Ahmed Shahin - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Access provided by:

MIT Libraries

Access provided by:

MIT Libraries

ADVANCED SEARCH

Author details

Ahmed Shahin

Also published under: A. Shahin

Publications

19

Citations

451

Publications by Year

20082024

Co-Authors:

Almoataz Y. AbdelazizSayed AbulanwarYoshihiro BabaB. DavatFujin Deng

Show All Co-Authors (28)

Ahmed Shahin

Also published under: A. Shahin

Affiliation

Electrical Engineering Department

Faculty of Engineering

Mansoura University, Mansoura, Egypt

Publication Topics

Current Source,
Load Power,
Reference Trajectory,
Battery Management System,
Boost Converter,
Charging Method,
Charging System,
Conductivity Values,
Constant Power Loads,
Control Strategy,
Control Structure,
Control System

Biography

Ahmed Shahin (Senior Member, IEEE) received the B.Sc. and M.Sc. degrees in electrical engineering from Mansoura University, Mansoura, Egypt, in 2000 and 2004, respectively, and the Ph.D. degree in electrical engineering from the Ecole Nationale Supérieure d’electricité et de Mécanique, Université de Lorraine, Nancy, France, in 2011. Since 2012, he has been an Associate Professor with the Faculty of Engineering, Mansoura University. His current research interests include renewable energy systems, modeling, and optimization methods, which are oriented to the power management, control, and fault diagnosis of the photovoltaic systems and fault tolerance in power electronic systems, electrical vehicles, distributed generation systems, and multis...

Publications

19

Citations

451

Publications by Year

20082024

Co-Authors:

Almoataz Y. Abdelaziz
Sayed Abulanwar
Yoshihiro Baba
B. Davat
Fujin Deng

Show All Co-Authors (28)

Author's Published Works

Search History

Showing 1-23 of 23 results

Conferences (14)

Journals (9)

Sort

Filter Results

Show

Subscribed Content

Open Access Only

Range
Single Year
Feng Jia(23)
Yaguo Lei(8)
Jianjun Shen(8)
Saibo Xing(5)
Yu Chen(5)
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China(3)
Key Laboratory of Road Construction Technology and Equipment of Ministry of Education, Chang’an University, Xi’an, China(3)
Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, China(2)
State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an, China(2)
School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China(2)
2023 IEEE International Conference on Mechatronics and Automation (ICMA)(3)
IEEE Access(3)
IEEE Transactions on Industrial Electronics(3)
2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD)(1)
2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings(1)
IEEE(23)
Harbin, Heilongjiang, China(3)
Xi'an, China(3)
Beijing, China(2)
Nanjing, China(2)
Guilin, Guangxi, China(1)
Fault Diagnosis(12)
Fault Diagnosis Method(8)
Feature Learning(8)
Health Status(8)
Vibration Signals(8)

Select All on Page

Sort By

Results

Simulation Data-Driven Semi-Supervised Fault Diagnosis Method for Rolling Bearings

Lifei Hao;Feng Jia;Jianjun Shen;Huadong Huang

2024 Global Reliability and Prognostics and Health Management Conference (PHM-Beijing)

Year: 2024 | Conference Paper |

HTML

In addressing the challenges of acquiring substantial and effective rolling bearing fault data in industrial environments, as well as the limited availability of labeled samples which hinders the generalization performance of intelligent diagnostic models, this paper proposes a semi-supervised fault diagnosis method driven by simulation data. Firstly, a fault-bearing simulation model under non-sta...Show More

Simulation Data-Driven Semi-Supervised Fault Diagnosis Method for Rolling Bearings

Lifei Hao;Feng Jia;Jianjun Shen;Huadong Huang

2024 Global Reliability and Prognostics and Health Management Conference (PHM-Beijing)

Year: 2024 | Conference Paper |

The Multifactor Aging Characteristics and Aging State Assessment of Epoxy Composites for GIS

Shuang Wang;Yu Chen;Feng Jia;Bin Guo;Jiale Mao;Yonghong Cheng;Zengbin Wang;Ruilei Gong

