Nadeem Shahbaz - IEEE Xplore Author Profile
Subscribe
ADVANCED SEARCH

Author details

< Back

Nadeem Shahbaz

Publications
14
Citations
14
Publications by Year
20222025
Co-Authors:
Show All Co-Authors (13)
Image of author Nadeem Shahbaz

Nadeem Shahbaz

Affiliation

State Key Laboratory of Electrical Insulation and Power Equipment
Xi’an Jiaotong University
Xi’an, Shaanxi, China

Publication Topics

Biography

Nadeem Shahbaz was born in Punjab, Pakistan, in 1992. He received the B.E. degree in electrical engineering from The University of Faisalabad, Faisalabad, Pakistan, in 2013, the M.S. degree in electrical engineering from the National University of Computer and Emerging Sciences, Karachi, Pakistan, in 2019, and the Ph.D. degree from Xi’an Jiaotong University, Xi’an, China, in 2021.,His research interests include electrical equipment fault diagnosis.(Based on document published on 14 January 2025).
Publications
14
Citations
14
Publications by Year
20222025
Co-Authors:
Show All Co-Authors (13)
Author's Published Works

Showing 1-14 of 14 results

  • Sort

Filter Results

Show

Sort By

Results

Diagnosis of Demagnetization Faults in PMSGs Based on Performance Evaluation of Machine Learning Algorithms Through Flux and Current Signals

Nadeem Shahbaz;Yu Chen;Feng Liang;Sichao Zhang;Shouwang Zhao;Yong Ma;Chong Li;Wei Deng;Yong Zhao

IEEE Transactions on Instrumentation and Measurement
Year: 2025 | Volume: 74 | Journal Article |
This study proposed an effective machine learning (ML)-based fault diagnosis method for demagnetization faults, including “healthy, 30% unipolar demagnetization, 50% multimagnet demagnetization, 100% adjacent pole demagnetization, and 40% uniform demagnetization,” in a 25-kW permanent magnet synchronous generator (PMSG) that considers algorithm accuracy and signal processing technique efficiency, ...Show More

Diagnosis of Demagnetization Faults in PMSGs Based on Performance Evaluation of Machine Learning Algorithms Through Flux and Current Signals

Nadeem Shahbaz;Yu Chen;Feng Liang;Sichao Zhang;Shouwang Zhao;Yong Ma;Chong Li;Wei Deng;Yong Zhao

Year: 2025 | Volume: 74 | Journal Article |

Demagnetization Modeling and Fault Diagnosis in a 25-kW PMSG Using Finite Element Method and Machine Learning Techniques

Nadeem Shahbaz;Yu Chen;Sichao Zhang;Feng Liang;Siyu Du;Shouwang Zhao;Yong Ma;Chong Li;Yong Zhao

2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2024 | Conference Paper |
In wind power power systems, the reliable functioning of Permanent Magnet Synchronous Generators (PMSG) depends on condition monitoring and fault diagnosis. Using 3D simulation models, we present a diagnostic approach for identifying multiple demagnetization problems in PMSG. Specifically, we use the four states of demagnetization: healthy condition, 50% unipolar magnet breakage, 75% demagnetizati...Show More

Demagnetization Modeling and Fault Diagnosis in a 25-kW PMSG Using Finite Element Method and Machine Learning Techniques

Nadeem Shahbaz;Yu Chen;Sichao Zhang;Feng Liang;Siyu Du;Shouwang Zhao;Yong Ma;Chong Li;Yong Zhao

Year: 2024 | Conference Paper |

Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Generator for Wind Turbine Based on ResNet-50 Transfer Learning Model

Siyu Du;Yu Chen;Sichao Zhang;Feng Liang;Nadeem Shahbaz;Guoqiang Zhu;Shouwang Zhao;Shuang Wang;Yong Ma;Chong Li;Zhongjie Wang;Yong Zhao

2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2024 | Conference Paper |
This paper utilizes Convolutional Neural Networks (CNN) with Residual Networks (ResNet) to identify and classify demagnetization faults in images. Firstly, we collected current feature datasets corresponding to two types of permanent magnet wind turbines, namely 25 kW and 2 MW, representing the source and target domains. Using the source domain dataset, we trained a ResNet50 model specifically for...Show More

Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Generator for Wind Turbine Based on ResNet-50 Transfer Learning Model

Siyu Du;Yu Chen;Sichao Zhang;Feng Liang;Nadeem Shahbaz;Guoqiang Zhu;Shouwang Zhao;Shuang Wang;Yong Ma;Chong Li;Zhongjie Wang;Yong Zhao

Year: 2024 | Conference Paper |

Fault Diagnosis of Inter-Turn Short Circuits in Doubly-Fed Induction Generators Based on External Magnetic Field and Vibration Signal Fusion

Shouwang Zhao;Yu Chen;Nadeem Shahbaz;Sichao Zhang;Feng Liang;Shuang Wang;Yong Ma;Wei Deng;Yong Zhao

2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2024 | Conference Paper |
This manuscript introduces a novel diagnostic technique for identifying inter-turn short circuits in doubly-fed induction generators (DFIGs) by integrating multi-channel external magnetic field (MFL) and vibration signals. The approach begins with cepstral pre-whitening to mitigate noise and improve the identification of fault-related characteristics. Following this, a correlation analysis is cond...Show More

Fault Diagnosis of Inter-Turn Short Circuits in Doubly-Fed Induction Generators Based on External Magnetic Field and Vibration Signal Fusion

Shouwang Zhao;Yu Chen;Nadeem Shahbaz;Sichao Zhang;Feng Liang;Shuang Wang;Yong Ma;Wei Deng;Yong Zhao

Year: 2024 | Conference Paper |

Electrical Fault Diagnosis Based on Feature Extraction and Support Vector Machine for Permanent Magnet Synchronous Generator

Sichao Zhang;Yu Chen;Feng Liang;Siyu Du;Nadeem Shahbaz;Shouwang Zhao;Yong Ma;Wei Deng;Yong Zhao

2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2024 | Conference Paper |
During the operation of the permanent magnet wind generator, electrical faults such as winding short circuit, winding open circuit, and winding asymmetry may occur, which directly affects the regular operation of the wind turbine and adversely affects wind power generation. This paper proposes an electrical fault diagnosis method for permanent magnet generators based on feature extraction and Supp...Show More

Electrical Fault Diagnosis Based on Feature Extraction and Support Vector Machine for Permanent Magnet Synchronous Generator

Sichao Zhang;Yu Chen;Feng Liang;Siyu Du;Nadeem Shahbaz;Shouwang Zhao;Yong Ma;Wei Deng;Yong Zhao

Year: 2024 | Conference Paper |

Demagnetization Fault Diagnosis Based on Feature Extraction and Stacking Ensemble Learning for Permanent Magnet Synchronous Generator

Sichao Zhang;Yu Chen;Feng Liang;Siyu Du;Nadeem Shahbaz;Shouwang Zhao;Chong Li;Wei Deng;Yong Zhao

2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2024 | Conference Paper |
During the operation of a permanent magnet wind turbine, magnet demagnetization failure may occur, which directly affects the regular operation of the wind turbine and adversely affects wind power generation. This paper proposes a demagnetization fault diagnosis method for permanent magnet generators based on feature extraction and stacking integrated learning. A permanent magnet generator with a ...Show More

Demagnetization Fault Diagnosis Based on Feature Extraction and Stacking Ensemble Learning for Permanent Magnet Synchronous Generator

Sichao Zhang;Yu Chen;Feng Liang;Siyu Du;Nadeem Shahbaz;Shouwang Zhao;Chong Li;Wei Deng;Yong Zhao

Year: 2024 | Conference Paper |

Assessment of Machine Learning Techniques for Identifying Demagnetization Stress in PMSG Using Flux and Current Signals

Nadeem Shahbaz;Yu Chen;Feng Liang;Shouwang Zhao;Sichao Zhang;Shuang Wang;Yong Ma;Yong Zhao;Wei Deng

