Fault Diagnosis of Rotating Machinery Based on Combination of Deep Belief Network and One-dimensional Convolutional Neural Network

Fault Diagnosis of Rotating Machinery Based on Combination of Deep Belief Network and One-dimensional Convolutional Neural Network

The traditional intelligent diagnosis methods of rotating machinery generally require feature extraction of the raw signals in advance. However, it is a very time-consuming and laborious process for extracting the sensitive feature information to improve classification performance. Deep learning met...

Full description

Bibliographic Details
Main Authors:	Yibing Li, Li Zou, Li Jiang, Xiangyu Zhou
Format:	Article
Language:	English
Published:	IEEE 2019-01-01
Series:	IEEE Access
Subjects:	Deep belief network (DBN) one-dimensional convolutional neural network (1D-CNN) rotating machinery feature extraction intelligent fault diagnosis
Online Access:	https://ieeexplore.ieee.org/document/8901430/

Similar Items

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery
by: Shengnan Tang, et al.
Published: (2020-01-01)

A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery
by: Shangjun Ma, et al.
Published: (2019-05-01)

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery
by: Shengnan Tang, et al.
Published: (2020-01-01)

Fault Diagnosis for Rotating Machinery Using Multiscale Permutation Entropy and Convolutional Neural Networks
by: Hongmei Li, et al.
Published: (2020-07-01)

Identifying Cancer-Related lncRNAs Based on a Convolutional Neural Network
by: Zihao Liu, et al.
Published: (2020-08-01)

Intelligent fault diagnosis of marine diesel engine based on deep belief network
by: ZHONG Guoqiang, et al.
Published: (2020-06-01)

Research on Rotating Machinery Fault Diagnosis Method Based on Energy Spectrum Matrix and Adaptive Convolutional Neural Network
by: Yiyang Liu, et al.
Published: (2021-12-01)

Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network
by: Xingchen Liu, et al.
Published: (2019-02-01)

An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions
by: Huihui Qiao, et al.
Published: (2019-01-01)

A Novel Framework for Early Pitting Fault Diagnosis of Rotating Machinery Based on Dilated CNN Combined With Spatial Dropout
by: Xueyi Li, et al.
Published: (2021-01-01)

Deep Decoupling Convolutional Neural Network for Intelligent Compound Fault Diagnosis
by: Ruyi Huang, et al.
Published: (2019-01-01)

Data Fusion Generative Adversarial Network for Multi-Class Imbalanced Fault Diagnosis of Rotating Machinery
by: Qianjun Liu, et al.
Published: (2020-01-01)

A Fault Diagnosis Method of Rotating Machinery Based on One-Dimensional, Self-Normalizing Convolutional Neural Networks
by: Jingli Yang, et al.
Published: (2020-07-01)

A Method of Rolling Bearing Fault Diagnose Based on Double Sparse Dictionary and Deep Belief Network
by: Junfeng Guo, et al.
Published: (2020-01-01)

Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network
by: Shuangjie Liu, et al.
Published: (2020-09-01)

Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs
by: Luqman S. Maraaba, et al.
Published: (2020-01-01)

A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network
by: Sheng Guo, et al.
Published: (2018-05-01)

Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery
by: Shengnan Tang, et al.
Published: (2020-01-01)

Rotation Equivariant Convolutional Neural Networks for Hyperspectral Image Classification
by: Mercedes E. Paoletti, et al.
Published: (2020-01-01)

Adaptive Moment Estimation On Deep Belief Network For Rupiah Currency Forecasting
by: Abram Setyo Prabowo, et al.
Published: (2019-01-01)

Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network
by: Jiaxing Wang, et al.
Published: (2021-01-01)

A Generative Adversarial Network-Based Intelligent Fault Diagnosis Method for Rotating Machinery Under Small Sample Size Conditions
by: Yu Ding, et al.
Published: (2019-01-01)

An Auxiliary Fault Identification Strategy of Flexible HVDC Grid Based on Convolutional Neural Network With Branch Structures
by: Jun Mei, et al.
Published: (2020-01-01)

Fault Prognosis of Hydraulic Pump Based on Bispectrum Entropy and Deep Belief Network
by: Li Hongru, et al.
Published: (2019-10-01)

Prediction Consistency Guided Convolutional Neural Networks for Cross-Domain Bearing Fault Diagnosis
by: Songsong Wu, et al.
Published: (2020-01-01)

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery
by: Shengnan Tang, et al.
Published: (2020-09-01)

A Precise Diagnosis Method of Structural Faults of Rotating Machinery based on Combination of Empirical Mode Decomposition, Sample Entropy, and Deep Belief Network
by: Zhaoyi Guan, et al.
Published: (2019-01-01)

Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization
by: Davor Kolar, et al.
Published: (2021-03-01)

EMG Pattern Classification by Split and Merge Deep Belief Network
by: Hyeon-min Shim, et al.
Published: (2016-12-01)

A Weighted Subdomain Adaptation Network for Partial Transfer Fault Diagnosis of Rotating Machinery
by: Sixiang Jia, et al.
Published: (2021-04-01)

An Intelligent Deep Feature Learning Method With Improved Activation Functions for Machine Fault Diagnosis
by: Wei You, et al.
Published: (2020-01-01)

A Novel Deep Recurrent Belief Network Model for Trend Prediction of Transformer DGA Data
by: Bo Qi, et al.
Published: (2019-01-01)

The Entropy Algorithm and Its Variants in the Fault Diagnosis of Rotating Machinery: A Review
by: Yongbo Li, et al.
Published: (2018-01-01)

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
by: Yu Wei, et al.
Published: (2019-04-01)

Fault diagnosis of rotating machinery based on time-frequency decomposition and envelope spectrum analysis
by: Yonggen Chang, et al.
Published: (2017-03-01)

Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input
by: Davor Kolar, et al.
Published: (2020-07-01)

Fault Diagnosis of Rotating Machinery Based on Wavelet Domain Denoising and Metric Distance
by: Naiquan Su, et al.
Published: (2019-01-01)

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery
by: Jalayer, M., et al.
Published: (2022)

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery
by: Jalayer, M., et al.
Published: (2022)

Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity
by: Bing Han, et al.
Published: (2020-06-01)