IEEE Transactions on Dielectrics and Electrical Insulation

Year: 2024 | Volume: 31, Issue: 6 | Journal Article |

HTML

Epoxy and its composites are widely used as raw materials for the solid insulations in gas-insulated switchgear (GIS) worldwide due to their excellent dielectric properties, thermal stability, corrosion resistance, and ease of processing. These insulation materials show aging phenomena under multiple stresses of electrical, thermal, and vibration during GIS operation, which reduces the electrical ...Show More

The Multifactor Aging Characteristics and Aging State Assessment of Epoxy Composites for GIS

Shuang Wang;Yu Chen;Feng Jia;Bin Guo;Jiale Mao;Yonghong Cheng;Zengbin Wang;Ruilei Gong

IEEE Transactions on Dielectrics and Electrical Insulation

Year: 2024 | Volume: 31, Issue: 6 | Journal Article |

Speed Synchronization Control of Paver Traveling Motor Based on Optimized Fuzzy Adaptive PID

Zheng Tang;Jianjun Shen;Feng Jia;Xiaohan Feng

2023 IEEE International Conference on Mechatronics and Automation (ICMA)

Year: 2023 | Conference Paper |

HTML

The normal running and operation of pavers is affected by the motor speed synchronization control system. When the motor is subjected to mutation interference, the speed will fluctuate, which will cause the movement trajectory to deviate and reduce the working quality. To realize speed synchronization of traveling motor and the normal operation of pavers, a fuzzy adaptive PID co-simulation method ...Show More

Speed Synchronization Control of Paver Traveling Motor Based on Optimized Fuzzy Adaptive PID

Zheng Tang;Jianjun Shen;Feng Jia;Xiaohan Feng

2023 IEEE International Conference on Mechatronics and Automation (ICMA)

Year: 2023 | Conference Paper |

Vertical Deformation Optimization for the Screed of Pavers Based on ABAQUS and Isight

Xiaohan Feng;Feng Jia;Jianjun Shen;Zheng Tang

2023 IEEE International Conference on Mechatronics and Automation (ICMA)

Year: 2023 | Conference Paper |

HTML

During the paving process, the posture of the screed is the main factor determining the evenness of the road surface. As the bottom of the screed is in direct contact with the high-temperature asphalt mixture at work, the bottom plate is heated and expands. This results in vertical deformation of the screed with upward heat deformation characteristics at both ends, thereby reducing the quality of ...Show More

Vertical Deformation Optimization for the Screed of Pavers Based on ABAQUS and Isight

Xiaohan Feng;Feng Jia;Jianjun Shen;Zheng Tang

2023 IEEE International Conference on Mechatronics and Automation (ICMA)

Year: 2023 | Conference Paper |

Source-free domain adaptation network for rolling bearing fault diagnosis

Yuanfei Wang;Feng Jia;Jianjun Shen;Lifei Hao

2023 IEEE International Conference on Mechatronics and Automation (ICMA)

Year: 2023 | Conference Paper |

HTML

The current purpose of cross domain rolling bearing fault diagnosis is to transfer rich diagnosis features from marked source to unmarked target domain. Most domain adaptation methods require simultaneous access to both source domain data and target domain data. However, the following two shortcomings limit the development of cross-domain bearing fault diagnosis: (1) Source dataset takes up so muc...Show More

Source-free domain adaptation network for rolling bearing fault diagnosis

Yuanfei Wang;Feng Jia;Jianjun Shen;Lifei Hao

2023 IEEE International Conference on Mechatronics and Automation (ICMA)

Year: 2023 | Conference Paper |

Few-shot fault diagnosis method for rolling bearing using local descriptors

Langfei An;Feng Jia;Bo Wang;Jingru Hou;Jianjun Shen;Xuding Song

2022 IEEE International Conference on Mechatronics and Automation (ICMA)

Year: 2022 | Conference Paper |

HTML

In the existing rolling bearing fault diagnosis methods based on few-shot learning, the focus is on the global information with high discrimination, and ignore the local information with low discrimination leads to its low diagnosis accuracy. To solve the above problem, we propose a few-shot fault diagnosis method for rolling bearing using local descriptors. Specifically, in the feature extraction...Show More

