FWRF: Mitigating Redundant Features in Fault Diagnosis via Feature Weighted Random Forest

Shaozhi Chen, Rui Yang*, Maiying Zhong*, Xiaopeng Xi, Marcos E. Orchard

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The performance of data-driven fault diagnosis methods is often impeded by irrelevant and redundant information in high-dimensional data. To address this issue, this article proposes a fault diagnosis method based on feature-weighted-inspired random forest (FWRF). In this method, a novel Boruta recursive feature elimination with cross-validation (BRFECV) algorithm is proposed to obtain an optimal feature set with associated weights. Moreover, a novel FWRF architecture is developed, replacing simple random sampling in random forest (RF) with weighted random sampling for feature subset formation. The assignment of weights to each feature is determined based on a specifically designed feature evaluation approach. Consequently, the proposed FWRF method can effectively use feature information while reducing noise interference. Finally, the proposed FWRF-based method is implemented for online fault diagnosis, with the effectiveness of the proposed method validated using satellite attitude control system data from a semi-physical simulation platform. Experimental results demonstrate that the proposed approach exhibits superior practicality and performance in terms of accuracy, running time, and F1-score compared with other methods for fault diagnosis in satellite attitude control systems.

Original languageEnglish
Article number3519911
JournalIEEE Transactions on Instrumentation and Measurement
Volume74
DOIs
Publication statusPublished - 12 Mar 2025

Keywords

  • Data-driven fault diagnosis
  • feature selection
  • feature-weighted-inspired random forest (FWRF)
  • satellite attitude control system
  • weighted random sampling

Fingerprint

Dive into the research topics of 'FWRF: Mitigating Redundant Features in Fault Diagnosis via Feature Weighted Random Forest'. Together they form a unique fingerprint.

Cite this