Search Results (25)

Effective condition monitoring (CM) in the mining industry is crucial for operational excellence, given the harsh environments, continuous operation, and high-value nature of assets. Traditional fault diagnosis methods like vibration analysis often prove inadequate due to signal noise, logistical challenges for sensor placement, and limitations in detecting subtle failures. This paper addresses these challenges by proposing an advanced contactless diagnostic system that integrates Infrared Thermography (IRT) with an optimized Convolutional Neural Network (CNN) for detecting machinery faults in mining operations. The core of the approach involves a customized ResNet-50 architecture, chosen for its inherent ability to extract hierarchical features directly from raw thermal image data, thereby circumventing the laborious and error-prone process of manual feature engineering. Recognizing the profound impact of hyperparameters on model performance, a novel optimization strategy is developed. This strategy utilizes an amended Salp Swarm Algorithm (SSA), which incorporates a Levy flight mutation strategy and improved position update mechanisms to enhance its exploration capabilities and prevent premature convergence, ensuring a thorough search of the complex hyperparameter space. The proposed methodology is rigorously evaluated using thermal images acquired from a heavy-duty belt conveyor system at the JARO S.A. mine. The optimized ResNet-50 model achieved a remarkable validation accuracy of 97.22%, demonstrating superior performance. Comparative analysis showed that our model significantly outperformed other state-of-the-art deep learning architectures, such as InceptionV3 and ResNet-18, as well as other metaheuristic optimization algorithms, yielding a 15.6% improvement over the basic SSA. This robust performance, combined with efficient convergence, underscores the model’s capacity for accurate and timely fault identification, paving the way for proactive maintenance, reduced downtime, and enhanced safety in demanding mining environments. Full article

External intrusion hazards such as construction machinery pose serious threats to the safe operation of transmission lines. However, reliable detection in transmission corridors remains challenging because hazardous targets usually exhibit articulated and elongated structures, while monitoring images are dominated by complex backgrounds and rare hard-negative samples. To address these challenges, this paper proposes CMHdet, a morphology-aware and hard-negative-optimized detection framework for external intrusion monitoring in transmission corridors. First, a Dynamic Deformable Transformer module is embedded into the feature extraction backbone to adaptively adjust spatial sampling positions and enhance the representation of irregular machinery structures under viewpoint changes and occlusion. Second, a dual-path multi-scale aggregation network with shifted-window attention is designed to preserve local structural details while strengthening cross-region contextual interaction for small and large-span targets. Third, a hard-negative-aware optimization strategy is developed by combining Gradient Harmonized Mining loss with a false-alarm-guided dynamic copy-paste augmentation mechanism, enabling the model to learn from confusing background regions frequently encountered in long-term monitoring. Experiments on a real-world transmission corridor dataset demonstrate that CMHdet achieves 93.4% mAP, outperforming the YOLOv10L baseline by 5.7 percentage points, with notable improvements under long-distance, occluded, and adverse-weather conditions. The results indicate that the proposed framework provides a reliable solution for intelligent external intrusion monitoring in transmission corridors. Full article

Robotic systems are increasingly being deployed in mining operations to support tasks such as inspection, navigation, environmental monitoring, and safety supervision. However, mining environments present significant challenges for robotic perception due to dynamic terrain conditions, poor illumination, airborne dust, and frequent disturbances caused by excavation and heavy machinery. Conventional frame-based vision systems often struggle under these conditions due to motion blur, latency, and limited dynamic range. This study proposes a system-level conceptual framework for integrating event-based sensing into robotic mining systems in order to support perception in highly dynamic and safety-critical environments, with the aim of improving responsiveness and robustness under such conditions. Event-based cameras, inspired by biological vision, asynchronously detect brightness changes at the pixel level and provide microsecond temporal resolution with high dynamic range and low latency. The proposed framework combines event cameras with complementary sensing modalities including LiDAR, inertial measurement units, and RGB cameras to form a multi-sensor perception architecture. The framework is structured into multiple functional layers encompassing environmental sensing, event-driven perception, sensor fusion and AI processing, digital twin integration, and autonomous decision-making. Potential application scenarios including robotic tunnel inspection, autonomous navigation of mining robots, hazard detection, multi-agent cooperation in mining sites, and real-time digital twin updating are also discussed. The proposed framework provides a unified system-level reference architecture intended to guide future implementation and validation. Full article

