Real-time contrasts control chart using random forests with weighted voting
作者：
Highlights：
• We propose RTC control charts using random forests with weighted voting.
• F-measure, G-mean, and MCC are used as performance measures to assign proper weights.
• Our method detects faults more rapidly by making monitoring statistics continuous.
• Our method can identify where the fault occurs because tree-based classifier is used.
• Experiments demonstrated that our method is more effective than the existing methods.
摘要
•We propose RTC control charts using random forests with weighted voting.•F-measure, G-mean, and MCC are used as performance measures to assign proper weights.•Our method detects faults more rapidly by making monitoring statistics continuous.•Our method can identify where the fault occurs because tree-based classifier is used.•Experiments demonstrated that our method is more effective than the existing methods.
论文关键词：Real-time contrasts (RTC),Fault detection,Fault isolation,Random forests,Weighted voting,Class imbalance
论文评审过程：Received 1 August 2016, Revised 18 November 2016, Accepted 2 December 2016, Available online 3 December 2016, Version of Record 10 December 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.12.002
