005","article-title":"Fault-diagnosis for reciprocating compressors using big data and machine learning","volume":"80","author":"Qi","year":"2018","journal-title":"Simul. Modell. Pract. Theory"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.ymssp.2015.09.005","article-title":"Fault detection in reciprocating compressor valves under varying load conditions","volume":"70","author":"Pichler","year":"2016","journal-title":"Mech. Syst. Sig. Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ji, J., and Ma, B. (2020). Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition-convolutional deep belief network. Measurement, 107619.","DOI":"10.1016\/j.measurement.2020.107619"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.neucom.2019.11.006","article-title":"Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor","volume":"380","author":"Cabrera","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.apacoust.2017.12.003","article-title":"Reciprocating compressor prognostics of an instantaneous failure mode utilising temperature only measurements","volume":"147","author":"Loukopoulos","year":"2019","journal-title":"Appl. Acoust."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.proeng.2014.06.355","article-title":"Automated fault classification of reciprocating compressors from vibration data: A case study on optimization using genetic algorithm","volume":"79","author":"Lin","year":"2014","journal-title":"Procedia Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.1088\/0964-1726\/15\/6\/009","article-title":"Automated condition classification of a reciprocating compressor using time\u2013frequency analysis and an artificial neural network","volume":"15","author":"Lin","year":"2006","journal-title":"Smart Mater. Struct."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4113","DOI":"10.1016\/j.eswa.2013.12.026","article-title":"An approach to fault diagnosis of reciprocating compressor valves using Teager\u2013Kaiser energy operator and deep belief networks","volume":"41","author":"AlThobiani","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3767","DOI":"10.1177\/0954406217740929","article-title":"Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network","volume":"232","author":"Tran","year":"2018","journal-title":"Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1016\/j.jngse.2016.09.062","article-title":"Valve fault detection for single-stage reciprocating compressors","volume":"35","author":"Khoshnazar","year":"2016","journal-title":"J. Nat. Gas Sci. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.infrared.2018.08.028","article-title":"Research on the intelligent diagnosis method of the server based on thermal image technology","volume":"96","author":"Liu","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2082","DOI":"10.1016\/j.eswa.2011.08.004","article-title":"Intelligent fault diagnosis of rotating machinery using infrared thermal image","volume":"39","author":"Younus","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.infrared.2017.06.006","article-title":"Thermal feature extraction of servers in a datacenter using thermal image registration","volume":"85","author":"Liu","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.infrared.2015.09.004","article-title":"Thermal image based fault diagnosis for rotating machinery","volume":"73","author":"Janssens","year":"2015","journal-title":"Infrared Phys. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.infrared.2016.12.003","article-title":"Diagnosis of the three-phase induction motor using thermal imaging","volume":"81","author":"Glowacz","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103140","DOI":"10.1016\/j.infrared.2019.103140","article-title":"Three-phase induction motor fault detection based on thermal image segmentation","volume":"104","author":"Anayi","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lo, N.G., Flaus, J.M., and Adrot, O. (2019, January 2\u20134). Review of Machine Learning Approaches in Fault Diagnosis Applied to IoT Systems. Proceedings of the 2019 International Conference on Control, Automation and Diagnosis (ICCAD), Grenoble, France.","DOI":"10.1109\/ICCAD46983.2019.9037949"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6921975","DOI":"10.1155\/2019\/6921975","article-title":"Fault Diagnosis of Reciprocating Compressor Based on Convolutional Neural Networks with Multisource Raw Vibration Signals","volume":"2019","author":"Yang","year":"2019","journal-title":"Math. Prob. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3436\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:40:13Z","timestamp":1760175613000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3436"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,18]]},"references-count":31,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20123436"],"URL":"https:\/\/doi.org\/10.3390\/s20123436","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,18]]}}}