Maximal information coefficient-based two-stage feature selection method for railway condition monitoring

Research output: Contribution to journalArticlepeer-review


External organisations

  • School of Automation, Hangzhou Dianzi University


In railway condition monitoring, feature classification is a very critical step, and the extracted features are used to classify the types and levels of the faults. To achieve better accuracy and efficiency in the classification, the extracted features must be properly selected. In this paper, maximal information coefficient is employed in two different stages to establish a new feature selection method. By using this proposed two-stage feature selection method, strong features with low redundancy are reserved as the optimal feature subset, which results in the classification process having a more moderate computational cost and good overall performance. To evaluate this proposed two-stage selection method and prove its advantages over others, a case study focusing on the rolling bearing is carried out. The result shows that the proposed selection method can achieve a satisfactory overall classification performance with low-computational cost.


Original languageEnglish
Pages (from-to)2681-2690
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number7
Early online date21 Jan 2019
Publication statusPublished - 1 Jul 2019


  • railway condition monitoring, maximal information coefficient, feature selection, bearing fruit, feature extraction, microwave integrated circuits, wavelet analysis, correlation, wavelet packets, rail transportation, time-frequency analysis