Application of data-driven models to predictive maintenance: Bearing wear prediction at TATA steel
作者：
Highlights：
• We show the potential of using incomplete sensor data to improving predictive maintenance.
• We present an industry application comparing data-driven methods for predictive maintenance.
• We compare the performance of Neural Networks, Partial least squared regression and Random Forest.
• We find that Partial least squared regression predicts the bush wear with a good accuracy.
• We demonstrate techniques for data cleaning in real world large and complex data sets.
摘要
•We show the potential of using incomplete sensor data to improving predictive maintenance.•We present an industry application comparing data-driven methods for predictive maintenance.•We compare the performance of Neural Networks, Partial least squared regression and Random Forest.•We find that Partial least squared regression predicts the bush wear with a good accuracy.•We demonstrate techniques for data cleaning in real world large and complex data sets.
论文关键词：Predictive maintenance,Industry 4.0,Data-driven,Machine learning
论文评审过程：Received 1 September 2020, Revised 29 July 2021, Accepted 30 July 2021, Available online 14 August 2021, Version of Record 2 September 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115699