Few-shot fault diagnosis method for rolling bearing using local descriptors

Langfei An;Feng Jia;Bo Wang;Jingru Hou;Jianjun Shen;Xuding Song

2022 IEEE International Conference on Mechatronics and Automation (ICMA)

Year: 2022 | Conference Paper |

Temperature Monitoring for 500 kV Oil-Filled Submarine Cable Based on BOTDA Distributed Optical Fiber Sensing Technology: Method and Application

Yu Chen;Shuang Wang;Yi Hao;Kai Yao;Hanzhi Li;Feng Jia;Dongli Yue;Qingyun Shi;Yonghong Cheng;Xiaowei Huang

IEEE Transactions on Instrumentation and Measurement

Year: 2022 | Volume: 71 | Journal Article |

Cited by: Papers (20)

HTML

The 500 kV oil-filled ac submarine cables in the networking project of China’s southern coast are large capacity, ultrahigh-voltage cross-sea submarine power cables, which are 31 km long and bundled with submarine optical cables. Temperature monitoring on submarine cables is of great significance in controlling the ampacity and ensuring operational reliability. Referring to the submarine cables’ s...Show More

Temperature Monitoring for 500 kV Oil-Filled Submarine Cable Based on BOTDA Distributed Optical Fiber Sensing Technology: Method and Application

Yu Chen;Shuang Wang;Yi Hao;Kai Yao;Hanzhi Li;Feng Jia;Dongli Yue;Qingyun Shi;Yonghong Cheng;Xiaowei Huang

IEEE Transactions on Instrumentation and Measurement

Year: 2022 | Volume: 71 | Journal Article |

Variable Voltage Variable Frequency Modular Multilevel AC/AC Converter With High-Frequency Harmonics Filtering Capability

Yongqing Meng;Jialu Sun;Ziyue Duan;Feng Jia;Xiuli Wang;Xifan Wang

IEEE Journal of Emerging and Selected Topics in Power Electronics

Year: 2022 | Volume: 10, Issue: 1 | Journal Article |

Cited by: Papers (9)

HTML

This article proposes a variable voltage variable frequency modular multilevel ac/ac converter (VVVF-MMC) with a high-frequency isolation link, which can realize voltage and frequency conversion simultaneously with insulated-gate bipolar transistors (IGBTs) of the same rated voltage and current level. The parallel connection of multiple modules is realized by coupling reactors, which can realize l...Show More

Variable Voltage Variable Frequency Modular Multilevel AC/AC Converter With High-Frequency Harmonics Filtering Capability

Yongqing Meng;Jialu Sun;Ziyue Duan;Feng Jia;Xiuli Wang;Xifan Wang

IEEE Journal of Emerging and Selected Topics in Power Electronics

Year: 2022 | Volume: 10, Issue: 1 | Journal Article |

Damage Analysis of Suspended Submarine Cable under Current Force Based on Finite Element Simulation

Jingwen Zhang;Yu Chen;Feng Jia;Yifang Zhou;Hanzhi Li;Changxi Li;Xiaowei Huang;Qiang Guo

2021 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)

Year: 2021 | Conference Paper |

Cited by: Papers (2)

HTML

In this paper, 500kV AC submarine cable was taken as the object, the finite element software COMSOL was used to establish the simulation model of fluid-structure coupling, and the force of the suspended submarine cable was analyzed. The stress distribution of the lead alloy sheath of the cable in four specific directions and the location of the maximum stress value was obtained. On this basis, the...Show More

Damage Analysis of Suspended Submarine Cable under Current Force Based on Finite Element Simulation

Jingwen Zhang;Yu Chen;Feng Jia;Yifang Zhou;Hanzhi Li;Changxi Li;Xiaowei Huang;Qiang Guo

2021 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)