2024 Global Reliability and Prognostics and Health Management Conference (PHM-Beijing)
Year: 2024 | Conference Paper |
This study focuses on the detection of faults in wind turbine operations to ensure reliability, safety, and cost efficiency. It introduces a machine learning-based diagnostic approach for identifying various faults, including eccentricity faults, stator short circuits, and both damaged and aging magnets in a 25 kW Permanent Magnet Synchronous Generator (PMSG). A 2D simulation model utilizing the F...Show More

Assessment of Machine Learning Techniques for Identifying Demagnetization Stress in PMSG Using Flux and Current Signals

Nadeem Shahbaz;Yu Chen;Feng Liang;Shouwang Zhao;Sichao Zhang;Shuang Wang;Yong Ma;Yong Zhao;Wei Deng

Year: 2024 | Conference Paper |

Identifying Early Stator Fault Severity in DFIGs Based on Adaptive Feature Mode Decomposition and Multiscale Complex Component Current Trajectories

Shouwang Zhao;Yu Chen;Feng Liang;Sichao Zhang;Nadeem Shahbaz;Shuang Wang;Yong Zhao;Wei Deng;Yonghong Cheng

IEEE Transactions on Instrumentation and Measurement
Year: 2024 | Volume: 73 | Journal Article |
Early fault detection is critical for ensuring reliable operation in wind power generation systems employing doubly fed induction generators (DFIGs). Although the widespread use of motor current signature analysis (MCSA) for noninvasive fault detection, early-stage faults in DFIGs often present with weak characteristic fault information, posing challenges for detection amidst noise and interferenc...Show More

Identifying Early Stator Fault Severity in DFIGs Based on Adaptive Feature Mode Decomposition and Multiscale Complex Component Current Trajectories

Shouwang Zhao;Yu Chen;Feng Liang;Sichao Zhang;Nadeem Shahbaz;Shuang Wang;Yong Zhao;Wei Deng;Yonghong Cheng

Year: 2024 | Volume: 73 | Journal Article |

Detecting Incipient Inter-Turn Short Circuit Faults in DFIGs Based on Magnetic Flux Leakage Theory and MPJ-SVDD Analytical Method

Shouwang Zhao;Yu Chen;Feng Liang;Sichao Zhang;Nadeem Shahbaz;Shuang Wang;Yong Zhao;Wei Deng;Yonghong Cheng

2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
This manuscript presents the Magnetic Flux Leakage (MFL) theory for incipient inter-turn short circuit (ITSC) faults in the Doubly Fed Induction Generators (DFIGs), which involves the simulation and modeling of the failure mechanism, the quantitative and qualitative description analysis, the health indicator construction, and early weak fault detection and state evaluation based on MFL theory. Fir...Show More

Detecting Incipient Inter-Turn Short Circuit Faults in DFIGs Based on Magnetic Flux Leakage Theory and MPJ-SVDD Analytical Method

Shouwang Zhao;Yu Chen;Feng Liang;Sichao Zhang;Nadeem Shahbaz;Shuang Wang;Yong Zhao;Wei Deng;Yonghong Cheng

Year: 2023 | Conference Paper |

Classification of Demagnetization Faults based on Performance Evaluation of Machine Learning Algorithms via Flux and Vibration Signals in Permanent Magnet Wind Turbine Generator

Nadeem Shahbaz;Yu Chen;Feng Liang;Sichao Zhang;Shouwang Zhao;Yong Ma;Chong Li;Wei Deng;Yong Zhao

2023 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Fault diagnosis and condition monitoring play a significant role in wind turbines as they guarantee safety and reliability and avoid perilous conditions; therefore, fault diagnosis prior to its existence saves both time and costs. This paper proposes a machine learning-based fault diagnosis technique using vibration and leakage flux for multiple demagnetization faults, including “healthy, 30% unip...Show More

Classification of Demagnetization Faults based on Performance Evaluation of Machine Learning Algorithms via Flux and Vibration Signals in Permanent Magnet Wind Turbine Generator

Nadeem Shahbaz;Yu Chen;Feng Liang;Sichao Zhang;Shouwang Zhao;Yong Ma;Chong Li;Wei Deng;Yong Zhao

