چکیده مقاله
Predictive Maintenance integrates time series forecasting and data analysis techniques to predict equipment failures through continuous monitoring and data collection This study investigates the enhancement of fault prediction using the Long Short Term Memory LSTM deep learning technique combined with a decision tree algorithm for optimizing time lag Experimental data analysis compares predictive maintenance models with and without additional features, revealing a significant 97 05% improvement in accuracy when incorporating these additional features The decision tree algorithm efficiently identifies a near optimal lag in a short timeframe, highlighting the enhanced performance of LSTM model in optimizing fault prediction models
کلیدواژهها
نویسندگان
Mansoureh Naderipour
Department of Industrial Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Kiyan Khaleghi
Department of Industrial Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
شیوه ارجاع
Naderipour, Mansoureh and Khaleghi, Kiyan,1403,Predictive Maintenance using LSTM Network Model Optimized Time-Lag with Decision Tree,The 10th International Conference on Industrial and Systems Engineering,Mashhad
ارائهشده در
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