Incremental Weighted Support Vector Data Description Method for Incipient Fault Detection of Rolling Bearing
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[1] Ruqiang Yan,et al. Machine health diagnosis based on approximate entropy , 2004, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510).
[2] Yaguo Lei,et al. Machinery health prognostics: A systematic review from data acquisition to RUL prediction , 2018 .
[3] Robert P. W. Duin,et al. Support Vector Data Description , 2004, Machine Learning.
[4] Gang Yin,et al. Online fault diagnosis method based on Incremental Support Vector Data Description and Extreme Learning Machine with incremental output structure , 2014, Neurocomputing.
[5] Fuchun Sun,et al. Building feature space of extreme learning machine with sparse denoising stacked-autoencoder , 2016, Neurocomputing.
[6] Jun-Geol Baek,et al. Density weighted support vector data description , 2014, Expert Syst. Appl..
[7] Lovekesh Vig,et al. Long Short Term Memory Networks for Anomaly Detection in Time Series , 2015, ESANN.
[8] Mahmood Fathy,et al. Adversarially Learned One-Class Classifier for Novelty Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[9] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[10] Hai Qiu,et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .
[11] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[12] Jieping Ye,et al. Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization , 2009, UAI.
[13] Yu Cheng,et al. Early Fault Detection Approach With Deep Architectures , 2018, IEEE Transactions on Instrumentation and Measurement.