{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T15:09:21Z","timestamp":1777129761454,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T00:00:00Z","timestamp":1674604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019 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\u2019 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\u2019 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\u2019 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.<\/jats:p>","DOI":"10.3390\/s23031331","type":"journal-article","created":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T03:53:18Z","timestamp":1674618798000},"page":"1331","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Haulage Cycles Identification for Wheeled Transport in Underground Mine Using Neural Networks"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1253-3149","authenticated-orcid":false,"given":"Artur","family":"Skoczylas","sequence":"first","affiliation":[{"name":"KGHM Cuprum Research and Development Centre Ltd., Gen W. Sikorskiego 2-8, 53-659 Wroclaw, Poland"},{"name":"Faculty of Management, Wroclaw University of Economics and Business, Komandorska 118\/120, 53-345 Wroclaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7281-8253","authenticated-orcid":false,"given":"Artur","family":"Rot","sequence":"additional","affiliation":[{"name":"Faculty of Management, Wroclaw University of Economics and Business, Komandorska 118\/120, 53-345 Wroclaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pawe\u0142","family":"Stefaniak","sequence":"additional","affiliation":[{"name":"KGHM Cuprum Research and Development Centre Ltd., Gen W. Sikorskiego 2-8, 53-659 Wroclaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pawe\u0142","family":"\u015aliwi\u0144ski","sequence":"additional","affiliation":[{"name":"KGHM Polska-Mied\u017a S.A., M. Sk\u0142odowskiej-Curie 48, 59-301 Lubin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,25]]},"reference":[{"key":"ref_1","first-page":"01002","article-title":"Industry 4.0 in development of new technologies for underground mining","volume":"Volume 174","author":"Paczesny","year":"2020","journal-title":"E3S Web of Conferences, Proceedings of the Vth International Innovative Mining Symposium, Kemerovo, Russia, 19\u201321 October 2020"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Reis, M.S., and Gins, G. (2017). Industrial process monitoring in the big data\/industry 4.0 era: From detection, to diagnosis, to prognosis. Processes, 5.","DOI":"10.3390\/pr5030035"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Skoczylas, A., Stefaniak, P., Anufriiev, S., and Jachnik, B. (2021). Belt conveyors rollers diagnostics based on acoustic signal collected using autonomous legged inspection robot. 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