{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T12:56:57Z","timestamp":1780491417256,"version":"3.54.1"},"reference-count":90,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T00:00:00Z","timestamp":1675814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to increasing demands for ensuring the safety and reliability of a system, fault detection (FD) has received considerable attention in modern industries to monitor their machines. Bulk materials are transported worldwide using belt conveyors as an essential transport system. The majority of conveyor components are monitored continuously to ensure their reliability, but idlers remain a challenge to monitor due to the large number of idlers (rollers) distributed throughout the working environment. These idlers are prone to external noises or disturbances that cause a failure in the underlying system operations. The research community has begun using machine learning (ML) to detect idler\u2019s defects to assist industries in responding to failures on time. Vibration and acoustic measurements are commonly employed to monitor the condition of idlers. However, there has been no comprehensive review of FD for belt conveyor idlers. This paper presents a recent review of acoustic and vibration signal-based fault detection for belt conveyor idlers using ML models. It also discusses major steps in the approaches, such as data collection, signal processing, feature extraction and selection, and ML model construction. Additionally, the paper provides an overview of the main components of belt conveyor systems, sources of defects in idlers, and a brief introduction to ML models. Finally, it highlights critical open challenges and provides future research directions.<\/jats:p>","DOI":"10.3390\/s23041902","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T04:57:41Z","timestamp":1675832261000},"page":"1902","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7024-6546","authenticated-orcid":false,"given":"Fahad","family":"Alharbi","sequence":"first","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia"},{"name":"Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suhuai","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2174-3383","authenticated-orcid":false,"given":"Kamran","family":"Shaukat","sequence":"additional","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia"},{"name":"Department of Data Science, University of the Punjab, Lahore 54890, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7173-7892","authenticated-orcid":false,"given":"Craig A.","family":"Wheeler","sequence":"additional","affiliation":[{"name":"School of Engineering, University of Newcastle, Callaghan, NSW 2308, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Engineering, University of Newcastle, Callaghan, NSW 2308, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,8]]},"reference":[{"key":"ref_1","unstructured":"Liu, X. (2016). Prediction of Belt Conveyor Idler Performance, TRAIL Research School."},{"key":"ref_2","first-page":"21","article-title":"Conveyor Belt Troubles (Bulk Material Handling)","volume":"2","author":"Govindan","year":"2014","journal-title":"Int. J. Emerg. Eng. Res. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8002","DOI":"10.1016\/j.ijleo.2016.05.111","article-title":"The conveyor belt longitudinal tear on-line detection based on improved SSR algorithm","volume":"127","author":"Li","year":"2016","journal-title":"Optik"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"797183","DOI":"10.1155\/2013\/797183","article-title":"Design of Online Monitoring and Fault Diagnosis System for Belt Conveyors Based on Wavelet Packet Decomposition and Support Vector Machine","volume":"5","author":"Li","year":"2013","journal-title":"Adv. Mech. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Nascimento, R., Carvalho, R., Delabrida, S.E., Bianchi, A.G.C., Oliveira, R.A.O., and Garcia, L.G.U. (2017, January 26\u201329). An Integrated Inspection System for Belt Conveyor Rollers\u2014Advancing in an Enterprise Architecture. Proceedings of the 19th International Conference on Enterprise Information Systems, Porto, Portugal.","DOI":"10.5220\/0006369101900200"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106520","DOI":"10.1016\/j.engfailanal.2022.106520","article-title":"Types and causes of damage to the conveyor belt\u2014Review, classification and mutual relations","volume":"140","author":"Bortnowski","year":"2022","journal-title":"Eng. Fail. Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110177","DOI":"10.1016\/j.measurement.2021.110177","article-title":"Research on the fault analysis method of belt conveyor idlers based on sound and thermal infrared image features","volume":"186","author":"Liu","year":"2021","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2689","DOI":"10.1016\/j.apt.2020.04.034","article-title":"Acoustic signal based fault detection on belt conveyor idlers using machine learning","volume":"31","author":"Liu","year":"2020","journal-title":"Adv. Powder Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.3390\/pr8091123","article-title":"A Review on Fault Detection and Process Diagnostics in Industrial Processes","volume":"8","author":"Park","year":"2020","journal-title":"Processes"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.1109\/TIE.2015.2417501","article-title":"A Survey of Fault Diagnosis and Fault-Tolerant Techniques\u2014Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches","volume":"62","author":"Gao","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106587","DOI":"10.1016\/j.ymssp.2019.106587","article-title":"Applications of machine learning to machine fault diagnosis: A review and roadmap","volume":"138","author":"Lei","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"110330","DOI":"10.1016\/j.measurement.2021.110330","article-title":"Automatic fault detection system for mining conveyor using distributed acoustic sensor","volume":"187","author":"Wijaya","year":"2022","journal-title":"Measurement"},{"key":"ref_13","unstructured":"Alspaugh, M. (2004). Latest Developments in Belt Conveyor Technology, MINExpo."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Qurthobi, A., Maskeliunas, R., and Dama\u0161evi\u010dius, R. (2022). Detection of Mechanical Failures in Industrial Machines Using Overlapping Acoustic Anomalies: A Systematic Literature Review. Sensors, 22.","DOI":"10.3390\/s22103888"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"409","DOI":"10.3390\/e21040409","article-title":"A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery","volume":"21","author":"Wei","year":"2019","journal-title":"Entropy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111869","DOI":"10.1016\/j.measurement.2022.111869","article-title":"Measurement of idlers rotation speed in belt conveyors based on image data analysis for diagnostic purposes","volume":"202","author":"Krot","year":"2022","journal-title":"Measurement"},{"key":"ref_17","first-page":"4","article-title":"Rotating Resistance of Belt Conveyor Idler Rolls","volume":"138","author":"Wheeler","year":"2015","journal-title":"J. Manuf. Sci. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1177\/0020294019840723","article-title":"A regression model for prediction of idler rotational resistance on belt conveyor","volume":"52","author":"Lu","year":"2019","journal-title":"Meas. Control."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Morales, A.S., Aqueveque, P.E., Henriquez, J.A., Saavedra, F., and Wiechmann, E.P. (2017, January 1\u20135). A Technology Review of Idler Condition Based Monitoring Systems for Critical Overland Conveyors in Open-Pit Mining Applications. Proceedings of the 2017 IEEE Industry Applications Society Annual Meeting, Cincinnati, OH, USA.","DOI":"10.1109\/IAS.2017.8101839"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zeng, F., Yan, C., Wu, Q., and Wang, T. (2020). Dynamic Behaviour of a Conveyor Belt Considering Non-Uniform Bulk Material Distribution for Speed Control. Appl. Sci., 10.","DOI":"10.3390\/app10134436"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s40857-019-00153-6","article-title":"Automatic and Efficient Fault Detection in Rotating Machinery using Sound Signals","volume":"47","author":"Altaf","year":"2019","journal-title":"Acoust. Aust."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"122644","DOI":"10.1109\/ACCESS.2019.2938227","article-title":"Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review","volume":"7","author":"Saufi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1504\/IJMME.2021.114914","article-title":"In-belt vibration monitoring of conveyor belt idler bearings by using wavelet package decomposition and artificial intelligence","volume":"12","author":"Roos","year":"2021","journal-title":"Int. J. Min. Miner. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104650","DOI":"10.1016\/j.conengprac.2020.104650","article-title":"An audio-based intelligent fault diagnosis method for belt conveyor rollers in sand carrier","volume":"105","author":"Peng","year":"2020","journal-title":"Control. Eng. Pract."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Angelo, T.D., Mendes, M., Keller, B., Ferreira, R., Delabrida, S., Rabelo, R., Azpurua, H., and Bianchi, A. (2019, January 16\u201319). Deep Learning-Based Object Detection for Digital Inspection in the Mining Industry. Proceedings of the 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA.","DOI":"10.1109\/ICMLA.2019.00116"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yang, W., Zhang, X., and Ma, H. (2016, January 19\u201322). An inspection robot using infrared thermography for belt conveyor. Proceedings of the 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Xi\u2019an, China.","DOI":"10.1109\/URAI.2016.7734069"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Szrek, J., Wodecki, J., B\u0142a\u017cej, R., and Zimroz, R. (2020). An Inspection Robot for Belt Conveyor Maintenance in Underground Mine\u2014Infrared Thermography for Overheated Idlers Detection. Appl. Sci., 10.","DOI":"10.3390\/app10144984"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s11192-015-1765-5","article-title":"The journal coverage of Web of Science and Scopus: A comparative analysis","volume":"106","author":"Mongeon","year":"2016","journal-title":"Scientometrics"},{"key":"ref_29","unstructured":"Cooper, D. (2015). Sensor Platform for Monitoring Conveyor Belt Rollers, University of Southern Queensland."