Incipient fault diagnosis in power transformers by data-driven models with over-sampled dataset

2021 ◽  
Vol 201 ◽  
pp. 107519
Author(s):  
Sofia Moreira de Andrade Lopes ◽  
Rogério Andrade Flauzino ◽  
Ruy Alberto Corrêa Altafim
Keyword(s):  
Fault Diagnosis ◽  
Data Driven ◽  
Power Transformers ◽  
Incipient Fault ◽  
Incipient Fault Diagnosis

A nearest neighbour clustering approach for incipient fault diagnosis of power transformers

2016 ◽  
Vol 99 (3) ◽  
pp. 1109-1119 ◽  
Author(s):  
Md Mominul Islam ◽  
Gareth Lee ◽  
Sujeewa Nilendra Hettiwatte
Keyword(s):  
Fault Diagnosis ◽  
Power Transformers ◽  
Nearest Neighbour ◽  
Clustering Approach ◽  
Incipient Fault ◽  
Incipient Fault Diagnosis

scholarly journals Rolling Bearing Incipient Fault Diagnosis Method Based on Improved Transfer Learning with Hybrid Feature Extraction

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7894
Author(s):  
Zhengni Yang ◽  
Rui Yang ◽  
Mengjie Huang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Data Distribution ◽  
Rolling Bearing ◽  
Data Driven ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Incipient Fault ◽  
Incipient Fault Diagnosis

Data-driven based rolling bearing fault diagnosis has been widely investigated in recent years. However, in real-world industry scenarios, the collected labeled samples are normally in a different data distribution. Moreover, the features of bearing fault in the early stages are extremely inconspicuous. Due to the above mentioned problems, it is difficult to diagnose the incipient fault under different scenarios by adopting the conventional data-driven methods. Therefore, in this paper a new unsupervised rolling bearing incipient fault diagnosis approach based on transfer learning is proposed, with a novel feature extraction method based on a statistical algorithm, wavelet scattering network, and a stacked auto-encoder network. Then, the geodesic flow kernel algorithm is adopted to align the feature vectors on the Grassmann manifold, and the k-nearest neighbor classifier is used for fault classification. The experiment is conducted based on two bearing datasets, the bearing fault dataset of Case Western Reserve University and the bearing fault dataset of Xi’an Jiaotong University. The experiment results illustrate the effectiveness of the proposed approach on solving the different data distribution and incipient bearing fault diagnosis issues.

scholarly journals Application of Parzen Window estimation for incipient fault diagnosis in power transformers

High Voltage ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 303-309 ◽  
Author(s):  
Md Mominul Islam ◽  
Gareth Lee ◽  
Sujeewa Nilendra Hettiwatte
Keyword(s):  
Fault Diagnosis ◽  
Power Transformers ◽  
Parzen Window ◽  
Incipient Fault ◽  
Incipient Fault Diagnosis
Sign in / Sign up

Export Citation Format

Share Document