@article{(Open Science Index):https://publications.waset.org/pdf/10010577,
	  title     = {Remaining Useful Life Estimation of Bearings Based on Nonlinear Dimensional Reduction Combined with Timing Signals},
	  author    = {Zhongmin Wang and  Wudong Fan and  Hengshan Zhang and  Yimin Zhou},
	  country	= {},
	  institution	= {},
	  abstract     = {In data-driven prognostic methods, the prediction
accuracy of the estimation for remaining useful life of bearings
mainly depends on the performance of health indicators, which
are usually fused some statistical features extracted from vibrating
signals. However, the existing health indicators have the following
two drawbacks: (1) The differnet ranges of the statistical features
have the different contributions to construct the health indicators,
the expert knowledge is required to extract the features. (2) When
convolutional neural networks are utilized to tackle time-frequency
features of signals, the time-series of signals are not considered.
To overcome these drawbacks, in this study, the method combining
convolutional neural network with gated recurrent unit is proposed to
extract the time-frequency image features. The extracted features are
utilized to construct health indicator and predict remaining useful life
of bearings. First, original signals are converted into time-frequency
images by using continuous wavelet transform so as to form the
original feature sets. Second, with convolutional and pooling layers
of convolutional neural networks, the most sensitive features of
time-frequency images are selected from the original feature sets.
Finally, these selected features are fed into the gated recurrent unit
to construct the health indicator. The results state that the proposed
method shows the enhance performance than the related studies which
have used the same bearing dataset provided by PRONOSTIA.},
	    journal   = {International Journal of Electronics and Communication Engineering},
	  volume    = {13},
	  number    = {7},
	  year      = {2019},
	  pages     = {484 - 491},
	  ee        = {https://publications.waset.org/pdf/10010577},
	  url   	= {https://publications.waset.org/vol/151},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 151, 2019},
	}