Haul Truck Cycle Identification Using Support Vector Machine and DBSCAN Models

Gawelski, Dawid; Jachnik, Bartosz; Stefaniak, Paweł; Skoczylas, Artur

doi:10.1007/978-3-030-63119-2_28

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…In practice, the collection of information in the course of the machine operation and maintenance process has a wide analytical application [ 17 , 18 ]. It allows one to calculate not only mine performance indicators [ 19 ], analyze context awareness in predictive maintenance [ 20 ], but also machines and operators [ 21 , 22 ]. It can also be used for building complex reliability models for machines and their parts [ 23 , 24 , 25 ], assessing the service life of selected manufacturers’ parts and estimating downtime or residual life-time of machinery [ 26 , 27 ].…”

Section: Mes and Erp Systems Used In Examined Casementioning

confidence: 99%

Application of Wearable Computer and ASR Technology in an Underground Mine to Support Mine Supervision of the Heavy Machinery Chamber

Stefaniak

Stachowiak

Koperska

et al. 2022

Sensors

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Systems that use automatic speech recognition in industry are becoming more and more popular. They bring benefits especially in cases when the user’s hands are often busy or the environment does not allow the use of a keyboard. However, the accuracy of algorithms is still a big challenge. The article describes the attempt to use ASR in the underground mining industry as an improvement in the records of work in the heavy machinery chamber by a foreman. Particular attention was paid to the factors that in this case will have a negative impact on speech recognition: the influence of the environment, specialized mining vocabulary, and the learning curve. First, the foreman’s workflow and documentation were recognized. This allowed for the selection of functionalities that should be included in the application. A dictionary of specialized mining vocabulary and a source database were developed which, in combination with the string matching algorithms, aim to improve correct speech recognition. Text mining analysis, machine learning methods were used to create functionalities that provide assistance in registering information. Finally, the prototype of the application was tested in the mining environment and the accuracy of the results were presented.

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Section: Mes and Erp Systems Used In Examined Casementioning

confidence: 99%

Application of Wearable Computer and ASR Technology in an Underground Mine to Support Mine Supervision of the Heavy Machinery Chamber

Stefaniak

Stachowiak

Koperska

et al. 2022

Sensors

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“…Unfortunately, the authors did not provide efficiency metrics. A different approach was presented in [ 13 ], where authors used machine speed (smoothed with moving average), engine rotational speed and an artificial logic signal created from merging the two. Those three signals were then used to feed the Support Vector Machine (SVM) algorithm along with the DBSCAN algorithm.…”

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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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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“…These works are excellent, but the signals they used are greatly affected by driving habits, so the stability and robustness of this algorithm are poor. In 2020, Gawelski et al 17 argued that the application of multidimensional sensor data for the recognition of truck cycle conditions could avoid the effects caused by the loss of hydraulic signals and proposed a corresponding method for the recognition of operating cycles. In the same year, Wodecki et al 18 also proposed a multidimensional data technique based on current and pressure signals for recognizing the operation cycles of heavy drilling rigs.…”

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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Haul Truck Cycle Identification Using Support Vector Machine and DBSCAN Models

Cited by 7 publications

References 15 publications

Application of Wearable Computer and ASR Technology in an Underground Mine to Support Mine Supervision of the Heavy Machinery Chamber

Application of Wearable Computer and ASR Technology in an Underground Mine to Support Mine Supervision of the Heavy Machinery Chamber

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

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

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