Convolutional Neural Network Based Fault Detection for Rotating Machinery

By Olivier Janssens, Viktor Slavkovikj, B. Vervisch, K. Stockman, M. Loccufier, S. Verstockt, R. Walle, Sofie Van Hoecke. 2016

1,173 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/d7135fea4c5cea6afe7dfd9c628894c3a37d5b02

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In Control and Systems Engineering

In Mechanical Engineering

Citations in the corpus

1-D Convolutional Neural Networks for Signal Processing Applications (2019, methodology): “Another technique was used in [5] where two vibration signals were measured using two accelerometers.”
Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, methodology): “The first paper employing CNN to identify bearing fault was published in 2016 [103], and in the next three years many papers applying the same technique [104]–[118] have emerged and contributed to advancing bearing fault detection in various aspects.”
An enhancement deep feature fusion method for rotating machinery fault diagnosis (2017, methodology): “In addition, CNN has some excellent properties such as weight sharing and shift-invariant for complex data feature learning [21, 36].”
Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, methodology): “The first paper employing CNN to identify bearing fault was published in 2016 [103], and in the next three years many papers applying the same technique [104]–[118] have”
Smart Anomaly Detection in Sensor Systems (2020, methodology): “Semi-supervised [42],[43],[44],[45] CNN [55],[56]”
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals (2017, background): “Janssens et al. proposed a CNN model for rotating machinery conditions recognition whose input is DFT of two lines of signals collected from two sensors placed perpendicular to each other [22].”
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox (2019, neutral): “In [27], a 2-D”
Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, background): “Again, [24], [91]–[93], and [94] are some of the published works which use CNN architecture for MFDD using a dataset other than the CWRU bearing dataset”