A technology review of idler condition based monitoring systems for critical overland conveyors in open-pit mining applications

Morales, Anibal S.; Aqueveque, Pablo; Henríquez, J. A.; Saavedra, Francisco; Wiechmann, Eduardo P.

doi:10.1109/ias.2017.8101839

Cited by 14 publications

(8 citation statements)

References 2 publications

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“… Idlers typically have low rotational speeds, making it more difficult to detect faults using traditional monitoring methods that are typically used for higher-speed machinery [ 17 , 18 ]. Idlers are typically exposed to harsh environments, such as dust, heat, and moisture, which can accelerate the degradation of the components and increase the likelihood of faults [ 19 ]. Idlers have a simple construction and are low-cost components, which means that there is a high number of them in a conveyor system.…”

Section: Introductionmentioning

confidence: 99%

“…Idlers are typically exposed to harsh environments, such as dust, heat, and moisture, which can accelerate the degradation of the components and increase the likelihood of faults [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Due to their rotating nature, when functional failures occur in components of belt conveyors, such as idlers, pulleys, and gearboxes, they generate noise that can be measured vibrationally or acoustically [ 19 , 35 ]. Idlers are generally believed to be the primary noise source in belt conveyor systems in acoustic and vibration methods [ 19 , 33 ]. Figure 6 illustrates the use of idler rolls on a typical belt conveyor.…”

Section: Introductionmentioning

confidence: 99%

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A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models

Alharbi

Luo

Zhang

et al. 2023

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

“…Idlers are typically exposed to harsh environments, such as dust, heat, and moisture, which can accelerate the degradation of the components and increase the likelihood of faults [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models

Alharbi

Luo

Zhang

et al. 2023

Sensors

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“…The initial test conducted on a conveyor belt at one of the Australian mining sites showed successful outcomes, where hot idler locations could be clearly detected. However, temperature information may not be sufficient for early damage detection mentioned in Morales et al 17 Figure 3 shows the evolution of lifetime signals from industrial equipment due to a damage with time. It can be seen that temperature changes can be detected when the idler condition is close to failure.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed optical fibre sensor for condition monitoring of mining conveyor using wavelet transform and artificial neural network

Wijaya

Rajeev

Gad

et al. 2021

Structural Contr & Hlth

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Condition monitoring of mining conveyors is highly essential, not only to reduce economic loss due to sudden component breakdown but also to prevent safety risks to maintenance personnel. The vibration measurement by means of Distributed Optical Fibre Sensors (DOFS) is a potential solution to provide cost-effective real-time monitoring of mining conveyors. However, an effective application of detecting early damage and damage progression mainly depends on collection quality of optical signal and robust signal processing methods. Therefore, this study focuses on the identification of an effective signal processing method for real-time monitoring of mining conveyor systems. A prototype conveyor system with various levels of idler damages was built to run experimental tests and to collect optical signals for further analysis. A wavelet-based damage detection method is proposed, and its efficiency is evaluated. Additionally, Artificial Neural Network (ANN) is integrated to develop a smart fault detection technology for fast detection and damage classification. The proposed method has 99% accuracy in classify damage condition under varying coveyor operating speed.

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Automatic Procedure for Steel Cord Belt Splicing Preparation

Barros

Santos

Azpúrua

et al. 2022

J Control Autom Electr Syst

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A technology review of idler condition based monitoring systems for critical overland conveyors in open-pit mining applications

Cited by 14 publications

References 2 publications

A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models

A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models

Distributed optical fibre sensor for condition monitoring of mining conveyor using wavelet transform and artificial neural network

Automatic Procedure for Steel Cord Belt Splicing Preparation

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