Year: 2021 | Conference Paper |

The Open Set Weighted Domain Adaptation Method for Fault Diagnosis of Rolling Bearings

Feng Jia;Shihao Li;Jianjun Shen;Junxing Ma

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)

Year: 2021 | Conference Paper |

HTML

The transfer fault diagnosis method can identify the fault of rolling bearing dataset collected under different working conditions or different equipment, which realized the cross-dataset application of fault knowledge. However, the existing transfer fault diagnosis methods all assume that the labeled training dataset and unlabeled testing dataset have the same fault label set. The above assumptio...Show More

The Open Set Weighted Domain Adaptation Method for Fault Diagnosis of Rolling Bearings

Feng Jia;Shihao Li;Jianjun Shen;Junxing Ma

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)

Year: 2021 | Conference Paper |

Distribution-Invariant Deep Belief Network for Intelligent Fault Diagnosis of Machines Under New Working Conditions

Saibo Xing;Yaguo Lei;Shuhui Wang;Feng Jia

IEEE Transactions on Industrial Electronics

Year: 2021 | Volume: 68, Issue: 3 | Journal Article |

Cited by: Papers (113)

HTML

As a deep learning model, a deep belief network (DBN) consists of multiple restricted Boltzmann machines (RBMs). Based on DBN, many intelligent fault diagnosis methods are proposed. However, these methods seldom considered the appearance of new working conditions during the operation of real machines. Varying working conditions lead to a change of feature distributions and finally result in low di...Show More

Distribution-Invariant Deep Belief Network for Intelligent Fault Diagnosis of Machines Under New Working Conditions

Saibo Xing;Yaguo Lei;Shuhui Wang;Feng Jia

IEEE Transactions on Industrial Electronics

Year: 2021 | Volume: 68, Issue: 3 | Journal Article |

Comprehensive Analysis of Temperature and Stress Distribution in Optical Fiber Composite Low Voltage Cable Using Finite Element Method

Ahsan Ashfaq;Yu Chen;Kai Yao;Feng Jia;Jing Yu;Yonghong Cheng

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (4)

HTML

Optical fiber composite low voltage cable (OPLC) is an optimized way of carrying out the function of supplying electrical power and communication signals in a single cable. In this paper, the temperature and stress distribution in OPLC cable is analyzed by using the finite element method as the current increases to maximum capacity. The increase of temperature and stress are the two main factors t...Show More

Comprehensive Analysis of Temperature and Stress Distribution in Optical Fiber Composite Low Voltage Cable Using Finite Element Method

Ahsan Ashfaq;Yu Chen;Kai Yao;Feng Jia;Jing Yu;Yonghong Cheng

Year: 2020 | Volume: 8 | Journal Article |

The 500kV Oil-filled Submarine Cable Temperature Monitoring System Based on BOTDA Distributed Optical Fiber Sensing Technology

Yu Chen;Shuang Wang;Yi Hao;Kai Yao;Hanzhi Li;Feng Jia;Qingyun Shi;Dongli Yue;Yonghong Cheng

2020 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)

Year: 2020 | Conference Paper |

Cited by: Papers (8)

HTML

The 500 kV oil-filled AC submarine cable in Hainan Networking System is the first large capacity, ultra-high voltage cross-sea submarine power cable in China, which is 31 kilometers long and bundled with a submarine optical cable. And temperature monitoring on the submarine cable is of great significance to control the current rating and ensure the operation reliability. Based on the structural ch...Show More

The 500kV Oil-filled Submarine Cable Temperature Monitoring System Based on BOTDA Distributed Optical Fiber Sensing Technology

Yu Chen;Shuang Wang;Yi Hao;Kai Yao;Hanzhi Li;Feng Jia;Qingyun Shi;Dongli Yue;Yonghong Cheng

2020 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)

Year: 2020 | Conference Paper |

Deep Neural Network Ensemble for the Intelligent Fault Diagnosis of Machines Under Imbalanced Data

Feng Jia;Shihao Li;Hao Zuo;Jianjun Shen

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (32)