Maintaining road infrastructure in underground mines is critical for ensuring efficient transportation, reducing fuel consumption, extending the lifespan of machines, and providing operator safety and comfort. At the same time, the operation of heavy machinery on uneven roads, and the presence of loose rock fragments make it impossible to keep roads in consistently good condition, necessitating continuous condition monitoring and appropriate maintenance planning. This paper proposes a framework based on a single inertial sensor mounted on a mining vehicle for road quality assessment and vehicle speed estimation. The developed methods have a hybrid character, combining the physical interpretability of inertial data with unsupervised AI-based techniques. The integrated analytical system, combining road surface quality assessment with vehicle speed analysis, serves as a decision-supporting tool for pinpointing road segments that are critical for maintenance, safety, transport efficiency, and machine wear. The proposed approach was validated using data collected from haul trucks operating under real-world conditions. The system has the potential to support more efficient and sustainable management of mine road maintenance by reducing unnecessary interventions, resource consumption, and the negative environmental and safety impacts associated with haulage operations. Full article

Securing mine sites is a challenging task due to the complexity of the infrastructure, the variety of physical and digital components, the distribution of assets and machineries, and the large number of stakeholders involved. Given the risks that are present in Tailings Storage Facilities (TSFs), mine operators are seeking technologies to accurately monitor the state of their dams. The latest developments implement evolutive monitoring and responsive risk management systems by adapting accurate Internet of Things technologies, automated mathematical model calculation to continually monitor the structural/geotechnical aspects of TSF, and a portfolio of innovative applications to support decision-making. Within this study, a comprehensive methodology is developed for assessing the environmental sustainability of a smart monitoring solution combining the life cycle assessment (LCA) method with the environmental risk assessment, which quantifies risk reduction potential. The use case scenario is identified based on real industrial data, also aligned with the common characteristics of tailing dams in Europe. Environmental sustainability of the smart monitoring solution is assessed through a cradle-to-grave LCA based on the ReCiPe 2016 (v1.1 Midpoint (H)) method. Monitoring impact alone is reduced primarily by the 40% reduction in monitoring visits, while the results show the environmental improvement of the TSF life cycle by 24% for CO₂-eq., as a step in-line with the EU’s long-term strategy for total decarbonisation in 2050, and Sustainable Development Goal 9 for Industry by the United Nations. Additionally, the 27% freshwater ecotoxicity reduction, 20% human toxicity (cancer) decrease, and the rest of the studied categories indicate an overall footprint improvement for the monitoring solution application on TSFs. The findings demonstrate clearly theoretical, practical and policy implications, not only for the benefit of such solutions for environmental protection, but also for the necessity of integrating risk in sustainability analysis approaches. Full article

Blasting operations associated with in-pit ramp construction in open-pit mines generate gaseous emissions originating from both explosive detonation and diesel-powered drilling and loading equipment. The research object of this study is the ramp construction process in an operating open-pit quarry, and the objective is to comparatively evaluate gaseous emissions across alternative blasting scenarios to support emission-aware operational decision-making. Five realistic blasting scenarios are assessed using a combined methodology that integrates laboratory fume index data for ANFO, emulsion explosives, and dynamite with diesel-emission estimates derived from non-road mobile machinery inventory factors. Laboratory detonation tests provide standardized upper-bound emission potentials for CO_x and NO_x, while drilling and loading emissions are quantified using a fuel-based inventory approach. The results show that the dominant contribution to total mass emissions arises from diesel combustion during drilling and loading, consistent with studies on real-world non-road mobile machinery inventory factors. Detonation fumes, although chemically concentrated and relevant for short-term exposure risk, represent a smaller share of the mass-based emission budget. Among the explosive types, bulk emulsions consistently exhibit lower toxic-gas emission indices than ANFO, attributable to their more uniform microstructure and a moderated reaction temperature. Dynamite demonstrates the lowest fume potential but is operationally less scalable for large open-pit patterns due to manual loading. Uncertainty analysis indicates that both laboratory-derived fume indices and diesel emission factors introduce systematic variability: laboratory tests tend to overestimate detonation fumes, while inventory-based diesel estimates may underestimate real-world NO_x and particulate emissions. Notwithstanding these limitations, the scenario-based framework developed here provides a robust basis for comparative evaluation of blasting strategies during ramp construction. The findings support increased use of emulsion explosives and emphasize the importance of moisture management, field-integrated gas monitoring, and improved characterization of diesel-equipment duty cycles. Full article

