A Study on Deep Learning Application of Vibration Data and Visualization of Defects for Predictive Maintenance of Gravity Acceleration Equipment

Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hypergravity accelerator. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accelerometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The method proposed in this paper was trained with transfer learning, a deep learning model that replaced the VGG19 model with a Fully Connected Layer (FCL) and Global Average Pooling (GAP) by converting the vibration signal into a short-time Fourier transform (STFT) or Mel-Frequency Cepstral Coefficients (MFCC) spectrogram and converting the input into a 2D image. As a result, the model proposed in this paper has seven times decreased trainable parameters of VGG19, and it is possible to quantify the severity while looking at the defect areas that cannot be seen with 1D.

[1]  Kesheng Wang,et al.  Wind turbine fault detection based on SCADA data analysis using ANN , 2014 .

[2]  E A Ilyin,et al.  Study of physiological effects of weightlessness and artificial gravity in the flight of the biosatellite Cosmos-936. , 1980, Acta astronautica.

[3]  Zhongkui Zhu,et al.  Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis , 2011 .

[4]  Agnes Muszynska,et al.  Chaotic responses of unbalanced rotor/bearing/stator systems with looseness or rubs , 1995 .

[5]  Stan Davis,et al.  Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Se , 1980 .

[6]  Mohammad Modarres,et al.  Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings , 2017 .

[7]  Byeong-Keun Choi,et al.  Performance Improvement of Feature-Based Fault Classification for Rotor System , 2020 .

[8]  Gilles Louppe,et al.  Independent consultant , 2013 .

[9]  Lutz Prechelt,et al.  Early Stopping - But When? , 2012, Neural Networks: Tricks of the Trade.

[10]  R. D. Marangoni,et al.  Vibration Analysis Of A Motor-Flexible Coupling-Rotor System Subject To Misalignment And Unbalance, Part I: Theoretical Model And Analysis , 1994 .

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

[12]  Enrico Zio,et al.  Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.

[13]  Walid G. Morsi,et al.  Fault Detection and Classification Based on Co-training of Semisupervised Machine Learning , 2018, IEEE Transactions on Industrial Electronics.

[14]  Zhiwei Wang,et al.  A practical chiller fault diagnosis method based on discrete Bayesian network , 2019, International Journal of Refrigeration.

[15]  Levent Eren,et al.  Bearing Fault Detection by One-Dimensional Convolutional Neural Networks , 2017 .

[16]  Zhehan Yi,et al.  Line-to-Line Fault Detection for Photovoltaic Arrays Based on Multiresolution Signal Decomposition and Two-Stage Support Vector Machine , 2017, IEEE Transactions on Industrial Electronics.

[17]  Jong-Myon Kim,et al.  Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions , 2019, Measurement.

[18]  Hongtao Zhang,et al.  Sensor fault detection and diagnosis for a water source heat pump air-conditioning system based on PCA and preprocessed by combined clustering , 2019, Applied Thermal Engineering.

[19]  Jing Li,et al.  An enhancement denoising autoencoder for rolling bearing fault diagnosis , 2018, Measurement.

[20]  Hassan Elahi,et al.  Vibration Feature Extraction and Analysis for Fault Diagnosis of Rotating Machinery-A Literature Survey , 2017 .

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

[22]  Haidong Shao,et al.  Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing , 2018 .

[23]  Justin Salamon,et al.  Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification , 2016, IEEE Signal Processing Letters.

[24]  Il-Song Kim.  On-line fault detection algorithm of a photovoltaic system using wavelet transform , 2016 .

[25]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[26]  Xiu Yao,et al.  Series DC Arc Fault Detection Based on Ensemble Machine Learning , 2020, IEEE Transactions on Power Electronics.

[27]  Peng Chen,et al.  Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery , 2018, IEEE Transactions on Instrumentation and Measurement.

[28]  Yuyun Zeng,et al.  Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model , 2019, Renewable Energy.

[29]  Jong-Myon Kim,et al.  Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network , 2018 .

[30]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[31]  Diego Cabrera,et al.  Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning , 2016, Sensors.

[32]  Dong Wang,et al.  A Novel Bearing Fault Diagnosis Method Based on Gaussian Restricted Boltzmann Machine , 2016 .

[33]  Haidong Shao,et al.  An enhancement deep feature fusion method for rotating machinery fault diagnosis , 2017, Knowl. Based Syst..

[34]  Soteris A. Kalogirou,et al.  Fault detection and diagnosis methods for photovoltaic systems: A review , 2018, Renewable and Sustainable Energy Reviews.

[35]  Frank Hutter,et al.  SGDR: Stochastic Gradient Descent with Warm Restarts , 2016, ICLR.

[36]  Jing Lin,et al.  Deep Coupled Dense Convolutional Network With Complementary Data for Intelligent Fault Diagnosis , 2019, IEEE Transactions on Industrial Electronics.

[37]  Haidong Shao,et al.  A novel deep autoencoder feature learning method for rotating machinery fault diagnosis , 2017 .

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

[39]  Youngjun Yoo,et al.  Fault Detection Method Using Multi-mode Principal Component Analysis Based on Gaussian Mixture Model for Sewage Source Heat Pump System , 2019, International Journal of Control, Automation and Systems.