Dissecting a data-driven prognostic pipeline: A powertrain use case
作者：
Highlights：
• Thorough preprocessing steps to cope with the limited on board resources.
• Classifier selection for deployment must evaluate different stability matrices.
• Pipeline validation using real engine data from a dedicated test bench environment.
• Data driven predictive maintenance offers satisfying performance for prognostic.
• Mismatch matrix as novel visual representation of classification results.
摘要
•Thorough preprocessing steps to cope with the limited on board resources.•Classifier selection for deployment must evaluate different stability matrices.•Pipeline validation using real engine data from a dedicated test bench environment.•Data driven predictive maintenance offers satisfying performance for prognostic.•Mismatch matrix as novel visual representation of classification results.
论文关键词：Predictive maintenance,Automotive,Machine learning,Classification,SVM,Neural network
论文评审过程：Received 10 March 2020, Revised 21 November 2020, Accepted 21 April 2021, Available online 26 April 2021, Version of Record 7 May 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115109
