Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems

Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems

Fault diagnosis in manufacturing systems represents one of the most critical challenges dealing with condition-based monitoring in the recent era of smart manufacturing. In the current Industry 4.0 framework, maintenance strategies based on traditional data-driven fault diagnosis schemes require enh...

Full description

Bibliographic Details
Main Authors:	Francisco Arellano-Espitia, Miguel Delgado-Prieto, Victor Martinez-Viol, Juan Jose Saucedo-Dorantes, Roque Alfredo Osornio-Rios
Format:	Article
Language:	English
Published:	MDPI AG 2020-07-01
Series:	Sensors
Subjects:	condition monitoring fault detection data-driven fault diagnosis systems deep neural network feature fusion
Online Access:	https://www.mdpi.com/1424-8220/20/14/3949

Similar Items

Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings
by: Juan Jose Saucedo-Dorantes, et al.
Published: (2021-08-01)

Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network
by: Juan-Jose Saucedo-Dorantes, et al.
Published: (2021-08-01)

A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery
by: Funa Zhou, et al.
Published: (2018-10-01)

An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy
by: Zhiwu Shang, et al.
Published: (2021-06-01)

A New Deep Fusion Network for Automatic Mechanical Fault Feature Learning
by: Yumei Qi, et al.
Published: (2019-01-01)

Single-Phase Grounding Fault Types Identification Based on Multi-Feature Transformation and Fusion
by: Fan, M., et al.
Published: (2022)

Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery
by: Xiaojie Guo, et al.
Published: (2016-12-01)

Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems
by: Francisco Arellano-Espitia, et al.
Published: (2021-08-01)

Deep learning approaches for fault detection and classifications in the electrical secondary distribution network: Methods comparison and recurrent neural network accuracy comparison
by: Daudi Mnyanghwalo, et al.
Published: (2020-01-01)

Blade Rub-Impact Fault Identification Using Autoencoder-Based Nonlinear Function Approximation and a Deep Neural Network
by: Alexander E. Prosvirin, et al.
Published: (2020-11-01)

A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis
by: Duy Tang Hoang, et al.
Published: (2021-01-01)

Bearing Fault Diagnosis with a Feature Fusion Method Based on an Ensemble Convolutional Neural Network and Deep Neural Network
by: Hongmei Li, et al.
Published: (2019-04-01)

Fault Diagnosis of Main Pump in Converter Station Based on Deep Neural Network
by: Qingsheng Zhao, et al.
Published: (2021-07-01)

A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals
by: Qing Ye, et al.
Published: (2020-08-01)

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox
by: Luyang Jing, et al.
Published: (2017-02-01)

Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques
by: Mohamed Benouis, et al.
Published: (2021-09-01)

A Low-Delay Lightweight Recurrent Neural Network (LLRNN) for Rotating Machinery Fault Diagnosis
by: Wenkai Liu, et al.
Published: (2019-07-01)

A Systematic Methodology for Gearbox Health Assessment and Fault Classification
by: Jay Lee, et al.
Published: (2011-01-01)

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review
by: Shen Zhang, et al.
Published: (2020-01-01)

Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN
by: Jinghui Pan, et al.
Published: (2021-06-01)

Data-Driven Feature Extraction for Analog Circuit Fault Diagnosis Using 1-D Convolutional Neural Network
by: Huahui Yang, et al.
Published: (2020-01-01)

A Novel Data-Driven Fault Feature Separation Method and Its Application on Intelligent Fault Diagnosis Under Variable Working Conditions
by: Shunming Li, 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)

Improved Hierarchical Adaptive Deep Belief Network for Bearing Fault Diagnosis
by: Changqing Shen, et al.
Published: (2019-08-01)

A Random Forest and Current Fault Texture Feature–Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR
by: Lei Kou, et al.
Published: (2021-06-01)

Comparison of Damage Zones of the Nojima and the Asano Faults from the Deep Drilling Project: Differences in Meso-to-microscale Deformation Structures related to Fault Activity
by: Nishiwaki, Takafumi
Published: (2020)

A new fault diagnosis method using deep belief network and compressive sensing
by: Yunfei Ma, et al.
Published: (2020-02-01)

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning
by: Chuan Li, et al.
Published: (2016-06-01)

Gearbox Incipient Fault Detection Based on Deep Recursive Dynamic Principal Component Analysis
by: Huaitao Shi, et al.
Published: (2020-01-01)

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis
by: Muhammad Sohaib, et al.
Published: (2017-12-01)

Modeling of Coal Mill System Used for Fault Simulation
by: Yong Hu, et al.
Published: (2020-04-01)

Fault Diagnosis Method of DC Charging Points for EVs Based on Deep Belief Network
by: Dexin Gao, et al.
Published: (2021-03-01)

A Review on Fault Detection and Process Diagnostics in Industrial Processes
by: You-Jin Park, et al.
Published: (2020-09-01)

A hydraulic fault diagnosis method based on sliding-window spectrum feature and deep belief network
by: Xinqing Wang, et al.
Published: (2017-09-01)

Extensive analysis of fault response and extracting fault features for DC microgrids
by: Eatmad W. Nahas, et al.
Published: (2021-04-01)

Data-Driven Incipient Fault Prediction for Non-Stationary and Non-Linear Rotating Systems: Methodology, Model Construction and Application
by: Qingfeng Wang, et al.
Published: (2020-01-01)

Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG
by: Zia Ullah, et al.
Published: (2020-07-01)

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning
by: Wentao Mao, et al.
Published: (2020-02-01)

A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis
by: Fan Jiang, et al.
Published: (2018-09-01)

Fault Diagnosis Based on Non-Negative Sparse Constrained Deep Neural Networks and Dempster–Shafer Theory
by: Zhuo Zhang, et al.
Published: (2020-01-01)

Cannot write session to /tmp/vufind_sessions/sess_vaj1512ppdpd8abpn3sfr6lg3t