Failure analysis and remanufacturing techniques in drilling and crushing equipment in mining industries: a review

Anecdotal evidence from industry suggests that those working as arborists prefer to use minimal brim style, climbing helmets rather than traditional forestry helmets. In the mining industry, workers prefer wireless, LED cap lamps. Workers cite better comfort, better ability to see their work and better ventilation as reasons to use those helmets and cap lamps. Safety personnel in the industry would like to base future helmet decisions and requirements on a complete understanding of the ergonomic and safety issues of all available head-borne equipment. Previous research has found that helmet design, head load and head/neck posture can influence the amount of neck discomfort experienced by users. Specific features of helmets and head-mounted personal protective equipment (PPE) in various industries have been changing to reflect ergonomic design principles. A series of three studies were conducted to evaluate usability and preference of new style cap lamps and helmet brims. PARTICIPANTS (n=10-16) were recruited primarily from undergraduate students, and each study represents a different group of novice participants. Two different courses that included a tunnel were used in the first two studies to evaluate cap lamp styles and wireless cap lamps, while a simulated arborist task was used in the final study to evaluate helmet brim. Measures of ergonomic and discomfort questionnaires were analysed for this paper. The first cap lamp study was able to conclude that LED lamps are preferred over incandescent lamps, while the second study demonstrated that users prefer a multi-directional beam, and adjustability features of the cap lamp. In the final study, participants who must perform extreme overhead tasks prefer a helmet with a minimal brim. Additional research is warranted to determine whether actual, industry workers demonstrate the same preferences for these PPE items.

Purpose – The aim of this paper is to characterize adaptation processes in business relationships. The nature of adaptive behavior is described by outlining activities and events in these relationships. The role of perceived product importance and complexity in the character of the adaptations processes is sought. Design/methodology/approach – A case study approach is adopted and two long-term relationships between buyers and sellers of capital equipment in the mining industry are investigated. Perspectives from both sides of the dyad (buyer and seller) were attained through in-depth interviews. Findings – Findings show that supplier-based adaptations occur more frequently than customer-based adaptations. The market antecedents of concentration and resource dependency are identified as drivers of adaptive behavior. Furthermore, product importance and complexity are key drivers to adaptation processes and the development of long-term relationships. Supplier's brand name and the choice of a direct channel strategy are identified as indicators of long-term commitment to the market. Moreover, two-task related factors were extremely relevant as selection criteria for capital equipment: the functional suitability and the degree of standardization/customization of the equipment. Research limitations/implications – The findings are specific to the market environment and recommendations are given for the realm of the mining industry. Multi-case studies in multi-contexts should be conducted to enable generalization and potential theory-building. Practical implications – A number of important managerial implications for buyers and sellers of capital equipment in the mining industry are given. Originality/value – This paper contributes to knowledge by providing rich descriptions of adaptation processes. This real life evidence enables the identification of major drivers of adaptive behavior and, consequently, the development of long-term successful relationships.

PurposeNowadays, green supply chain (SC) management acts as an important strategic issue for the manufacturers. The effective SC design requires the development of analytical models and design tools. Because of the key role of steel in the infrastructure of industries, this metal is called the development metal. Despite the importance of this industry and its economic and environmental impacts through its SC, the SC structure of this industry has been less studied at the macro level. Therefore, the purpose of this paper is twofold: first, to design the structure of a steel industry SC at three levels; and second, to find the most effective and efficient carbon dioxide emitted industry among the supplier industries of the steel industry SC in China as a case study.Design/methodology/approachIn this paper, due to the relationships among different industries, DEMATEL as a multi-criteria decision-making method has been applied.FindingsA SC structure for the steel industry has been designed at three levels. The results indicated that the industries that had the highest relationship with the steel industry are mine industry, electricity, water, and gas industry, and optical and electrical equipment industry, which were recognized as the first-level suppliers for the steel industry. On the other hand, considering the relationship among the embodied carbon dioxide emissions of various industries in China as a case study, it can be said that among the steel suppliers, the most important polluting industries, respectively, are mining industry, electricity, water, and gas industry, optical and electrical equipment industry, machinery industry, chemicals and chemical products industry and coke, refined petroleum and nuclear fuel industry.Practical implicationsThe developed SC can help in providing the steel industries’ managers a basic model for their supplier selection problem at the macro level. This paper can also help the industrial managers to understand the causal relationships among the suppliers of their industries. Finally, this paper can help government and industries managers to discover the most polluted industrial suppliers in the steel industry.Originality/valueThe novelty of this study belongs to the usage of DEMATEL method based on the input-output table to discover the relationships among the industries as well as identifying the main raw material suppliers of the steel industry at three levels. Furthermore, this research discovers the relationships among the embedded carbon dioxide emission of various industries in steel SC to determine the most important polluting industries in steel SC.

