Real-time contrasts control chart using random forests with weighted voting

• Published in: Expert systems with applications 2017-04, Vol.71, p.358-369
• Main Authors: Jang, Seongwon, Park, Seung Hwan, Baek, Jun-Geol
• Format: Article
• Language: English
• Subjects: Change detection; Class imbalance; Control charts; Correlation analysis; Correlation coefficients; Discriminant analysis; Experiments; Fault detection; Fault isolation; Faults; Monitoring; Random forests; Real time; Real-time contrasts (RTC); Statistical methods; Statistics; Support vector machines; Voting; Weighted voting
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2016.12.002

•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. Real-time fault detection and isolation are important tasks in process monitoring. A real-time contrasts (RTC) control chart converts the process monitoring problem into a real-time classification problem and outperforms existing methods. However, the monitoring statistics of the original RTC chart are discrete; this could make the fault detection ability less efficient. To make monitoring statistics continuous, distance-based RTC control charts using support vector machines (SVM) and kernel linear discriminant analysis (KLDA) were proposed. Although the distance-based RTC charts outperformed the original RTC chart, the distance-based RTC charts have a disadvantage in that it is difficult to analyze the causes of faults when using these charts. Therefore, we propose improved RTC control charts using random forests with weighted voting. These improved RTC control charts can detect changes more rapidly by making monitoring statistics continuous; additionally, they can also analyze the causes of faults in a similar manner to the original RTC chart. Further, the improved RTC control charts alleviate the class imbalance problem by using F-measure, G-mean, and Matthews correlation coefficient (MCC) as performance measures to assign proper weights to individual classifiers. Experiments show that the proposed methods outperform the original RTC chart and are more effective than the distance-based RTC charts using SVM and KLDA.