TY - JOUR
T1 - FWRF
T2 - Mitigating Redundant Features in Fault Diagnosis via Feature Weighted Random Forest
AU - Chen, Shaozhi
AU - Yang, Rui
AU - Zhong, Maiying
AU - Xi, Xiaopeng
AU - Orchard, Marcos E.
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2025/3/12
Y1 - 2025/3/12
N2 - 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.
AB - 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.
KW - Data-driven fault diagnosis
KW - feature selection
KW - feature-weighted-inspired random forest (FWRF)
KW - satellite attitude control system
KW - weighted random sampling
UR - http://www.scopus.com/inward/record.url?scp=105001546094&partnerID=8YFLogxK
U2 - 10.1109/TIM.2025.3550596
DO - 10.1109/TIM.2025.3550596
M3 - Article
AN - SCOPUS:105001546094
SN - 0018-9456
VL - 74
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
M1 - 3519911
ER -