Deep Learning-Based Object Detection for Digital Inspection in the Mining Industry

D'Angelo, Thiago; Mendes, Marina; Keller, Breno; Ferreira, Rafael; Delabrida, Saul; Rabelo, Ricardo; Azpúrua, Héctor; Bianchi, Andrea Gomes Campos

doi:10.1109/icmla.2019.00116

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…In the equipment management category, a fault diagnosis [73,74] study that diagnosed equipment defects was performed. Haulage operations [75][76][77][78][79] and navigation [80] studies were conducted to optimize the transportation means, such as trucks and loaders, and to indicate the travelling mode of equipment, respectively.…”

Section: Publication Sourcementioning

confidence: 99%

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Jung¹,

Choi²

2021

Minerals

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Recent developments in smart mining technology have enabled the production, collection, and sharing of a large amount of data in real time. Therefore, research employing machine learning (ML) that utilizes these data is being actively conducted in the mining industry. In this study, we reviewed 109 research papers, published over the past decade, that discuss ML techniques for mineral exploration, exploitation, and mine reclamation. Research trends, ML models, and evaluation methods primarily discussed in the 109 papers were systematically analyzed. The results demonstrated that ML studies have been actively conducted in the mining industry since 2018, mostly for mineral exploration. Among the ML models, support vector machine was utilized the most, followed by deep learning models. The ML models were evaluated mostly in terms of their root mean square error and coefficient of determination.

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Section: Publication Sourcementioning

confidence: 99%

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Jung¹,

Choi²

2021

Minerals

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“…Temperature logs are also crucial for the monitoring of machine mechanics. This statement arises from the fact that variations from the regular patterns of operating temperature are a common symptom of machinery malfunction [3,10,11]. There are various methods to measure temperature in industry.…”

Section: Related Workmentioning

confidence: 99%

A Case Study on Remote Instrumentation of Vibration and Temperature in Bearing Housings

Cardona

Cifuentes

Hernandez

et al. 2021

JLPEA

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Data collection is one of the most relevant topics of modern automation and industry. It is usually a costly and time-consuming task, especially in continuous processes. Our case study takes place in a sugar cane mill. The required continuous operation of a belt conveyor for bagasse transportation makes it a critical system in the overall production process. Therefore, a predictive maintenance tool is highly applicable here. We identified bearing housings as critical points for data collection intended for prognostics of the conveyor. However, given the number of points, the cost of a commercial solution becomes unfeasible by our company. This paper reports the development of low-cost devices for measurements and wireless transmission of vibration and temperature data from bearing housings. We assessed several sensor options and made decisions based on a cost-suitability commitment, which led to the design of the electronic devices. The devices were tested for correct operation, reliability (99%), and relative measurement errors under 1.2%. From the tests, we conclude that our proposal is appropriate for our case study’s industrial needs and budget restrictions.

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“…On this basis, thermal image-based detection methods are not considered accurate and reliable methods for intelligent FD, and will not be considered in this review. It is also important to note that thermal image-based detection methods are limited in terms of the number of articles published [ 25 , 26 , 27 ]. Table 1 compares vibration-based methods with acoustic-based and thermal image-based methods in terms of five characteristics: early detection, detection of a wide range of faults, accuracy, detection of multiple idlers simultaneously, and effects of environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

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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Deep Learning-Based Object Detection for Digital Inspection in the Mining Industry

Cited by 9 publications

References 11 publications

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

A Case Study on Remote Instrumentation of Vibration and Temperature in Bearing Housings

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

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