A Review of Reliability and Fault Analysis Methods for Heavy Equipment and Their Components Used in Mining

Odeyar, Prerita; Apel, Derek B.; Hall, Robert A.; Zon, Brett; Skrzypkowski, Krzysztof

doi:10.3390/en15176263

Cited by 25 publications

(11 citation statements)

References 80 publications

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“…Finally, Ref. [46] provides a summary of the literature on statistical techniques in reliability, particularly for predicting failures and applications of heavy equipment in the mining industry. In addition, they propose to compare traditional methods with machine learning methods by analyzing case studies presented in the literature.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Applications for Reliability Engineering: A Review

Payette

Abdul-Nour

2023

Sustainability

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The treatment of big data as well as the rapid improvement in the speed of data processing are facilitated by the parallelization of computations, cloud computing as well as the increasing number of artificial intelligence techniques. These developments lead to the multiplication of applications and modeling techniques. Reliability engineering includes several research areas such as reliability, availability, maintainability, and safety (RAMS); prognostics and health management (PHM); and asset management (AM), aiming at the realization of the life cycle value. The expansion of artificial intelligence (AI) modeling techniques combined with the various research topics increases the difficulty of practitioners in identifying the appropriate methodologies and techniques applicable. The objective of this publication is to provide an overview of the different machine learning (ML) techniques from the perspective of traditional modeling techniques. Furthermore, it presents a methodology for data science application and how machine learning can be applied in each step. Then, it will demonstrate how ML techniques can be complementary to traditional approaches, and cases from the literature will be presented.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Applications for Reliability Engineering: A Review

Payette

Abdul-Nour

2023

Sustainability

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“…With the development of deep machine learning, equipment diagnostics are moving under the control of unmanned self-learning systems that receive information from smart sensors and use neural networks for analysis and decision making [111]. This forms a system of machine vision; however, machine knowledge technologies are already being formed today, which have been accumulated and improved without human participation, thus increasing the adequacy of decisions made by machines [112]. An example is the positive experience of partially replacing the functions of an operator of a mining wheel loader with a machine vision and learning system that integrated GPS signals, as well as the analytical networks CART, DBSCAN, and C5.0, which helped to cluster and classify data to control the movement of the machine in a quarry [113].…”

Section: Machine Scene Analysis and Scene Understandingmentioning

confidence: 99%

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

Zhironkin

Taran

2023

Energies

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The expansion of end-to-end Industry 4.0 technologies in various industries has caused a technological shock in the mineral resource sector, wherein itsdigital maturity is lower than in the manufacturing sector. As a result of the shock, the productivity and profitability of raw materials extraction has begun to lag behind the industries of its deep processing, which, in the conditions of volatile raw materials markets, can provoke sectoral crises. The diffusion of Industry 4.0 technologies in the mining sector (Mining 4.0) can prevent a technological shock if they are implemented in all segments, including quarrying (Surface Mining 4.0). The Surface Mining 4.0 technological platform would connect the advanced achievements of the Fourth Industrial Revolution (end-to-end digital artificial intelligence technologies, cyber-physical systems and unmanned production with traditional geotechnology) without canceling them, but instead bringing them to a new level of productivity, resource consumption, and environmental friendliness. In the future, the development of Surface Mining 4.0 will provide a response to the technological shock associated with the acceleration of the digital modernization of the mining sector and the increase in labor productivity, which are reducing the operating costs of raw materials extraction. In this regard, the given review is an attempt to analyze the surface mining digital transformation over the course of the diffusion of Industry 4.0 technologies covered in scientific publications. The authors tried to show the core and frontiers of Surface Mining 4.0 development to determine the production, economic, and social effect of replacing humans with digital and cyber-physical systems in the processes of mineral extraction. Particular attention was paid to the review of research on the role of Surface Mining 4.0 in achieving sustainable development goals.

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“…Today, RAM engineering is a well-developed discipline that has branched into specialized areas such as software, mechanical, and many other engineering fields. In this regard, in oil and mine engineering, due to the machinery-based nature, RAM analysis is one of the leading systems for improving the utilization of the project [5]. Several RAM analyses for different types of mining machines have already been reported, including crushing plants [6], drum shearers in longwall coal mining, rotary drilling machines [7], and productivity of draglines [8].…”

Section: Application Of Rammentioning

confidence: 99%

A Critical Investigation on the Reliability, Availability, and Maintainability of EPB Machines: A Case Study

Koohsari¹,

Kalatehjari

Moosazadeh

et al. 2022

Applied Sciences

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Tunnelling is a vital geotechnical engineering feature of underground transportation systems that is potentially hazardous if not properly investigated, studied, planned, and executed. A reliability, availability, and maintainability (RAM) analysis is one of the main practical techniques in machinery-based projects to recognize the failure and repair rates of machines during or after their operations. RAM analysis of mechanized tunneling can help to manage the project safety and cost, and improve the availability and performance of the machine. There are several methods to obtain and predict the RAM of a system, including the Markov chain simulation and other statistical methods; however, the result of the analysis can be affected by the selected method. This paper presents the results of a critical investigation on the RAM of the Earth pressure balance machines (EPBMs) used in developing an urban metro project in Isfahan, Iran. The five kilometer length of the first line of the Isfahan metro project was excavated using EPBMs over four years. After overhauling the EPBMs and making some minor changes, excavation of the second line started, and to date, about 1.2 km has been excavated by the refurbished machines. In the present study, a RAM analysis has been applied to electrical, mechanical, and cutter head subsystems of the EPBMs in Lines 1 and 2 of the Isfahan metro project over an 18- and 7-month period of machine operation, respectively. The results show that the estimated availability, A(t), determined by the Markov method, is closer to reality but cannot be propagated to reliability R(t) and maintainability M(t) analysis. It was also revealed that by predicting the required maintenance and proper planning, the overall availability of the EPBM was improved from 45% in Line 1 to 61% in Line 2. The outcomes of this study can be used in the future planning of urban tunneling projects to estimate machine, staff, and logistic performance with the least possible error, and appropriately arrange the factors involved in the system.

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A Review of Reliability and Fault Analysis Methods for Heavy Equipment and Their Components Used in Mining

Cited by 25 publications

References 80 publications

Machine Learning Applications for Reliability Engineering: A Review

Machine Learning Applications for Reliability Engineering: A Review

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

A Critical Investigation on the Reliability, Availability, and Maintainability of EPB Machines: A Case Study

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