Mining industry is characterized by a high operational revenue, and hence high availability of heavy equipment used in mining industry is a critical factor to ensure the revenue target. To maintain high avaliability of the heavy equipment, the equipment’s owner hires an agent to perform maintenance action. Contract is then used to control the relationship between the two parties involved. The traditional contracts such as fixed price, cost plus or penalty based contract studied is unable to push agent’s performance to exceed target, and this in turn would lead to a sub-optimal result (revenue). This research deals with designing maintenance contract compensation schemes. The scheme should induce agent to select the highest possible maintenance effort level, thereby pushing agent’s performance and achieve maximum utility for both parties involved. Principal agent theory is used as a modeling approach due to its ability to simultaneously modeled owner and agent decision making process. Compensation schemes considered in this research includes fixed price, cost sharing and revenue sharing. The optimal decision is obtained using a numerical method. The results show that if both parties are risk neutral, then there are infinite combination of fixed price, cost sharing and revenue sharing produced the same optimal solution. The combination of fixed price and cost sharing contract results in the optimal solution when the agent is risk averse, while the optimal combination of fixed price and revenue sharing contract is obtained when agent is risk averse. When both parties are risk averse, the optimal compensation scheme is a combination of fixed price, cost sharing and revenue sharing.

Impaired operator line of sight has been implicated in several pedestrian–equipment accidents and fatalities in the mining industry. Existing training methods for conveying visibility information lack worker engagement and may be insufficient to capture the dynamic, three-dimensional nature of blind spots around industrial equipment. The present study utilized a custom virtual reality experience intended to shift the way in which visibility information is presented. Visibility knowledge, confidence levels and safe pedestrian behaviors around the load-haul-dump vehicle were examined among participants in control and experimental (virtual reality and conventional training) groups (n = 72). Results demonstrate that the virtual reality intervention was not effective for increasing visibility knowledge and safe pedestrian behaviors relative to controls, although the performances of the virtual reality and conventional training groups were comparable. A discrepancy was identified in the perceived versus actual visibility knowledge and safe pedestrian behaviors at the rear of the load-haul-dump vehicle among the virtual reality training group. The findings suggest poor knowledge transfer between the three-dimensional virtual reality experience and the two-dimensional visibility plot used. The work also speaks to the importance of emphasizing rear-facing visibility deficits around machinery within industry safety training materials.

The mining sector is booming due to the market and worldwide demand increase. The use of novel industrial processes and equipment, growth in trained personnel, and growth in managerial skills go hand in hand with the need to increase productivity in mining areas. These improvements make the mining industry one of the world's riskiest and most unstable. Several large-scale mining projects have failed due to a failure to recognize or underestimate risks, even when the industry uses risk management methods appropriately. To increase decision-making accuracy and protect mining organizations, risk mitigation of development work should be pursued. Safety and risk assessment is vital in the mining industry. The paper evaluates mining industry risks and safety. This research used Gabon's mining sector. This research included multiple dataset-gathering and interpretation methods. This study shows how to find a variety of recognized risks and uncertainty that have not been considered in any methodical or systemic approach to mining sector risk and safety monitoring. This research organizes risks hierarchically to show their consequences and propensity for each stage.

The Inverse Velocity (IV) Method, by Fukuzono (1985), has been used in the mining industry along with slope monitoring radars for almost a decade to predict collapses, with significant successes but also with certain limitations due to the uncertainty associated with the method and the characteristics of the different mechanisms of failure in rock masses. This paper summarises the results of research undertaken on 74 pit wall failures, on high and low walls, in different type of mines all over the world, since 2004. Only the characteristics of the failures associated with the application of Fukuzono’s method are discussed. The results are presented statistically, aiming to illustrate the different values of inverse velocity at collapse that could be achieved and the possible errors when forecasting the time of collapse. Keeping in mind the variability of results are essential to a successful risk management at any open pit mine. Some discussions on the type of inverse velocity plots and its possible association to different failure mechanism are also presented for geotechnical practitioners to be aware of when forecasting collapses.

