Drilling Operations Classification Utilizing Data Fusion and Machine Learning Techniques

Autor:	Hesam Jafarian, Marzieh Zare, Jussi-Pekka Lehtinen, Ari Visa, Liisa Aha
Rok vydání:	2021
Předmět:	Artificial neural network Computer science business.industry Machine learning computer.software_genre Fault (power engineering) Sensor fusion Automation Fault detection and isolation Mining industry Damages Artificial intelligence business computer
Zdroj:	Proceedings of the International Neural Networks Society ISBN: 9783030805678 EANN
DOI:	10.1007/978-3-030-80568-5_32
Popis:	Varieties of impediments expose the Mining industry to unexpected equipment failures, high commodity price damages, and various environmental difficulties and safety challenges. Therefore, we are looking for an appropriate approach to mitigate the cost of the sudden fault of Mining operations by increasing the automation level of monitoring and operating systems. To attain this goal, we utilize Machine Learning models and data fusion techniques on measured data values. As a result, we are capable of classifying the five diverse mining operations. The outcome of the method presented in this paper assists in the early fault detection of mining operations. This approach can support fault detection and prevention procedures. Besides, it leads to acceleration and enhancement of the maintenance and repair operations by simplifying the required query time.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a262e32a0a685a434307c490352d61af https://doi.org/10.1007/978-3-030-80568-5_32 Zobrazit plný text záznamu