},{"key":"ref_30","unstructured":"(2022, August 23). Conveyor Guarding in Mines. Available online: https:\/\/www.ontario.ca\/page\/conveyor-guarding-mines."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4005","DOI":"10.1007\/s11831-022-09727-6","article-title":"Predictive Monitoring of Incipient Faults in Rotating Machinery: A Systematic Review from Data Acquisition to Artificial Intelligence","volume":"29","author":"Saini","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_32","unstructured":"Zimroz, R., and Kr\u00f3l, R. (2009). Failure Analysis of Belt Conveyor Systems, Prace Naukowe Instytutu G\u00f3rnictwa Politechniki Wroc\u0142awskiej."},{"key":"ref_33","first-page":"11","article-title":"Failure analysis of belt conveyor system","volume":"2","author":"Gurjar","year":"2012","journal-title":"Int. J. Eng. Soc. Sci."},{"key":"ref_34","first-page":"35","article-title":"Belt conveyor idler roll behaviors","volume":"7","author":"Reicks","year":"2008","journal-title":"Bulk Mater. Handl. By Conveyor Belt"},{"key":"ref_35","unstructured":"Yang, B.Y. (2014). Fibre Optic Conveyor Monitoring System, Australian Coal Research Limited."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.measurement.2018.04.066","article-title":"Experimental research on condition monitoring of belt conveyor idlers","volume":"127","author":"Liu","year":"2018","journal-title":"Measurement"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"104898","DOI":"10.1016\/j.engfailanal.2020.104898","article-title":"Failure analysis of idler roller bearings in belt conveyors","volume":"117","year":"2020","journal-title":"Eng. Fail. Anal."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"126","DOI":"10.3103\/S1068366617020076","article-title":"A technique for forecasting the durability of rolling bearings and the optimum choice of lubricants under flood-lubrication and oil-starvation conditions","volume":"38","author":"Dmitrichenko","year":"2017","journal-title":"J. Frict. Wear"},{"key":"ref_39","unstructured":"FLEXCO (2022, September 05). What Affects Conveyor Roller Life? Technical Solutions for Belt Conveyor Productivity. Available online: http:\/\/documentlibrary.flexco.com\/X2640_enAU_2525_INSCCTlife_0813.pdf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.neucom.2019.09.109","article-title":"Audio-based fault diagnosis for belt conveyor rollers","volume":"397","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_41","unstructured":"Jiang, X.P., and Cao, G.Q. (2015, January 15\u201317). Belt Conveyor Roller Fault Audio Detection Based on the Wavelet Neural Network. Proceedings of the International Conference on Natural Computation, Zhangjiajie, China."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Shiri, H., Wodecki, J., Zi\u0119tek, B., and Zimroz, R. (2021). Inspection robotic UGV platform and the procedure for an acoustic signal-based fault detection in belt conveyor idler. Energies, 14.","DOI":"10.3390\/en14227646"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Skoczylas, A., Stefaniak, P., Anufriiev, S., and Jachnik, B. (2021). Belt Conveyors Rollers Diagnostics Based on Acoustic Signal Collected Using Autonomous Legged Inspection Robot. Appl. Sci., 11.","DOI":"10.3390\/app11052299"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"510","DOI":"10.17531\/ein.2022.3.12","article-title":"Roller damage detection method based on the measurement of transverse vibrations of the conveyor belt","volume":"24","author":"Bortnowski","year":"2022","journal-title":"Eksploat. I Niezawodn. Maint. Reliab."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.measurement.2018.10.001","article-title":"Fault diagnosis of self-aligning troughing rollers in belt conveyor system using k-star algorithm","volume":"133","author":"Ravikumar","year":"2019","journal-title":"Measurement"},{"key":"ref_46","unstructured":"Ravikumar, S., Kanagasabapathy, S., Muralidharan, V., Srijith, R., and Bimalkumar, M. (2018). Emerging Trends in Engineering, Science and Technology for Society, Energy and Environment, CRC Press."},{"key":"ref_47","unstructured":"Ravikumar, S., Kangasabapathy, H., and Muralidharan, V. (2014, January 2\u20133). Fault Diagnosis of Self Aligning Carrying Idler (SAI) in Belt-Conveyor System Using Statistical Features and Support Vector Machine. Proceedings of the International Conference on Computational Intelligence & Advanced Manufacturing Research, Chennai, India."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ravikumar, S., Muralidharan, V., Ramesh, P., and Pandian, C. (2021). Fault Diagnosis of Self-aligning Conveyor Idler in Coal Handling Belt Conveyor System by Statistical Features Using Random Forest Algorithm, Springer.","DOI":"10.1007\/978-981-15-7241-8_16"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/S0098-1354(02)00160-6","article-title":"A review of process fault detection and diagnosis: Part I: Quantitative model-based methods","volume":"27","author":"Venkatasubramanian","year":"2003","journal-title":"Comput. Chem. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ericeira, D.R., Rocha, F., Bianchi, A.G.C., and Pessin, G. (2020, January 19\u201324). Early Failure Detection of Belt Conveyor Idlers by Means of Ultrasonic Sensing. Proceedings of the International Joint Conference on Neural Networks, Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9207646"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.ymssp.2018.12.051","article-title":"An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings","volume":"122","author":"Yang","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_53","first-page":"390134","article-title":"Gearbox fault identification and classification with convolutional neural networks","volume":"2015","author":"Chen","year":"2015","journal-title":"Shock. Vib."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.measurement.2018.07.092","article-title":"Prediction of flow blockages and impending cavitation in centrifugal pumps using Support Vector Machine (SVM) algorithms based on vibration measurements","volume":"130","author":"Panda","year":"2018","journal-title":"Measurement"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.jsv.2016.05.027","article-title":"Convolutional neural network based fault detection for rotating machinery","volume":"377","author":"Janssens","year":"2016","journal-title":"J. Sound Vib."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Tagawa, Y., Maskeli\u016bnas, R., and Dama\u0161evi\u010dius, R. (2021). Acoustic Anomaly Detection of Mechanical Failures in Noisy Real-Life Factory Environments. Electronics, 10.","DOI":"10.3390\/electronics10192329"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"107020","DOI":"10.1016\/j.apacoust.2019.107020","article-title":"Trends in audio signal feature extraction methods","volume":"158","author":"Sharma","year":"2020","journal-title":"Appl. Acoust."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.measurement.2014.08.047","article-title":"Condition monitoring of Self aligning carrying idler (SAI) in belt-conveyor system using statistical features and decision tree algorithm","volume":"58","author":"Muralidharan","year":"2014","journal-title":"Measurement"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Rocha, F., Garcia, G., Pereira, R.F.S., Faria, H.D., Silva, T.H., Andrade, R.H.R., Barbosa, E.S., Almeida, A., Cruz, E., and Andrade, W. (2021). ROSI: A Robotic System for Harsh Outdoor Industrial Inspection\u2014System Design and Applications. J. Intell. Robot. Syst. Theory Appl., 103.","DOI":"10.1007\/s10846-021-01459-2"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"e2827","DOI":"10.1002\/stc.2827","article-title":"Distributed optical fibre sensor for condition monitoring of mining conveyor using wavelet transform and artificial neural network","volume":"28","author":"Wijaya","year":"2021","journal-title":"Struct. Control. Health Monit."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"108367","DOI":"10.1016\/j.measurement.2020.108367","article-title":"Teager energy spectral kurtosis of wavelet packet transform and its application in locating the sound source of fault bearing of belt conveyor","volume":"173","author":"Zhang","year":"2021","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Junbo, T., Weining, L., Juneng, A., and Xueqian, W. (2015, January 23\u201325). Fault diagnosis method study in roller bearing based on wavelet transform and stacked auto-encoder. Proceedings of the 27th Chinese Control and Decision Conference (2015 CCDC), Qingdao, China.","DOI":"10.1109\/CCDC.2015.7162738"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.jmsy.2021.08.012","article-title":"Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review","volume":"61","author":"Leukel","year":"2021","journal-title":"J. Manuf. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3292","DOI":"10.1007\/s12206-009-0807-4","article-title":"Rolling element bearing fault detection using an improved combination of Hilbert and wavelet transforms","volume":"23","author":"Wang","year":"2009","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_65","unstructured":"Li, C., Song, Z.-H., and Li, P. (2004, January 15\u201319). Bearing fault detection via wavelet packet transform and rough set theory. Proceedings of the Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788), Hangzhou, China."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.measurement.2019.06.025","article-title":"A multi-class support vector machine real-time detection system for surface damage of conveyor belts based on visual saliency","volume":"146","author":"Hao","year":"2019","journal-title":"Measurement"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Gong, W., Chen, H., Zhang, Z., Zhang, M., Wang, R., Guan, C., and Wang, Q. (2019). A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion. Sensors, 19.","DOI":"10.3390\/s19071693"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1690","DOI":"10.1073\/pnas.1800256115","article-title":"Classification and interaction in random forests","volume":"115","author":"Denisko","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"110787","DOI":"10.1016\/j.measurement.2022.110787","article-title":"Research on a sound-based method for belt conveyor longitudinal tear detection","volume":"190","author":"Wang","year":"2022","journal-title":"Measurement"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"109152","DOI":"10.1016\/j.measurement.2021.109152","article-title":"Longitudinal tear detection method of conveyor belt based on audio-visual fusion","volume":"176","author":"Che","year":"2021","journal-title":"Measurement"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.dsp.2010.09.004","article-title":"A new online signature verification system based on combining Mellin transform, MFCC and neural network","volume":"21","author":"Fallah","year":"2011","journal-title":"Digit. Signal Process."