Mining industry is one of the most important economic sectors in the modern world. Complex working conditions, high loads and the need for continuous monitoring of equipment technical condition require highly qualified specialists and effective tools to analyze large data volumes. Failure analysis of mining machinery and equipment is one of the important processes to determine and eliminate the causes of failures in order to improve the reliability and safety of machinery and equipment operation. The use of modern methods of statistical data processing makes this process more efficient and accurate. The development of a tool for failure analysis of mining machines and equipment can be very beneficial to mining companies. By analyzing the data on mining machines and equipment failures, identifying the primary causes of failures and providing corrective recommendations, the analysis tool can prevent equipment failures, improve machine safety and performance. The development of this tool requires an interdisciplinary approach as it should be user-friendly and scalable. In this regard, the purpose of the study is to present a creation method of an adaptive tool for the Microsoft Excel-based analysis of mining machine failures. The authors consider the basic operation principles of this tool, its functional composition and application potential under various operating conditions of mining equipment. Much attention is paid to the description of the main operation algorithm of the program, which makes it possible to efficiently process large volumes of data, produce accurate results and display them in the form convenient for reliability level estimation and transition to the forecasting of mining machinery and equipment assembly life. Further improvement of the tool for adaptive analysis of data on mining machine operation, within the framework of this study, can be performed by adding new parameters or automation of the troubleshooting processes using neural networks.

PurposeThe purpose of this study is to propose a hybrid reliability-centered maintenance (RCM) approach for mining transportation machines of a limestone complex, a real case in Esfahan, Iran.Design/methodology/approachCriteria for selecting critical machines were collected within literature and selected by decision-makers (DCs), and critical machines have been identified using the preference ranking organization method for enrichment of evaluations (PROMETHEE). Also, multi-criteria decision-making (MCDM) methods were used in addition to failure mode, effects and criticality analysis (FMECA) for selecting and prioritizing high-risk failures as well as optimizing the RCM performance. More specifically, the criteria of severity, detectability and frequency of occurrence were selected for risk assessment based on the previous studies, and were weighted using the analytic hierarchy process (AHP) method. Also, the technique for order of preference by similarity to ideal solution (TOPSIS) has been applied to prioritize failures' risk. Finally, the critical failures were inserted in the RCM decision-making worksheet and the required actions were determined for them.FindingsAccording to the obtained values from PROMEHTEE method, the machine with code 739-7 was selected as the first priority and the most critical equipment. Further, based on results of TOPSIS method, the failure mode of “Lubrication hole clogging in crankpin bearing due poor quality oil,” “Deformation of main bearing due to overwork” and “The piston ring hotness due to unusual increase in the temperature of cylinder” have the highest risks among failure modes, respectively.Originality/valueRCM has been deployed in various studies. However, in the current study, a hybrid MCDM-FMECA has been proposed to cope with high-risk failures. Besides, transportation machineries are one of the most critical equipment in the mining industry. Due to noticeable costs of this equipment, effective and continuous usage of this fleet requires the implementation of proper maintenance strategy. To the best of our knowledge, there is no research which has used RCM for transportation systems in the mining sector, and therefore, the innovation of this research is employment of the proposed hybrid approach for transportation machineries in the mining industry.

Operator fatigue is one of the most prevalent root causes of accidents, both on the highway and in workplaces where heavy equipment is used and 12- hour shifts are employed, such as in the mining industry. In response to this concern, a growing number of Fatigue Management Technologies (FMT) are becoming available to help maintain operator alertness and performance levels by detecting operator fatigue and interfacing with the operator and/or supervisor to prevent accidents and incidents (Williamson et al., 2005, Barr et al., 2005). In light of the numerous competing technologies, the research community, as well as industry, could benefit from the flexible evaluation tool proposed here. It will assist industries as a whole, and corporations more specifically, in identifying the best FMT solutions for different work and/or driving situations. This project was specifically focused on the needs of operators of heavy equipment in the mining industry, but could also be of value to other like industries where shift work is necessary and maintaining high levels of alertness are crucial for ensuring workplace safety and productivity.

Risk management is becoming increasingly more complex. Risk assessment, approached quantitatively, requires a factual database to define the likelihood of adverse health effects of workplace-related injuries and exposures, and it attempts to balance scientific knowledge with concerns of staff, investigators and administration. Practical guidance should be provided for Romanian coal mining companies to make progress in risk assessment process. Guidance is given on how to effectively introduce quantitative risk assessment in mining industry, the main goal being to highlight that the most valuable resource remains experience gained by effectively performing the process. Analyzing how various parameters are described/used, the paper aims to establish the place and role of quantitative risk analysis mining. Possibilities of developing safety/reliability database in coal mining are investigated. The block diagram describing the conceptual structure of a database on failures, safety of equipment and workers in the mining industry was developed. Because mining relies heavily on complex technologies - permanent mining facilities and large mobile equipment and support services - often located in isolated and hostile environments, the implementation of quantitative risk analysis and the development of a realistic database could be considered as a resilience business strategy and conversion of available knowledge into management actions.