HTML

Imbalanced classification using deep learning has attracted much attention in intelligent fault diagnosis of machinery. However, the existing methods use individual deep neural network to extract features and recognize the health conditions under imbalanced dataset, which may easily over-fit the mechanical data and affect the diagnosis accuracy. To deal with this problem, this paper takes the adva...Show More

Deep Neural Network Ensemble for the Intelligent Fault Diagnosis of Machines Under Imbalanced Data

Feng Jia;Shihao Li;Hao Zuo;Jianjun Shen

Year: 2020 | Volume: 8 | Journal Article |

A Deep Multi-Label Learning Framework for the Intelligent Fault Diagnosis of Machines

Jianjun Shen;Shihao Li;Feng Jia;Hao Zuo;Junxing Ma

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (37)

HTML

Deep learning has been applied in intelligent fault diagnosis of machines since it trains deep neural networks to simultaneously learn features and recognize faults. In the intelligent fault diagnosis methods based on deep learning, feature learning and fault recognition are achieved by solving a multi-class classification problem. The multi-class classification, however, has not considered the re...Show More

A Deep Multi-Label Learning Framework for the Intelligent Fault Diagnosis of Machines

Jianjun Shen;Shihao Li;Feng Jia;Hao Zuo;Junxing Ma

Year: 2020 | Volume: 8 | Journal Article |

A Polynomial Kernel Induced Distance Metric to Improve Deep Transfer Learning for Fault Diagnosis of Machines

Bin Yang;Yaguo Lei;Feng Jia;Naipeng Li;Zhaojun Du

IEEE Transactions on Industrial Electronics

Year: 2020 | Volume: 67, Issue: 11 | Journal Article |

Cited by: Papers (182)

HTML

Deep transfer-learning-based diagnosis models are promising to apply diagnosis knowledge across related machines, but from which the collected data follow different distribution. To reduce the distribution discrepancy, Gaussian kernel induced maximum mean discrepancy (GK-MMD) is a widely used distance metric to impose constraints on the training of diagnosis models. However, the models using GK-MM...Show More

A Polynomial Kernel Induced Distance Metric to Improve Deep Transfer Learning for Fault Diagnosis of Machines

Bin Yang;Yaguo Lei;Feng Jia;Naipeng Li;Zhaojun Du

IEEE Transactions on Industrial Electronics

Year: 2020 | Volume: 67, Issue: 11 | Journal Article |

Small Signal Stability Analysis and Control Optimization of Y-connected Modular Multilevel Converter for Fractional Frequency Offshore Wind Power Application

Feng Jia;Yongqing Meng;Jianyang Yu;Shuhao Yan;Rong Ye;Yuchen Tang

2019 IEEE 8th International Conference on Advanced Power System Automation and Protection (APAP)

Year: 2019 | Conference Paper |

Cited by: Papers (1)

HTML

Y-connected modular multilevel converter(Y-MMC) is a new circuit topology of direct AC/AC power conversion, with competitive application prospects in fractional frequency offshore wind power system. Different from the previous converter topology, Y-MMC realizes the frequency conversion by single-phase voltage cancellation. Simultaneously, the voltages between main bridges and auxiliary bridges can...Show More

Small Signal Stability Analysis and Control Optimization of Y-connected Modular Multilevel Converter for Fractional Frequency Offshore Wind Power Application

Feng Jia;Yongqing Meng;Jianyang Yu;Shuhao Yan;Rong Ye;Yuchen Tang

2019 IEEE 8th International Conference on Advanced Power System Automation and Protection (APAP)

Year: 2019 | Conference Paper |

A Transfer Learning Method for Intelligent Fault Diagnosis from Laboratory Machines to Real-Case Machines

Bin Yang;Yaguo Lei;Feng Jia;Saibo Xing

2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC)

Year: 2018 | Conference Paper |

Cited by: Papers (16)

HTML

It is difficult to train a reliable intelligent fault diagnosis model for machines used in real cases (MURC) because there are not sufficient labeled data. However, we can easily simulate various faults in a laboratory, and the data from machines used in the laboratory (MUL) contain fault knowledge related to the data from MURC. Thus, it is possible to identify the health states of MURC by using r...Show More

