A novel deep autoencoder feature learning method for rotating machinery fault diagnosis

By Haidong Shao, Hongkai Jiang, Huiwei Zhao, Fuan Wang. 2017

630 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/4b8667b09a6bf71311d7df7ab80c30c81fa8acf5

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In Control and Systems Engineering

In Mechanical Engineering

Citations in the corpus

Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, methodology): “In [122], a locomotive bearing dataset developed at the Northwestern Polytechnical University is used to validate the performance of auto-encoders.”
Deep Autoencoder based Energy Method for the Bending, Vibration, and Buckling Analysis of Kirchhoff Plates (2020, background): “The learning ability of deep neural networks has been enhanced with diﬀerent architectures, such as deep belief network (DBN) or deep autoencoder (DAE) [43, 44].”
Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, background): “The typical deep learning models include but are not limited to the convolutional neural network (CNN) [14], [36], [37], recurrent neural network [38], restricted Boltzmann machine [39], autoencoder [40], [41], deep belief network [42], and their variants.”
Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application (2018, background): “In the past few years, the deep learning models, including stacked auto-encoder (SAE) [15], deep belief networks (DBN) [16] and, in particular, convolutional neural networks (CNN) [17]-[23], have gained much popularity and success in mechanical fault diagnosis issues, showing an extraordinary…”
Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, methodology): “In [121], a locomotive bearing dataset developed at the Northwestern Polytechnical University is used to validate the performance of auto-encoders.”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, methodology): “These have been used for very diverse applications like diabetes detection [118], action detection for surveillance [119], feature learning for process pattern recognition [120], denoising for speech enhancement [121] [122], fault diagnosis [123], social image understanding [124], low light image en”
Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, methodology): “The references [77], [78] are some of the works carried out on MFDD with an auto-encoder using a dataset other than the CWRU dataset.”
Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, methodology): “According to [106], a deep AE method was applied to the fault diagnosis of a gearbox and an electrical locomotive bearing, and the corresponding diagnostic accuracy was”
A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings (2021, background): “Liu’s GRU-based non-linear predictive denoising autoencoder (GRU-NP-DAE) [16], Shao’s auto-encoder (AE) [19] and Mao’s auto-encoder extreme learning machine (AE-ELM) [13].”