Recent advances in remote coal mining machine sensing, guidance, and 

teleoperation

Ralston, Jonathon; Hainsworth, D.W.; Reid, David; Anderson, Denis L.; McPhee, R.J.

doi:10.1017/s0263574701003447

Cited by 23 publications

(12 citation statements)

References 7 publications

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“…If the interface is not within the specified depth, more coal can be extracted. The overall benefits are improved coal quality, increased mining productivity and enhanced worker safety [3].…”

Section: Introductionmentioning

confidence: 99%

“…The reactive sensors are limited as the miner has already cut into the low quality coal and surrounding strata when the interface is detected which damages the machinery and dilutes the coal. Some examples of reactive sensors are monitoring cutting drum current, sensing changes in machine vibration signatures and instrumented cutting picks [3]. Predictive approaches however sense the remnant coal thickness before it is mined and thus allow for optimal mining.…”

Section: Introductionmentioning

confidence: 99%

“…The most common predictive technique uses a natural gamma radiation sensor to estimate the coal thickness by exploiting the radioactivity of host strata found with many coal deposits. The efficacy of this technique is highly site specific as well as requiring long processing delays (up to 30 seconds) to obtain a reliable depth estimate [3]. This time delay can impact significantly coal mining production rates.…”

Section: Introductionmentioning

confidence: 99%

“…Jha et al [13] used stepped frequency GPR to map barrier thickness in coal mines. Ralston and Hainsworth [1,3] investigated coal seam thickness estimation using impulse GPR with promising results. Preliminary research has shown that using GPR as a horizon control sensor is possible and the key to the final solution is real-time intelligent data processing [14].…”

Section: Introductionmentioning

confidence: 99%

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Near-surface Interface Detection for Coal Mining Applications using Bispectral Features and GPR

Strange

Ralston

Chandran

2005

Subsurf Sens Technol Appl

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The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coalclay, coal-shale and shale-clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range. KEY WORDSGPR, bispectrum, interface detection, horizon control, coal mining.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Near-surface Interface Detection for Coal Mining Applications using Bispectral Features and GPR

Strange

Ralston

Chandran

2005

Subsurf Sens Technol Appl

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“…One important application for GPR layer thickness estimation is horizon control sensing in underground coal mining [4]. In [1], GPR was used to detect coal seam interfaces within 5 cm.…”

Section: Introductionmentioning

confidence: 99%

Coal seam thickness estimation using GPR and higher order statistics - the near-surface case

Strange¹,

Chandran²,

Ralston³

Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005.

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A novel pattern recognition-based approach to detect near-surface interfaces using ground penetrating radar (GPR) has been reported in [1]. The approach was used to successfully detect interfaces within 5 cm of the ground surface. This technique has been adapted for the important task of layer thickness estimation in the nearsurface range. This is inherently a difficult problem to solve in practice because the radar echo is often dominated by unwanted components such as antenna crosstalk and ring-down, ground reflection effects and clutter. Features derived from the bispectrum and a nearest-neighbour classifier have been utilized for this processing task. It is shown that unlike traditional second order correlation based methods such as matched filtering which can fail in known conditions, layer thickness estimation using this approach can be reliably extended to the near-surface region.

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Development and applications of rescue robots for explosion accidents in coal mines

Zhu

et al. 2019

Journal of Field Robotics

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It is essential to provide disaster relief assistance after coal mine explosions. Often, it is life‐threatening for rescuers to enter an accident scene blindly; therefore, a coal mine rescue robot (CMRR) has been developed. However, the application of the CMRR has not proven satisfactory after decades of development. To solve this problem, we summarize the reasons for this disappointing state and address the technical challenges of the CMRR. Based on these reasons and the associated technical challenges, two generations of tracked robots have been developed. The China University of Mining Technology‐V (CUMT‐V) (A) robot was first developed and its walking system, body support system, communication system, environmental awareness system, and control system are described in detail. A performance test was performed on the CUMT‐V (A) robot and some problems were encountered. To address these problems, we designed the CUMT‐V (B) robot. The field test was conducted in Shanxi province, China, in August 2016. The application results show that the robot has good adaptability to complex terrain and high reliability in terms of environmental awareness and data transmission. In conclusion, the robot is nearing practical applications.

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Recent advances in remote coal mining machine sensing, guidance, and teleoperation

Cited by 23 publications

References 7 publications

Near-surface Interface Detection for Coal Mining Applications using Bispectral Features and GPR

Near-surface Interface Detection for Coal Mining Applications using Bispectral Features and GPR

Coal seam thickness estimation using GPR and higher order statistics - the near-surface case

Development and applications of rescue robots for explosion accidents in coal mines

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