Sensors | |
Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis | |
Jong-Myon Kim1  Cong Dai Nguyen2  Cheol Hong Kim3  | |
[1] Department of Electrical, Electronics, and Computer Engineering, University of Ulsan, Ulsan 44610, Korea;Faculty of Radio-Electronic Engineering, Le Quy Don Technical University, Hanoi 10000, Vietnam;School of Computer Science and Engineering, Soongsil University, Seoul 06978, Korea; | |
关键词: fault diagnosis; feature extraction; gearbox fault identification; adaptive noise canceling technique; principal component analysis; support vector machine; | |
DOI : 10.3390/s22114091 | |
来源: DOAJ |
【 摘 要 】
Using an adaptive noise canceling technique (ANCT) and distance ratio principal component analysis (DRPCA), this paper proposes a new fault diagnostic model for multi-degree tooth-cut failures (MTCF) in a gearbox operating at inconsistent speeds. To account for background and disturbance noise in the vibration characteristics of gear failures, the proposed approach employs ANCT in the first stage to optimize vibration signals. The ANCT applies an adaptive denoising technique to each basic frequency segment in the whole frequency response of vibrations. Following that, a novel DRPCA is used to extract the discriminating low-dimensional features. The DRPCA initially determines each feature’s relative proximity to fault categories by computing the average Euclidian distance ratio between similar and dissimilar classes. The most discriminatory features with the lowest dimensions are selected, as determined by principal component analysis (PCA). The new DRPCA is created by combining distance ratio–based feature inspection with PCA. The optimal feature set containing the most discriminative features is then fed to the support vector machine classifier to identify multiple failure categories. The experimental results indicate that the proposed model outperforms the state-of-art approaches and offers the highest identification accuracy.
【 授权许可】
Unknown