A Transfer Learning Method for Intelligent Fault Diagnosis from Laboratory Machines to Real-Case Machines

Bin Yang;Yaguo Lei;Feng Jia;Saibo Xing

2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC)

Year: 2018 | Conference Paper |

Intelligent fault diagnosis of rotating machinery using locally connected restricted boltzmann machine in big data era

Saibo Xing;Yaguo Lei;Feng Jia;Jing Lin

2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

Year: 2017 | Conference Paper |

Cited by: Papers (5)

HTML

In intelligent fault diagnosis, unsupervised feature learning is a potential tool to replace the manual feature extraction in big data era. Therefore, we first develop a locally connected restricted Boltzmann machine (LCRBM) from the traditional RBM in order to handle the periodic appearance of fault characteristics in the raw signals of rotating machinery. Then, using LCRBM, we propose a method f...Show More

Intelligent fault diagnosis of rotating machinery using locally connected restricted boltzmann machine in big data era

Saibo Xing;Yaguo Lei;Feng Jia;Jing Lin

2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

Year: 2017 | Conference Paper |

A dirty data recognition method for machinery condition monitoring in big data era

Yaguo Lei;Xin Zhou;Xuefang Xu;Feng Jia

IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society

Year: 2017 | Conference Paper |

Cited by: Papers (4)

HTML

Condition monitoring of machinery has entered the big data era, while the existence of dirty data reduces the quality of the whole data. In order to recognize the dirty data included in machinery monitoring data, a new method is proposed in this paper. First, a feature named sampled power index (SPI) is designed to transform the dirty data recognition issue into the outlier recognition. Then the w...Show More

A dirty data recognition method for machinery condition monitoring in big data era

Yaguo Lei;Xin Zhou;Xuefang Xu;Feng Jia

IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society

Year: 2017 | Conference Paper |

Reconstruction independent component analysis-based methods for intelligent fault diagnosis

Yaguo Lei;Hongkai Shan;Feng Jia;Jing Lin

2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

Year: 2016 | Conference Paper |

Cited by: Papers (3)

HTML

Based on machine learning techniques, this paper presents a novel intelligent fault diagnosis method, which is an integrated framework concerning reconstruction independent component analysis (RICA) and multiclass relevance vector machine (MRVM). In this method, the RICA is first used to automatically extract features from raw vibration signals. Then, the learned features are used as the input dat...Show More

Reconstruction independent component analysis-based methods for intelligent fault diagnosis

Yaguo Lei;Hongkai Shan;Feng Jia;Jing Lin

2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

Year: 2016 | Conference Paper |

A method of automatic feature extraction from massive vibration signals of machines

Feng Jia;Yaguo Lei;Saibo Xing;Jing Lin

2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings

Year: 2016 | Conference Paper |

Cited by: Papers (4)

HTML

In the studies of intelligent fault diagnosis of machines, lots of effort goes into designing effective feature extraction algorithms. Such processes would consume plenty of human labor, especially when dealing with massive vibration signals. So it is interesting to automatically extract features using machine learning techniques, instead of manually extracting them. To deal with the problem, this...Show More

A method of automatic feature extraction from massive vibration signals of machines

Feng Jia;Yaguo Lei;Saibo Xing;Jing Lin

2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings

Year: 2016 | Conference Paper |

An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

Yaguo Lei;Feng Jia;Jing Lin;Saibo Xing;Steven X. Ding

IEEE Transactions on Industrial Electronics

Year: 2016 | Volume: 63, Issue: 5 | Journal Article |

Cited by: Papers (973)

HTML

Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional intelligent diagnosis methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise. Such processes take advantage of human ingenuity but are...Show More

An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

Yaguo Lei;Feng Jia;Jing Lin;Saibo Xing;Steven X. Ding

IEEE Transactions on Industrial Electronics

Year: 2016 | Volume: 63, Issue: 5 | Journal Article |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.