Energy-Fluctuated Multiscale Feature Learning With Deep ConvNet for Intelligent Spindle Bearing Fault Diagnosis

By Xiaoxi Ding, Qingbo He. IEEE Transactions on Instrumentation and Measurement, 2017

412 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/04f9dc91232c705a09ce69f588bd4cc0507ccf8f

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In Control and Systems Engineering

In Mechanical Engineering

Citations in the corpus

Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks (2020, methodology): “Ding and He [20] applied a CNN with an”
Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, neutral): “[110], the authors reported a novel method for both bearing fault type and severity level recognition.”
Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application (2018, neutral): “In [17], the authors investigated different time-frequency methods to convert the 1D vibration signal into time-frequency images, which were then fed into CNN for bearing condition diagnosis.”
Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes (2018, methodology): “Likewise, Ding and He [13] used CNNs to automatically learn”
Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, methodology): “In addition to identifying damages on rolling element bearings, the adoption of CNN on spindle bearings is also discussed in [119], in which the wavelet packet energy of the vibration signal is taken as input.”
A comprehensive review on convolutional neural network in machine fault diagnosis (2020, methodology): “aring [57] [58] [59] / [60] Bearing, gear/bearing, pump Time or frequency domain transform [62] [63] [64] [65] [66] [67] [68] [69] / [61] / [70] [71] [72] Bearing/bearing, gear/gear Wavelet transform [73] [74] [75] [76] [77] Bearing [78] [79] [80] [81] / [82] Gear/gear, rotor, bearing [83] [84] / [8”
1D Convolutional Neural Networks and Applications: A Survey (2019, methodology): “Furthermore, they have been frequently used in processing sequential data including Natural Language Processing and Speech Recognition [34,35] and even 1D signals e.g., vibration [36,37].”
Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network (2019, methodology): “Ding and He [35] further employed this MSCNN and mined effective features for bearing fault diagnosis.”
WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis (2019, methodology): “For 2D-CNN, Ding [26] proposed a multi-scale feature mining method based on wavelet packet energy image and deep CNN for spindle bearing fault diagnosis.”