A Simple Method of the Haulage Cycles Detection for LHD Machine

Koperska, Wioletta; Skoczylas, Artur; Stefaniak, Paweł

doi:10.1007/978-3-030-63119-2_27

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…For bucket loaders, the HYDOILP variable is more informative and allows for detection of each of the work cycle components. This estimation can be performed using the algorithm presented in [ 4 ], but we decided to use a modified (shortened) version of it. The cycle detection is performed by finding the local extrema moments on the convolution signal between the HYDOILP variable and the unit jump function.…”

Section: Methodsmentioning

confidence: 99%

“…The easiest way to detect the haulage cycles is to monitor the hydraulic pressure measured at the hydraulic cylinder (bucket/cargo box), where the cycles components are clearly visible. Such an approach was presented by the authors of [ 4 ], where repeated convolution with reversed unit step were used to detect work cycles along with cycles components for loaders. The algorithm showed an accuracy of 96.3% for two cycle components (driving empty/full) and 67.4% for the third (unloading).…”

Section: Introductionmentioning

confidence: 99%

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Haulage Cycles Identification for Wheeled Transport in Underground Mine Using Neural Networks

Skoczylas

Rot

Stefaniak

et al. 2023

Sensors

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The task of ore transportation is performed in all mines, regardless of their type (open pit/underground) or mining process. A substantial number of enterprises utilize wheeled machines to perform ore haulage, especially haul trucks and loaders. These machines’ work consists of repeating cycles, and each cycle can be divided into 4 operations: loading, driving with full box/bucket, unloading and driving with empty box/bucket. Monitoring this process is essential to create analytical tools that support foremen and other management crew in achieving effective and optimal production and planning activities. Unfortunately, information gathered regarding the process is frequently based on operators’ oral testimony. This process not only allows for abuse but is also a repetitive and tedious task that must be performed by foremen. The time and attention of foremen is valuable as they are responsible for managing practically everything in their current mine section (machines, operators, works, repairs, emergencies, safety, etc.). Therefore, the automatization of the described process of information gathering should be performed. In this article, we present two neural network models (one for haul trucks and one for loaders) build for detecting work cycles of the ore haulage process. Both models were built utilizing a 2-stage approach. In the first stage, the models’ structures were optimized, while the second was focused on optimizing hyperparameters for the structure with best performance. Both of the proposed models were trained using data collected from on-board monitoring systems over hundreds of the machines’ work hours and utilized the same input features: vehicle speed, fuel consumption, selected gear and engine rotational speed. Models have been subjected to comprehensive testing during which the efficiency and stability of the model responsible for haul trucks was proven. Results for loaders were not as high quality for haul trucks; however, some interesting facts were discovered that indicate possible directions for future development.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Haulage Cycles Identification for Wheeled Transport in Underground Mine Using Neural Networks

Skoczylas

Rot

Stefaniak

et al. 2023

Sensors

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“…1) It initializes variables for the length of the signal (n) and an array representing time (t). 2) Finds local maxima indices in the signal using SciPy's argrelextrema function [53], [54]. 3) Creates an interpolator using cubic interpolation between identified extrema.…”

Section: B Utilization Of Fractional Derivativesmentioning

confidence: 99%

Detecting of Robotic Imitation of Human on-the-Website Activity With Advanced Vector Analysis and Fractional Derivatives

Malashin,

Tynchenko,

Gantimurov

et al. 2024

IEEE Access

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This paper introduces a novel approach for the detection of automated entities in online environments through the analysis of mouse dynamics. Leveraging fractional derivatives and vector cross products, our methodology scrutinizes the intricate patterns embedded in mouse movements. Fractional derivatives capture the non-integer order dynamics, while vector cross products reveal deviations from expected trajectories. The combination of these advanced mathematical tools offers a unique perspective on distinguishing between human and bot behaviors. We present experimental results showcasing the efficacy of our approach in various scenarios, demonstrating its potential in the realm of cybersecurity and online integrity. Our findings contribute to the evolving landscape of bot detection methodologies, emphasizing the importance of incorporating mathematical rigor in the analysis of digital behavior.

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“…In 2016, Polak et al 7 found that the bucket jitter is more noticeable when the LHD is loading, so, based on these characteristics, they subdivided the heavy load working condition into loading and hauling with the help of statistical analytical tools. In 2020, Koperska et al 8,9 proposed a convolution of the smoothed signal with an inverted step function to reduce signal interference and then applied this method to recognize loading, hauling, and dumping. In addition, Wodecki et al 10 published a short review of smoothing methods for the hydraulic signal from the LHD.…”

Section: Introductionmentioning

confidence: 99%

The load-haul-dump operation cycle recognition based on multi-sensor feature selection and bidirectional long short-term memory network

Meng

et al. 2023

Measurement and Control

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The operational cycle identification of the load-haul-dump (LHD) can help support the production process optimization in the underground mining industry and thus reduce mining costs. However, most of the existing research works use only the hydraulic bucket signal of LHD as the data source, and the stability and robustness of the identification method are poor. A few advanced research works use the variational Bayesian Gaussian mixture model to introduce other signals, but the accuracy of this recognition method is not perfect at present. In addition, the current identification methods are unable to simultaneously recognize the four working conditions of the LHD which include loading, hauling, dumping, and transiting. To solve these problems, a random forest feature selection (RFFS) and bidirectional long short-term memory (Bi-LSTM) based operation cycle recognition algorithm is proposed. Firstly, RFFS is used to remove redundant features based on the multi-sensor signals of the LHD. Then, Bi-LSTM is applied to fully exploit the temporal correlation between different operation regimes and accurately recognize the operation cycles. The effectiveness and superiority of the algorithm are verified by the experiment on the actual data of the LHD. The proposed algorithm can recognize four working conditions simultaneously, among which the recognition accuracy of loading conditions is the highest, up to 95.42%, and the weighted accuracy of this algorithm can reach 91.75% using the occupied time of each working condition as the weighting factor.

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A Simple Method of the Haulage Cycles Detection for LHD Machine

Cited by 5 publications

References 8 publications

Haulage Cycles Identification for Wheeled Transport in Underground Mine Using Neural Networks

Haulage Cycles Identification for Wheeled Transport in Underground Mine Using Neural Networks

Detecting of Robotic Imitation of Human on-the-Website Activity With Advanced Vector Analysis and Fractional Derivatives

The load-haul-dump operation cycle recognition based on multi-sensor feature selection and bidirectional long short-term memory network

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