Sürdürülebilir Kalkınma Amaçları
Yayınlar
- BildiriYalnız künye2021Sequential Pattern Mining Method for Predictive Maintenance of Large Mining Trucks
Kahraman, Abdulgani; Kantardzic, Mehmed; Kahraman, M. Mustafa; Kotan, Muhammed; Hasic Telalovic, J.; Kantardzic, M.
In recent decades, various solutions had been sought for reducing operating costs while increasing the production of minerals in mining operations. Equipment health monitoring technologies had been used for monitoring and increasing the availability of machines. However, the data obtained from these technologies had only been used for monitoring the equipment health, and not for the prediction of failures. In this paper, it was relied on alarms and signals collected through real-time health monitoring technologies for predicting crucial mining truck failures. Sequential Pattern Mining (SPM) Method for Predictive Maintenance had been developed and implemented as a methodology to discover which group of alarms and signals might be related to specific truck failures. The results indicate that the SPM method is able to detect machine failures of trucks with high accuracy with an average 96%. The proposed methodology may reduce the maintenance time, and the expenditures caused by truck breakdowns in the mining industry.
- MakaleAçık Erişim2021A data-driven multi-regime approach for predicting energy consumption
Kahraman, Abdulgani; Kantardzic, Mehmed; Kahraman, Mustafa; Kotan, Muhammed
There has been increasing interest in reducing carbon footprints globally in recent years. Hence increasing share of green energy and energy efficiency are promoted by governments. There-fore, optimizing energy consumption is becoming more critical for people, companies, industries, and the environment. Predicting energy consumption more precisely means that future energy management planning can be more effective. To date, most research papers have focused on predicting residential building energy consumption; however, a large portion of the energy is consumed by industrial machines. Prediction of energy consumption of large industrial machines in real time is challenging due to concept drift, in which prediction performance deteriorates over time. In this research, a novel data-driven method multi-regime approach (MRA) was developed to better predict the energy consumption for industrial machines. Whereas most papers have focused on finding an excellent prediction model that contradicts the no-free-lunch theorem, this study concentrated on adding potential concept drift points into the prediction process. A real-world dataset was collected from a semi-autonomous grinding (SAG) mill used as a data source, and a deep neural network was utilized as a prediction model for the MRA method. The results proved that the MRA method enables the detection of multi-regimes over time and provides a highly accurate prediction performance, thanks to the dynamic model approach.