Diesel-powered machinery is the primary energy source in underground mining, exposing workers to hazardous diesel exhaust emissions. This study evaluates occupational exposure to diesel particulate matter (DPM) and gaseous pollutants (NO, NO₂) at an underground mine before and after implementing Diesel Particulate Filters (DPF) and Selective Catalytic Reduction (SCR) in mining equipment. A comprehensive monitoring campaign was conducted, employing elemental carbon (EC) as a tracer for diesel particulate emissions and electrochemical sensors for gas measurements. Results show a substantial reduction in EC concentrations following the implementation of DPFs, with median EC exposure decreasing from 0.145 mg/m³ in 2021 to 0.034 mg/m³ in 2023, and the proportion of samples exceeding the occupational exposure limit (OEL) falling from 90% to 28%. Similarly, SCR implementation led to a 72% reduction in NO₂ levels and a 77.5% decrease in NO concentrations in certain equipment; however, NO levels remained persistently high near loaders, suggesting that additional mitigation measures are required. These findings underscore the efficacy of DPF and SCR technologies in improving air quality and reducing occupational exposure in underground mining environments. Nevertheless, persistent NO concentrations and maintenance-related challenges highlight the need for a holistic emission control approach, integrating ventilation improvements, expanded DPF adoption, alternative propulsion systems, and enhanced maintenance protocols. This study provides critical insights into the effectiveness of advanced emission reduction strategies and informs future regulatory compliance efforts in the mining industry. Full article

Given that rotating machinery is one of the most commonly used types of mechanical equipment in industrial applications, the identification of its health status is crucial for the safe operation of the entire system. Traditional equipment health status identification mainly relies on conventional single-modal data, such as vibration or acoustic modalities, which often have limitations and false alarm issues when dealing with real-world operating conditions and complex environments. However, with the increasing automation of coal mining equipment, the monitoring of multimodal data related to equipment operation has become more prevalent. Existing multimodal health status identification methods are still imperfect in extracting features, with poor complementarity and consistency among modalities. To address these issues, this paper proposes a multimodal joint representation learning and residual neural network-based method for rotating machinery health status identification. First, vibration, acoustic, and image modal information is comprehensively utilized, which is extracted using a Gramian Angular Field (GAF), Mel-Frequency Cepstral Coefficients (MFCCs), and a Faster Region-based Convolutional Neural Network (RCNN), respectively, to construct a feature set. Second, an orthogonal projection combined with a Transformer is used to enhance the target modality, while a modality attention mechanism is introduced to take into consideration the interaction between different modalities, enabling multimodal fusion. Finally, the fused features are input into a residual neural network (ResNet) for health status identification. Experiments conducted on a gearbox test platform validate the proposed method, and the results demonstrate that it significantly improves the accuracy and reliability of rotating machinery health state identification. Full article

