Smart augmented reality with AI service expert for world's industries

Madeleine, Madeleine; Martinsen, Madeleine; Zhou, Yihong; Dahlquist, Erik; Yan, Jinyue; Kyprianidis, Konstantinos

doi:10.46855/energy-proceedings-9449

Cited by 1 publication

(2 citation statements)

References 9 publications

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“…For instance, in timestep 2, the concentration in volume element 1 was 0.5. Equation (3) shows how it can be simulated. The value in a certain position depends on the effect of ventilation, vehicle movement, and the behavior This is a general solution being shown in Figure 9 and not the absolute values.…”

Section: Simulated Datamentioning

confidence: 99%

“…In terms of energy consumption, the ventilation of underground mining accounts for around 49% [2], which corresponds to approximately 1 million tons of CO 2 equivalents of Sweden's annual total of approximately 50 million tons of CO 2 equivalents [3]. In alignment with the Paris Agreement, the Swedish government has established a target to achieve a fossil-free status by the year 2045.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Holistic Approach Promotes Failure Prevention of Smart Mining Machines Based on Bayesian Networks

Martinsen,

Fentaye,

Dahlquist

et al. 2023

Machines

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In the forthcoming era of fully autonomous mining, spanning from drilling operations to port logistics, novel approaches will be essential to pre-empt hazardous situations in the absence of human intervention. The progression towards complete autonomy in mining operations must have meticulous approaches and uncompromised security. By ensuring a secure transition, the mining industry can navigate the transformative shift towards autonomy while upholding the highest standards of safety and operational reliability. Experiments involving autonomous pathways for mining machinery that utilize AI for route optimization demonstrate a higher speed capacity than manually operated approaches; this translates to enhanced productivity, subsequently fostering increased production capacity to meet the rising demand for metals. Nonetheless, accelerated wear on crucial elements like tires, brakes, and bearings on mining machines has been observed. Autonomous mining processes will require smarter machines without humans that guide and support actions prior to a hazardous situation occurring. This paper will delve into a comprehensive perspective on the safety of autonomous mining machines by using Bayesian networks (BN) to detect possible hazard fires. The BN is tuned with a combination of empirical field data and laboratory data. Various faults have been recognized, and their correlation with the measurements has been established.

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Section: Simulated Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%