Belt Conveyor Idlers Fault Detection Using Acoustic Analysis and Deep Learning Algorithm with the YAMNet Pretrained Network

Alharbi, F; Luo, S; Zhao, S; Yang, G; Wheeler, C; Chen, Z

Belt Conveyor Idlers Fault Detection Using Acoustic Analysis and Deep Learning Algorithm with the YAMNet Pretrained Network

Alharbi, F Luo, S Zhao, S

Yang, G Wheeler, C Chen, Z

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Sensors Journal, 2024, PP, (99), pp. 1-1
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Alharbi, F
dc.contributor.author	Luo, S
dc.contributor.author	Zhao, S https://orcid.org/0000-0002-6261-8864
dc.contributor.author	Yang, G
dc.contributor.author	Wheeler, C
dc.contributor.author	Chen, Z
dc.date.accessioned	2024-10-09T03:49:11Z
dc.date.available	2024-10-09T03:49:11Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Sensors Journal, 2024, PP, (99), pp. 1-1
dc.identifier.issn	1530-437X
dc.identifier.issn	1558-1748
dc.identifier.uri	http://hdl.handle.net/10453/181340
dc.description.abstract	Belt conveyor systems are essential in industries like automotive, aerospace, power generation, and heavy machinery, with idlers playing a crucial role in ensuring the smooth movement of materials. However, constant operation in noisy environments accelerates wear and tear on idlers and obscures early signs of malfunction, such as grinding or rattling from loose parts. This challenge makes early fault detection difficult, increasing downtime and maintenance costs. Therefore, timely and accurate fault detection is vital to prevent severe system issues, ensure optimal performance, and avoid unexpected breakdowns and costly production interruptions. Intelligent Fault Detection (IFD) using artificial intelligence (AI) methods has emerged as a solution, with machine learning techniques like convolutional neural networks (CNNs) proving effective. This study uses YAMNet, initially designed for Sound Event Detection (SED), to identify faults in belt conveyor idlers by analyzing their unique acoustic signatures. We enhance detection capabilities by extracting temporal features from YAMNet-generated embeddings using Bidirectional Long-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU), augmented with a soft attention mechanism. These features are evaluated using XGBoost, achieving an impressive 90% accuracy in fault detection across idler test sets. Our approach was rigorously compared to the VGGish model and validated on the publicly available MIMII dataset, where it demonstrated superior performance with AUC scores of 0.8355 for fans, 0.9414 for pumps, 0.9265 for valves, and 0.9703 for sliders. These results significantly improve over baseline scores, with increases of 19.36% for fans, 38.44% for pumps, 74.81% for valves, and 38.61% for sliders. This advancement represents a significant step forward in conveyor system diagnostics, providing a robust solution for enhancing industrial safety and operational efficiency.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Sensors Journal
dc.relation.isbasedon	10.1109/JSEN.2024.3439509
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0205 Optical Physics, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.classification	40 Engineering
dc.title	Belt Conveyor Idlers Fault Detection Using Acoustic Analysis and Deep Learning Algorithm with the YAMNet Pretrained Network
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0205 Optical Physics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Audio, Acoustics and Vibration (CAAV)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-10-09T03:49:07Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Belt conveyor systems are essential in industries like automotive, aerospace, power generation, and heavy machinery, with idlers playing a crucial role in ensuring the smooth movement of materials. However, constant operation in noisy environments accelerates wear and tear on idlers and obscures early signs of malfunction, such as grinding or rattling from loose parts. This challenge makes early fault detection difficult, increasing downtime and maintenance costs. Therefore, timely and accurate fault detection is vital to prevent severe system issues, ensure optimal performance, and avoid unexpected breakdowns and costly production interruptions. Intelligent Fault Detection (IFD) using artificial intelligence (AI) methods has emerged as a solution, with machine learning techniques like convolutional neural networks (CNNs) proving effective. This study uses YAMNet, initially designed for Sound Event Detection (SED), to identify faults in belt conveyor idlers by analyzing their unique acoustic signatures. We enhance detection capabilities by extracting temporal features from YAMNet-generated embeddings using Bidirectional Long-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU), augmented with a soft attention mechanism. These features are evaluated using XGBoost, achieving an impressive 90% accuracy in fault detection across idler test sets. Our approach was rigorously compared to the VGGish model and validated on the publicly available MIMII dataset, where it demonstrated superior performance with AUC scores of 0.8355 for fans, 0.9414 for pumps, 0.9265 for valves, and 0.9703 for sliders. These results significantly improve over baseline scores, with increases of 19.36% for fans, 38.44% for pumps, 74.81% for valves, and 38.61% for sliders. This advancement represents a significant step forward in conveyor system diagnostics, providing a robust solution for enhancing industrial safety and operational efficiency.

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http://hdl.handle.net/10453/181340