Year: 2023 | Conference Paper |

Multiscale Park's Vector Approach for Identifying Early Stator Fault Severity Using MCSA in DFIGs

Yu Chen;Shouwang Zhao;Feng Liang;Sichao Zhang;Nadeem Shahbaz;Shuang Wang;Yong Zhao;Wei Deng;Yonghong Cheng

2023 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Early fault detection of Doubly-fed Induction Generators (DFIG) is a key problem for reliable operation of wind power generation. Motor current signature analysis (MCSA) is the most reliable and widely used technique, gaining favor because it is non-invasive. In the initial stage of wind generator fault, the fault characteristic information is weak and easy to be covered by noise and interference ...Show More

Multiscale Park's Vector Approach for Identifying Early Stator Fault Severity Using MCSA in DFIGs

Yu Chen;Shouwang Zhao;Feng Liang;Sichao Zhang;Nadeem Shahbaz;Shuang Wang;Yong Zhao;Wei Deng;Yonghong Cheng

Year: 2023 | Conference Paper |

Demagnetization Fault Diagnosis Based on Feature Extraction and Convolutional Neural Network for Permanent Magnet Generator

Sichao Zhang;Yu Chen;Feng Liang;Nadeem Shahbaz;Shouwang Zhao;Yong Ma;Chong Li;Wei Deng;Yong Zhao

2023 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2023 | Conference Paper |
During the operation of permanent magnet wind turbines, magnetic steel demagnetization faults may occur, directly affecting the normal operation of wind turbines and having adverse effects on wind power generation. This article proposes a demagnetization fault diagnosis method for permanent magnet generators based on feature extraction and convolutional neural network. A permanent magnet generator...Show More

Demagnetization Fault Diagnosis Based on Feature Extraction and Convolutional Neural Network for Permanent Magnet Generator

Sichao Zhang;Yu Chen;Feng Liang;Nadeem Shahbaz;Shouwang Zhao;Yong Ma;Chong Li;Wei Deng;Yong Zhao

Year: 2023 | Conference Paper |

Evaluation Method of Performance Degradation based on End Magnetic Flux Leakage for Short Circuit Fault of Doubly Fed Induction Generator

Shouwang Zhao;Yu Chen;Feng Liang;Sichao Zhang;Nadeem Shahbaz;Shuang Wang;Yong Zhao;Wei Deng;Yonghong Cheng

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2022 | Conference Paper |
Cited by: Papers (5)
The health status of the Doubly Fed Induction Generator (DFIG) is related to the actual operating conditions, the external environment, the accumulation of sudden factors, and the coupling effect. The degradation feature extraction is mainly based on a single signal or multiple statistics of a single signal. The principal component components were extracted from the end magnetic flux leakage (MFL)...Show More

Evaluation Method of Performance Degradation based on End Magnetic Flux Leakage for Short Circuit Fault of Doubly Fed Induction Generator

Shouwang Zhao;Yu Chen;Feng Liang;Sichao Zhang;Nadeem Shahbaz;Shuang Wang;Yong Zhao;Wei Deng;Yonghong Cheng

Year: 2022 | Conference Paper |

Research and Application of Intelligent Diagnosis Technology in Permanent Magnet Generator for Stress Demagnetization Fault

Nadeem Shahbaz;Yu Chen;Feng Liang;Shouwang Zhao;Sichao Zhang;Shuang Wang;Yong Ma;Yong Zhao;Wei Deng

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Fault diagnosis before its existence and the complete shutdown is essentially critical for the whole industry. Fault diagnosis based on condition monitoring methods and artificial intelligence techniques are very potent. This paper assesses the machine-learning-based processes using air gap flux and stator current for eccentricity, magnet broken, and stator inter-turn short circuit faults in Perma...Show More

Research and Application of Intelligent Diagnosis Technology in Permanent Magnet Generator for Stress Demagnetization Fault

Nadeem Shahbaz;Yu Chen;Feng Liang;Shouwang Zhao;Sichao Zhang;Shuang Wang;Yong Ma;Yong Zhao;Wei Deng

Year: 2022 | Conference Paper |

IEEE Account

Purchase Details

Profile Information

Need Help?

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.