Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry
作者：
Highlights：
• Data analysis lead to a number of insights on the meaning of the features and how they describe machine behaviour.
• Some rules were derived from the associations found during the data analysis and were consolidated in a rule-based model.
• The rule-based model will be used to complement predictive maintenance models in the future.
• Combining feature selection methods with the data analysis insights helped reduce the feature space from 47 to 32 features.
摘要
•Data analysis lead to a number of insights on the meaning of the features and how they describe machine behaviour.•Some rules were derived from the associations found during the data analysis and were consolidated in a rule-based model.•The rule-based model will be used to complement predictive maintenance models in the future.•Combining feature selection methods with the data analysis insights helped reduce the feature space from 47 to 32 features.
论文关键词：Predictive maintenance,Data analysis,Feature selection,Rule-based model
论文评审过程：Received 8 May 2018, Revised 10 October 2018, Accepted 11 October 2018, Available online 30 October 2018, Version of Record 20 March 2019.
论文官网地址：https://doi.org/10.1016/j.ijinfomgt.2018.10.006
