Construction of a batch-normalized autoencoder network and its application in mechanical intelligent fault diagnosis

[1]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[2]  Shunming Li,et al.  An intelligent fault diagnosis framework for raw vibration signals: adaptive overlapping convolutional neural network , 2018, Measurement Science and Technology.

[3]  Fulei Chu,et al.  Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples , 2013 .

[4]  Jay Lee,et al.  Degradation Assessment and Fault Modes Classification Using Logistic Regression , 2005 .

[5]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[6]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.

[7]  Lu Weining,et al.  Fault diagnosis method study in roller bearing based on wavelet transform and stacked auto-encoder , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).

[8]  Hongmei Liu,et al.  Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals , 2016 .

[9]  Changqing Shen,et al.  A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines , 2019, Mechanical Systems and Signal Processing.

[10]  V. Sugumaran,et al.  A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis , 2012, Appl. Soft Comput..

[11]  Ruqiang Yan,et al.  A sparse auto-encoder-based deep neural network approach for induction motor faults classification , 2016 .

[12]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[13]  Changqing Shen,et al.  Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery , 2017, IEEE Access.

[14]  Yourong Li,et al.  A selective fuzzy ARTMAP ensemble and its application to the fault diagnosis of rolling element bearing , 2016, Neurocomputing.

[15]  Wenliao Du,et al.  Wavelet leaders multifractal features based fault diagnosis of rotating mechanism , 2014 .

[16]  Yu Xue,et al.  Text classification based on deep belief network and softmax regression , 2016, Neural Computing and Applications.

[17]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[18]  J. Lin,et al.  Fault diagnosis of rolling bearings using multifractal detrended fluctuation analysis and Mahalanobis distance criterion , 2012, 18th International Conference on Automation and Computing (ICAC).

[19]  Liang Guo,et al.  Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring , 2016 .

[20]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[21]  Joachim Denzler,et al.  ImageNet pre-trained models with batch normalization , 2016, ArXiv.

[22]  Liang Guo,et al.  A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines , 2018, Neurocomputing.

[23]  Roland Memisevic,et al.  Zero-bias autoencoders and the benefits of co-adapting features , 2014, ICLR.

[24]  Zhu Huijie,et al.  Fault diagnosis of hydraulic pump based on stacked autoencoders , 2015, 2015 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI).

[25]  Guolin He,et al.  Semisupervised Distance-Preserving Self-Organizing Map for Machine-Defect Detection and Classification , 2013, IEEE Transactions on Instrumentation and Measurement.

[26]  Yaguo Lei,et al.  Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization , 2018, Mechanical Systems and Signal Processing.

[27]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .