Sequential Pattern Mining Method for Predictive Maintenance of Large Mining Trucks

Kahraman, Abdulgani; Kantardzic, Mehmed; Kahraman, Muhammet Mustafa; Kotan, Muhammed

doi:10.1007/978-3-030-72805-2_9

Cited by 1 publication

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accurately predicting the energy consumption of industrial machines is a complex process even for experts in the field [38]. The precision of a forecast degrades because any system experiences unforeseen changes in machine running conditions.…”

Section: Methodsmentioning

confidence: 99%

“…Operator mistakes such as a lack of maintenance, aggressive running of machines, poor quality of materials, excessive speed, and high pressure can all have a significant impact on machines' power usage. However, most real-world industrial streaming data do not include all these changes in machine operations [38], [40]. In order to make a more accurate prediction of energy usage, the issue of uncertainty or unknown dynamics in streaming data must first be addressed [8], [41].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Modeling With Integrated Concept Drift Detection for Predicting Real-Time Energy Consumption of Industrial Machines

2022

Self Cite

View full text Add to dashboard Cite

Industrial machinery is a significant energy consumer, and its CO 2 emissions have increased dramatically in recent years. Therefore, energy efficiency is becoming crucial for businesses, governments, as well as the planet. Estimating the power consumption of industrial machines with greater accuracy assists management and optimizes machine operation parameters. Real-time industrial machine datasets present several challenges, such as changes in the data over time, unknown running conditions, missing data, etc. Most research publications focus on the accuracy of traditional static models of forecasting; however, prediction performance deteriorates over time because data evolves. We implemented deep learning as a prediction model for three distinct real-world industrial datasets. The proposed method, dynamic modeling with memory (DMWM), improved overall prediction performance compared with conventional approaches by identifying concept drifts and optimizing the number of required models in response to industrial datasets' recurring machine energy consumption patterns.

show abstract

Section: Methodsmentioning

confidence: 99%