Ore processing equipment is constantly subjected to impacts from various types of ore. However, the impact force characteristics generated by ore particles of different masses have not been thoroughly studied, which has hindered the design and monitoring of such equipment. This paper presents an experimental study on the dynamic impact characteristics of iron ore particles under free–fall conditions. The research focuses on understanding the mechanical behavior of ore particles of varying sizes and weights when colliding with metallic components, particularly crushers, which are critical in the ore processing industry. A modified Split Hopkinson Pressure Bar apparatus was utilized to measure the impact forces, durations, and deformation patterns during collisions. Two types of fired iron ore pellets were collected from industrial plants and sorted into different mass ranges for testing. The pellets were dropped from a height of 1 m to impact a steel rod, and the resulting impact forces were recorded using strain gauges. Additionally, finite element simulations were conducted to validate the experimental methodology. The results revealed significant variations in impact force, duration, and deformation patterns, influenced by particle mass and impact position. The maximum recorded impact force was approximately 7500 N, indicating the high energy involved in these collisions. Impact durations ranged from 0.05 to 0.11 milliseconds, emphasizing the rapid nature of the interactions. The deformation patterns were consistent across all particles, supporting the applicability of Hertz’s contact theory.This study offers valuable insights into the dynamic impact characteristics of iron ore particles, which are essential for optimizing the design and performance of mining machinery. Full article

Puromycin (Puro) is a natural aminonucleoside antibiotic that inhibits protein synthesis by its incorporation into elongating peptide chains. The unique mechanism of Puro finds diverse applications in molecular biology, including the selection of genetically engineered cell lines, in situ protein synthesis monitoring, and studying ribosome functions. However, the key step of Puro biosynthesis remains enigmatic. In this work, pur6-guided genome mining is carried out to explore the natural diversity of Puro-like antibiotics. The diversity of biosynthetic gene cluster (BGC) architectures suggests the existence of distinct structural analogs of puromycin encoded by pur-like clusters. Moreover, the presence of tRNA^Cys in some BGCs, i.e., cst-like clusters, leads us to the hypothesis that Pur6 utilizes aminoacylated tRNA as an activated peptidyl precursor, resulting in cysteine-based analogs. Detailed metabolomic analysis of Streptomyces sp. VKM Ac-502 containing cst-like BGC revealed the production of a cysteinyl-based analog of Puro—cystocin (Cst). Similar to puromycin, cystocin inhibits both prokaryotic and eukaryotic translation by the same mechanism. Aminonucleoside N-acetyltransferase CstC inactivated Cst, mediating antibiotic resistance in genetically modified bacteria and human cells. The substrate specificity of CstC originated from the steric hindrance of its active site. We believe that novel aminonucleosides and their inactivating enzymes can be developed through the directed evolution of the discovered biosynthetic machinery. Full article

Underground mines are considered one of the riskiest facilities for human activities due to numerous accidents and geotechnical failures recorded worldwide over the last century, which have resulted in unsafe labor conditions, poor health outcomes, injuries, and fatalities. One significant cause of these accidents is the inadequate or nonexistent capacity for the real-time monitoring of safety conditions in underground mines. In this context, new emerging technologies linked to the Industry 4.0 paradigm, such as sensors, the Internet of Things (IoT), and LoRaWAN (Long Range Wide Area Network) wireless connectivity, are being implemented for planning the efficient, safe, and sustainable performance of underground mine labor environments. This paper studies the implementation of an ecosystem composed of IoT sensors and LoRa wireless connectivity in a data-acquisition system, which eliminates the need for expensive cabling and manual monitoring in mining operations. Laying cables in an underground mine necessitates cable support and protection against issues, such as machinery operations, vehicle movements, mine operator activities, and groundwater intrusion. As the underground mine expands, additional sensors typically require costly cable installations unless wireless connectivity is employed. The results of this review indicate that an IoT LoRaWAN-based wireless sensor network (WSN) provides real-time data under complex conditions, effectively transmitting data through physical barriers. This network presents an attractive low-cost solution with reliable, simple, scalable, secure, and competitive characteristics compared to cable installations and manually collected readings, which are more sporadic and prone to human error. Reliable data on the behavior of the underground mine enhances productivity by improving key performance indicators (KPIs), minimizing accident risks, and promoting sustainable environmental conditions for mine operators. Finally, the adoption of IoT sensors and LoRaWAN wireless connectivity technologies provides information of the underground mine in real-time, which supports better decisions by the mining industry managers, by ensuring compliance with safety regulations, improving the productive performance, and fostering a roadmap towards more environmentally friendly labor conditions. Full article

