Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning

By Jiedi Sun, Changhong Yan, Jiangtao Wen. IEEE Transactions on Instrumentation and Measurement, 2018

365 citations recorded by the source corpus.

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A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion (2020, background): “With the ability of automatically learning multiple levels of data abstraction, when applied to extract features from the raw signals in the fault diagnosis application, DL achieves high accuracy and outperforms other traditional signal processing and feature extraction techniques [15]–[17].”
Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, background): “20% [129] Stack sparse AE 2 N/A Data compression N/A 97.”
Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, background): “• Many variants have been proposed: stacked denoising AE [125], deep ensemble AE [128], and stacked sparse AE [130].”
Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks (2020, methodology): “The traditional data-driven fault diagnosis methods have two phases: manual feature extraction and selection and fault classification [10], [11].”
DCNN-Based Multi-Signal Induction Motor Fault Diagnosis (2020, background): “In machine fault diagnosis, deep architecture can automatically learn useful representations from massive sensor data and recognize certain fault patterns [25]–[28].”