System Independent Fault Diagnosis for Synchronous Generator

Jeet Gandhi, R. Gopinath, and C. Santhosh Kumar
Publication Target: 
IJPHM
Publication Issue: 
2
Submission Type: 
Full Paper
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ijphm_17_030.pdf617.9 KBNovember 27, 2017 - 9:22am

Creating a unified fault diagnosis model that can detect faults across systems with different ratings (system independent fault diagnosis) would be of great interest in making condition-based maintenance (CBM) more popular.
In this work, three phase synchronous generators with 3 and 5 kVA ratings are used for detecting stator inter-turn short circuit faults.

Our baseline is a 3 kVA generator working at 1 A load during training and testing, to emulate the system/load dependent fault diagnosis. We obtained a classification accuracy of 99.75%, 100% and 100% for R phase, Y phase and B phase faults respectively.
Subsequently, we evaluated the system for its load independent performance. Performance accuracy deteriorated due to the load specific variations (LSV) in the input feature vector (IFV). LSV is undesired, and we used nuisance attribute projection (NAP) to remove them. Using NAP, we obtained a performance improvement of 23.13%, 17.75% and 20.72% for three fault models on the 3 kVA generator and similar performance improvement was obtained for 5 kVA generator also.

Further, we experimented for load and system independent fault diagnosis. In this case, we consider LSV and system specific variations (SSV) on IFV as undesired. We experimented with two types of NAP, (1) single step NAP, (2) stacked NAP. Experimental results show that the two staged stacked NAP outperforms.
We obtained an improvement of 23.99%, 16.06% and 28.39%, in classification accuracy for three fault models, resulting in overall classification accuracy of 89.22%, 94.67% and 94.59% for R phase, Y phase and B phase fault models respectively.

Publication Year: 
2017
Publication Volume: 
8
Publication Control Number: 
030
Page Count: 
11
Submission Keywords: 
Machine fault diagnosis
Synchronous generator
Discrete wavelet transform
Support Vector Machine
Inter-turn fault
Nuisance attribute projection
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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