The evaluation and prediction of reliability and testability of mining machinery and equipment are crucial, as advancements in mining technology have increased the importance of ensuring the safety of both the technological process and human life. This study focuses on developing a reliability model to analyze the controllability of mining equipment. The model, which examines the reliability of a mine cargo-passenger hoist, utilizes statistical methods to assess failures and diagnostic controlled parameters. It is represented as a transition graph and is supported by a system of equations. This model enables the estimation of the reliability of equipment components and the equipment as a whole through a diagnostic system designed for monitoring and controlling mining equipment. A mathematical and logical model is proposed to calculate availability and downtime coefficients for different structures within the mining equipment system. This analysis considers the probability of failure-free operation of the lifting unit based on the structural scheme, with additional redundancy for elements with lower reliability. The availability factor of the equipment for monitoring and controlling the mine hoisting plant is studied for various placements of diagnostic systems. Additionally, a logistic concept is introduced for organizing preventive maintenance systems and reducing equipment recovery time by optimizing spare parts, integrating them into strategies aimed at enhancing the reliability of mine hoisting plants. Full article

Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programs of the National Institute for Occupational Accident Insurance (Inail). Objectives: The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate for certain investigation objectives, types, and sources of data, as defined by the authors. Methods: Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies were based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we used principal component analysis (PCA) and meta-analysis. Results: The search strategy based on the PRISMA eligibility criteria provided us with 63 out of 2234 potential articles, 206 observations, 89 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource types. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: “supervised methods, institutional dataset, and predictive and classificatory purposes” (correlation 0.97–8.18 × 10⁻¹; p-value 7.67 × 10⁻⁵⁵–1.28 × 10⁻²²) and the second, Dim2 “not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment” (corr. 0.84–0.47; p-value 5.79 × 10⁻²⁵–-3.59 × 10⁻⁶). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry. Conclusions: The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53–0.96) compared to not-supervised methods. Full article

Acid mine drainage (AMD), resulting from mining activities, poses a significant environmental concern. It adversely affects metallic materials, particularly carbon steel composites used in mining machinery and structures. Highly acidic and oxidizing compounds like sulfuric acid and ferric ions cause corrosion, iron oxide formation, and hydrogen gas release, which degrade carbon steel. AMD also alters the solvent’s properties, dissolving heavy metals and contaminants, and intensifying the environmental impact of mining. A 30-week experiment immersed metal plates in AMD to study its effects. Weekly observations of the plates and solvent were made. The plate measurements and physicochemical data were analyzed using graphical–statistical analysis and fuzzy logic techniques to assess the data quality and identify errors. The results reveal consistent findings with prior studies, such as material degradation with weight loss and alterations in acid drainage media, including increased pH and total dissolved solids (TDS). These changes in the solvent characteristics stem from the dissolution of metal ions from corroded surfaces, reacting with the acid solution. Overall, this study discusses the effects of AMD (acid mine drainage) on metallic materials and emphasizes the significance of monitoring and reducing the environmental consequences of mining activities. Full article

The helical gear pair of a box-type vibration exciter of a mine-used linear vibrating screen is subjected to complex excitation and prone to broken tooth failures. At present, investigation regarding the difference and particularity between gear transmission in vibrating screens (i.e., vibration machinery) and that in rotating machinery is still a challenge, which is the key to revealing the performance and failure mechanism of gear transmission in the premise of application to vibrating screens. In order to intuitively display the peculiarity of gear transmission on the exciter, an innovative virtual prototype model of a gear pair of a vibrating screen exciter is proposed. This model considers the effects of internal and external excitation, such as the friction and lubrication of the gear, strong alternating load produced by a large eccentric block, the reciprocating motion of the screen body, the large clearance of bearing and so on, and its correctness is verified. Based on the comparison, the inducement for the high fatigue rate of exciter gears is revealed. Models of a vibrating screen’s excitation system with different degrees of broken teeth are also established, and tooth fault features are proposed for fault detection. Sensitive indicators for the degradation degree of tooth damage are put forward, and the monitoring strategy is presented that with the increase of damage degree, the waveform index and pulse index of axial vibration acceleration increase. The analysis results provide powerful support for the optimal design of the vibrating screen’s exciter gears and fault diagnosis. Full article