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Glowacz, A. (2018). Recognition of Acoustic Signals of Commutator Motors. Appl. Sci., 8.","DOI":"10.3390\/app8122630"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Shaikh, K.B., Jawarkar, N.P., and Ahmed, V. (2021, January 22\u201325). Machine diagnosis using acoustic analysis: A review. Proceedings of the 2021 IEEE Conference on Norbert Wiener in the 21st Century (21CW), Chennai, India.","DOI":"10.1109\/21CW48944.2021.9532537"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"9623","DOI":"10.1038\/s41598-022-13237-7","article-title":"A deep learning approach for detecting drill bit failures from a small sound dataset","volume":"12","author":"Tran","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"107735","DOI":"10.1016\/j.measurement.2020.107735","article-title":"Latest developments in gear defect diagnosis and prognosis: A review","volume":"158","author":"Kumar","year":"2020","journal-title":"Measurement"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"5419","DOI":"10.1109\/TII.2020.3022369","article-title":"Multiscale diversity entropy: A novel dynamical measure for fault diagnosis of rotating machinery","volume":"17","author":"Wang","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"108070","DOI":"10.1016\/j.apacoust.2021.108070","article-title":"Fault diagnosis of angle grinders and electric impact drills using acoustic signals","volume":"179","author":"Glowacz","year":"2021","journal-title":"Appl. Acoust."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Glowacz, A. (2019). Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals. Sensors, 19.","DOI":"10.3390\/s19020269"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1687814020916107","DOI":"10.1177\/1687814020916107","article-title":"Fault diagnosis of synchronous hydraulic motor based on acoustic signals","volume":"12","author":"Hou","year":"2020","journal-title":"Adv. Mech. Eng."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"106226","DOI":"10.1016\/j.ymssp.2019.07.007","article-title":"Acoustic fault analysis of three commutator motors","volume":"133","author":"Glowacz","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.ymssp.2018.07.044","article-title":"Fault diagnosis of single-phase induction motor based on acoustic signals","volume":"117","author":"Glowacz","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/S0301-679X(99)00077-8","article-title":"A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings","volume":"32","author":"Tandon","year":"1999","journal-title":"Tribol. Int."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1006\/mssp.2000.1338","article-title":"a Comparative Study of Acoustic and Vibration Signals in Detection of Gear Failures Using WIGNER-VILLE Distribution","volume":"15","author":"Baydar","year":"2001","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"273","DOI":"10.7763\/IJET.2010.V2.133","article-title":"A comparative study between vibration and acoustic signals in HTC cooling pump and chilling pump","volume":"2","author":"Devi","year":"2010","journal-title":"Int. J. Eng. Technol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"3713","DOI":"10.1007\/s11042-022-13428-4","article-title":"Natural language processing: State of the art, current trends and challenges","volume":"82","author":"Khurana","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"108518","DOI":"10.1016\/j.measurement.2020.108518","article-title":"Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network","volume":"173","author":"Wang","year":"2021","journal-title":"Measurement"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"4570","DOI":"10.1109\/JSEN.2018.2825879","article-title":"Acoustic Sensing From a Multi-Rotor Drone","volume":"18","author":"Wang","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"10662","DOI":"10.1109\/ACCESS.2020.2965177","article-title":"Analysis of a Quadcopter\u2019s Acoustic Signature in Different Flight Regimes","volume":"8","author":"Djurek","year":"2020","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1902\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:28:04Z","timestamp":1760120884000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1902"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,8]]},"references-count":90,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23041902"],"URL":"https:\/\/doi.org\/10.3390\/s23041902","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,8]]}}}