4.7 Article

Fault Diagnosis of Induction Motors Under Untrained Loads With a Feature Adaptation and Improved Broad Learning Framework

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Review Computer Science, Interdisciplinary Applications

Review on Machine Learning Algorithm Based Fault Detection in Induction Motors

Prashant Kumar et al.

Summary: Fault detection in induction motors is crucial, and the application of machine learning algorithms can provide reliable and effective solutions for preventive maintenance.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2021)

Article Automation & Control Systems

Domain Knowledge-Based Deep-Broad Learning Framework for Fault Diagnosis

Jian Feng et al.

Summary: The article introduces a novel domain-knowledge-based deep-broad learning framework that shows high flexibility and requires few labeled samples. Experimental results demonstrate that the framework performs significantly well in fault diagnosis tasks.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

Article Automation & Control Systems

Semisupervised Graph Convolution Deep Belief Network for Fault Diagnosis of Electormechanical System With Limited Labeled Data

Xiaoli Zhao et al.

Summary: This article proposes an intelligent fault diagnosis method for electromechanical systems based on a new semisupervised graph convolution deep belief network algorithm, which can achieve high diagnostic accuracy with a small amount of labeled data.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Article Energy & Fuels

Induction Machine Fault Detection and Classification Using Non-Parametric, Statistical-Frequency Features and Shallow Neural Networks

Rahul R. Kumar et al.

Summary: This article presents a two-stage fault detection and classification scheme specifically designed for rotating electrical machines, using new condition indicators specific to the frequency domain. The approach involves feature reduction using principal component analysis (PCA) and shallow neural networks for online detection and classification. Experimental validation on signals from grid and inverter fed induction motors confirms the effectiveness of the proposed scheme.

IEEE TRANSACTIONS ON ENERGY CONVERSION (2021)

Article Engineering, Multidisciplinary

Induction motor broken rotor bar faults diagnosis using ANFIS-based DWT

Menshawy A. Mohamed et al.

Summary: This paper proposes a new method for diagnosing BRB faults in three phase squirrel-cage induction motors using DWT and ANFIS. The experimental results show that this technique is valid and effective for BRB fault diagnosis.

INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION (2021)

Article Engineering, Electrical & Electronic

Feature Extraction for Data-Driven Remaining Useful Life Prediction of Rolling Bearings

Huimin Zhao et al.

Summary: The study introduced a new data-driven feature extraction method to enhance the accuracy of RUL prediction for rolling bearings. By combining different algorithms, a rapid, stable, and adaptable RUL prediction model specifically for rolling bearings was established.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

Article Automation & Control Systems

Smart Sensor-Based Synergistic Analysis for Rotor Bar Fault Detection of Induction Motors

Peter Luong et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2020)

Article Automation & Control Systems

Multiscale Kernel Based Residual Convolutional Neural Network for Motor Fault Diagnosis Under Nonstationary Conditions

Ruonan Liu et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

Article Engineering, Electrical & Electronic

A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion

Duy Tang Hoang et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

Article Automation & Control Systems

An Integrated Feature-Based Failure Prognosis Method for Wind Turbine Bearings

Milad Rezamand et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2020)

Article Automation & Control Systems

Intelligent Fault Diagnosis of Multichannel Motor-Rotor System Based on Multimanifold Deep Extreme Learning Machine

Xiaoli Zhao et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2020)

Article Automation & Control Systems

Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery

Siyuan Chen et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2020)

Article Automation & Control Systems

Adaptive System Identification and Severity Index-Based Fault Diagnosis in Motors

Anam Abid et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2019)

Article Automation & Control Systems

Multilevel Information Fusion for Induction Motor Fault Diagnosis

Jinjiang Wang et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2019)

Article Energy & Fuels

Interturn Short Fault Diagnosis in a PMSM by Voltage and Current Residual Analysis With the Faulty Winding Model

Seokbae Moon et al.

IEEE TRANSACTIONS ON ENERGY CONVERSION (2018)

Article Automation & Control Systems

Single and Simultaneous Fault Diagnosis With Application to a Multistage Gearbox: A Versatile Dual-ELM Network Approach

Zhi-Xin Yang et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2018)

Article Automation & Control Systems

Novel Particle Swarm Optimization-Based Variational Mode Decomposition Method for the Fault Diagnosis of Complex Rotating Machinery

Xian-Bo Wang et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2018)

Article Engineering, Electrical & Electronic

Multiple features extraction and selection for detection and classification of stator winding faults

Smail Haroun et al.

IET ELECTRIC POWER APPLICATIONS (2018)

Article Engineering, Mechanical

Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing

Haidong Shao et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)

Article Computer Science, Artificial Intelligence

Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture

C. L. Philip Chen et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)

Article Engineering, Mechanical

A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine

Pak Kin Wong et al.

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE (2017)

Article Automation & Control Systems

Broken rotor bar fault diagnosis using fast Fourier transform applied to field-oriented control induction machine: simulation and experimental study

Tarek Ameid et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2017)

Article Engineering, Mechanical

Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network

Li-Hua Wang et al.

CHINESE JOURNAL OF MECHANICAL ENGINEERING (2017)

Article Automation & Control Systems

Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling

Xiaosong Hu et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2016)

Article Statistics & Probability

Limit theorems for order statistics from exponentials

Yu Miao et al.

STATISTICS & PROBABILITY LETTERS (2016)

Article Engineering, Electrical & Electronic

Variational Mode Decomposition

Konstantin Dragomiretskiy et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2014)

Article Automation & Control Systems

From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis

Xuewu Dai et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2013)

Article Engineering, Electrical & Electronic

Symmetrical components and current Concordia based assessment of single phasing of an induction motor by feature pattern extraction method and radar analysis

Surajit Chattopadhyay et al.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2012)

Article Engineering, Mechanical

Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines

Ruqiang Yan et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2012)

Article Computer Science, Artificial Intelligence

Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM-CART Model

Manjeevan Seera et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2012)

Article Automation & Control Systems

Fault detection in induction machines using power spectral density in wavelet decomposition

Jordi Cusido et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2008)

Article Engineering, Mechanical

Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals

BS Yang et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2006)