1DCNN Fault Diagnosis Based on Cubic Spline Interpolation Pooling

The conventional pooling method for processing one-dimensional vibration signals may lead to certain issues, such as weakening and loss of feature information. The present study proposes the cubic spline interpolation pooling method. The method is appropriate for processing one-dimensional signals. The proposed method can transform the pooling problem into a linear fitting problem, use the cubic spline interpolation method with outstanding fitting effects, and calculate the fitting function of the input signals. Moreover, the values of the interpolation points are sequentially taken as the feature value output. Furthermore, the network using the conventional pooling method and the pooling network model proposed in the present study are compared, tested, and analyzed on the constructed simulation signals and the measured bearing dataset. It is concluded that the proposed pooling method can reduce the data dimension while improving the network feature extraction capability and is more appropriate for pooling one-dimensional signals.

[1]  Dong Yu,et al.  Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..

[2]  S. Dyer,et al.  Cubic-spline interpolation. 1 , 2001 .

[3]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[4]  Xiaodong Gu,et al.  Max-Pooling Dropout for Regularization of Convolutional Neural Networks , 2015, ICONIP.

[5]  김정민,et al.  Cubic Spline Interpolation을 이용한 얼굴 영상의 단순화 , 2010 .

[6]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[7]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[8]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[9]  Huan Wang,et al.  A Novel Deeper One-Dimensional CNN With Residual Learning for Fault Diagnosis of Wheelset Bearings in High-Speed Trains , 2019, IEEE Access.

[10]  Rob Fergus,et al.  Stochastic Pooling for Regularization of Deep Convolutional Neural Networks , 2013, ICLR.

[11]  Guiji Tang,et al.  Analysis of Rotor Rubbing Fault Signal Based on Hilbert-Huang Transform , 2009, 2009 International Conference on Measuring Technology and Mechatronics Automation.

[12]  S. R. Smith,et al.  High-resolution alignment of action potential waveforms using cubic spline interpolation. , 1988, Journal of biomedical engineering.

[13]  Andrea J. van Doorn,et al.  The Structure of Locally Orderless Images , 1999, International Journal of Computer Vision.

[14]  Kunihiko Fukushima,et al.  Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position , 1982, Pattern Recognit..

[15]  Tomohiro Nakatani,et al.  Far-field speech recognition using CNN-DNN-HMM with convolution in time , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[16]  Zhihua Wei,et al.  Mixed Pooling for Convolutional Neural Networks , 2014, RSKT.

[17]  Keiji Yanai,et al.  Food image recognition using deep convolutional network with pre-training and fine-tuning , 2015, 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).

[18]  Yann LeCun,et al.  What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[19]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[20]  Sang-Hoon Oh,et al.  Deep CNNs Along the Time Axis With Intermap Pooling for Robustness to Spectral Variations , 2016, IEEE Signal Processing Letters.

[21]  Pei Zheng,et al.  Study on Engine Vibration Signal Collection Method , 2010, 2010 2nd International Conference on Information Engineering and Computer Science.

[22]  Simon Haykin,et al.  GradientBased Learning Applied to Document Recognition , 2001 .

[23]  Moncef Gabbouj,et al.  Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Industrial Electronics.

[24]  Matthew D. Zeiler.  Hierarchical Convolutional Deep Learning in Computer Vision , 2013 .

[25]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[26]  Jian Tang,et al.  Signal Status Recognition Based on 1DCNN and Its Feature Extraction Mechanism Analysis , 2019, Sensors.

[27]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[28]  Jugurta R. Montalvão Filho,et al.  Speech Recognition in Noisy Environments with Convolutional Neural Networks , 2015, 2015 Brazilian Conference on Intelligent Systems (BRACIS).

[29]  Jean Ponce,et al.  A Theoretical Analysis of Feature Pooling in Visual Recognition , 2010, ICML.

[30]  SchmidhuberJürgen.  Deep learning in neural networks , 2015 .

[31]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.