Engineering ceramics have a growing application potential for the wear-protection of working parts of extraction, processing, conveying, and dust collection equipment and piping systems in mining and mineral industries. The alumina, zirconia, alumina-zirconia, and silicon carbide-based ceramics developed and manufactured by Ceramic Protection Corporation (CPC) are reviewed. These ceramics have high hardness and other mechanical properties, excellent wear- and corrosion resistance at wet or dry working conditions, at high velocity and turbulence of transported materials, high pressure and cavitation. They provide significantly longer life cycle than many other materials widely used in the mining and mineral processing. The ceramic components may be manufactured in accordance with the customer design and requirement as monolithic bodies, including the bodies with the complicated shapes, and as tiles. The wear test results and the factors effected wear resistance are discussed. The range of products and applications in the mining and mineral industries are reviewed.

Digital twins (DTs) are transforming business operations across industries through accurate replication of physical entities using the Internet of Things and big data analytics. Despite booming progress in the manufacturing, aerospace and buildings sectors, the adoption of DTs in the minerals industry has been slow, and integration with efficient visualisation and user interactions has not been fully optimised to achieve maximum fidelity and usability. One promising avenue for enhancing DT capabilities is the utilisation of extended reality (XR) technologies, which also hold great potential for realising an industrial metaverse where real-world business activities can be conducted in a virtual space. This article proposes a cost-effective and scalable approach to developing a DT with real-time monitoring and control capabilities for a ball mill operation, a widely used processing equipment in the minerals industry. The case study showcases two approaches with different levels of system integration by leveraging serious game development platforms, toolkits and workflows.

Ammonium nitrate (AN) is a widely used compound in the agriculture and mining industries that, if improperly handled, poses a significant explosive hazard, as evidenced by the 2020 Port of Beirut explosion. In the wake of this event, the Australian government required failure analysis studies for all bulk AN storage. This paper details one such failure analysis study where bulk solid AN was stored at a mining facility that also featured a large, double-walled, refrigerated API 620 storage tank containing liquid ammonia. The goal of this study was to determine the quantity of solid AN that, if an explosion were to occur, could cause failure of the tank, potentially resulting in a significantly larger explosion. Nonlinear dynamic finite element analysis (FEA) was performed to simulate blast overpressure wave propagation and incidence upon the exterior tank wall and system response including contact between inner and outer tank walls. The tank was subjected to blast scenarios ranging from the equivalent of 77,000 lbsTNT up to 420,000 lbsTNT to study its behavior from essentially elastic response through effectively certain loss of containment. There is no industry standard or published code establishing failure criteria for a tank or pressure vessel subjected to an external explosion. Therefore, various sources were used to inform the criteria defining structural failure of the tank, including the United States Department of Defense Unified Facilities Criteria 3-340 (DOD UFC 3-340-02), ASME Boiler and Pressure Vessel Code Section VIII Division 3 (ASME VIII-3), and ASME VIII-2. Comparisons were made between these methods, and a range of AN quantities likely to cause failure of the tank is proposed. This study highlights the challenges of blast analysis, failure-level response simulation, and, ultimately, quantifying the consequence of cascading failure events.

For the activities of the mining industry land, equipment, material, and energy are used. During operation material and energy flows such as overburden, dead rock, tailings, wastewater, exhaust air, dust, energy, abrasion, coolant and lubricant losses, are released. These released material and energy flows are nearly always without value for the raw material supply chain as they are not production targets. Instead, they have negative effects on the economy and ecology and are, therefore, referred to as ‘non‐intended’. The knowledge of the quantities and qualities of these non‐intended outputs as a function of the processes and their parameters is the basis for technical and economical measures. A methodology for the acquisition and assessment of the material and energy flows in the mining industry was developed and tested at the Technical University Berlin, Germany. For that purpose and based on a system analysis in different mines, all relevant material and energy flows were assigned to individual processes. Causal relationships, possible interactions, quantities, and qualities were examined as functions of system parameters. Finally, a technical and economic evaluation was performed.