A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models
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 conv...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/4/1902 |
| _version_ | 1797618275887087616 |
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| author | Fahad Alharbi Suhuai Luo Hongyu Zhang Kamran Shaukat Guang Yang Craig A. Wheeler Zhiyong Chen |
| author_facet | Fahad Alharbi Suhuai Luo Hongyu Zhang Kamran Shaukat Guang Yang Craig A. Wheeler Zhiyong Chen |
| author_sort | Fahad Alharbi |
| collection | DOAJ |
| description | 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’s 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. |
| first_indexed | 2024-03-11T08:10:46Z |
| format | Article |
| id | doaj.art-5ea37e3b7c7c431e8660afb0d5a28d63 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:10:46Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5ea37e3b7c7c431e8660afb0d5a28d632023-11-16T23:07:40ZengMDPI AGSensors1424-82202023-02-01234190210.3390/s23041902A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning ModelsFahad Alharbi0Suhuai Luo1Hongyu Zhang2Kamran Shaukat3Guang Yang4Craig A. Wheeler5Zhiyong Chen6School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, AustraliaSchool of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, AustraliaSchool of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, AustraliaSchool of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, AustraliaSchool of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, AustraliaSchool of Engineering, University of Newcastle, Callaghan, NSW 2308, AustraliaSchool of Engineering, University of Newcastle, Callaghan, NSW 2308, AustraliaDue 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’s 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.https://www.mdpi.com/1424-8220/23/4/1902belt conveyor idlersconveyor systemsdeep learningfault detectionmachine learning |
| spellingShingle | Fahad Alharbi Suhuai Luo Hongyu Zhang Kamran Shaukat Guang Yang Craig A. Wheeler Zhiyong Chen A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models Sensors belt conveyor idlers conveyor systems deep learning fault detection machine learning |
| title | A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models |
| title_full | A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models |
| title_fullStr | A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models |
| title_full_unstemmed | A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models |
| title_short | A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models |
| title_sort | brief review of acoustic and vibration signal based fault detection for belt conveyor idlers using machine learning models |
| topic | belt conveyor idlers conveyor systems deep learning fault detection machine learning |
| url | https://www.mdpi.com/1424-8220/23/4/1902 |
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