Condition assessment of power transformers using genetic-based neural networks

2003 ◽  
Vol 150 (1) ◽  
pp. 19 ◽  
Author(s):  
Y.-C. Huang
Keyword(s):  
Neural Networks ◽  
Condition Assessment ◽  
Power Transformers

Urban Railway Bridge Fast Evaluation Based on Cloud Computing

2011 ◽  
Vol 71-78 ◽  
pp. 4501-4505
Author(s):  
Ming Chen ◽  
Wan Zhou
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Model Updating ◽  
Condition Assessment ◽  
Fe Model ◽  
Railway Bridge ◽  
Fast Evaluation ◽  
Railway Bridges ◽  
Computing Model ◽  
Bridge Condition Assessment

Although modern bridge are carefully designed and well constructed, damage may occur in them due to unexpected causes. Currently, many different techniques have been proposed and investigated in bridge condition assessment. However, evaluation efficiency of condition assessment has not been paid much attention by the researchers. A fast evaluation of the urban railway bridge condition based on the cloud computing is presented. In this paper dynamic FE model and Artificial neural networks technique is applied to model updating. The cloud computing model provides the basis for fast analyses. It was found that when applied to the actually railway bridges, the proposed method provided results similar to those obtained by experts, but can improve efficiency of bridge

Condition assessment of power transformers based on multi-attributes using fuzzy logic

2017 ◽  
Vol 11 (8) ◽  
pp. 983-990 ◽  
Author(s):  
Chilaka Ranga ◽  
Ashwani Kumar Chandel ◽  
Rajeevan Chandel
Keyword(s):  
Fuzzy Logic ◽  
Condition Assessment ◽  
Power Transformers

scholarly journals Condition Assessment Techniques for Insulation Diagnosis of Oil Filled Power Transformers

2019 ◽  
Vol 14 (2) ◽  
pp. 126-131
Author(s):  
Dillip Kumar Puhan ◽  
Rajat Sharma ◽  
B. Nageshwar Rao ◽  
K. P. Meena ◽  
◽  
...  
Keyword(s):  
Condition Assessment ◽  
Power Transformers ◽  
Assessment Techniques

Remote Monitoring Tool for Condition Assessment of Power Transformers

2019 ◽  
Author(s):  
Pubudu Haputhanthirige ◽  
Stephan Fernando ◽  
Thirasara Gunaruwan ◽  
Vimukthi Gamage ◽  
Rasara Samarasinghe ◽  
...  
Keyword(s):  
Remote Monitoring ◽  
Condition Assessment ◽  
Power Transformers ◽  
Monitoring Tool

On-line condition assessment of power transformers using wideband FBG based acoustic sensor

2015 ◽  
Author(s):  
B. Sarkar ◽  
C. Koley ◽  
N. K. Roy ◽  
P. Kumbhakar
Keyword(s):  
Condition Assessment ◽  
Power Transformers ◽  
Acoustic Sensor ◽  
On Line ◽  
Line Condition

scholarly journals Deep Learning for Detecting Building Defects Using Convolutional Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3556 ◽  
Author(s):  
Husein Perez ◽  
Joseph H. M. Tah ◽  
Amir Mosavi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Effective Means ◽  
Scale Up ◽  
Real Life ◽  
Health And Safety ◽  
Condition Assessment ◽  
Automated Detection ◽  
Proposed Model ◽  
Current Asset

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of work commonly comprise of engaging building surveyors to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the physical condition of the building elements, including cost estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time consuming, laborious, and expensive and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access. This paper aims at evaluating the application of convolutional neural networks (CNN) towards an automated detection and localisation of key building defects, e.g., mould, deterioration, and stain, from images. The proposed model is based on pre-trained CNN classifier of VGG-16 (later compaired with ResNet-50, and Inception models), with class activation mapping (CAM) for object localisation. The challenges and limitations of the model in real-life applications have been identified. The proposed model has proven to be robust and able to accurately detect and localise building defects. The approach is being developed with the potential to scale-up and further advance to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones.

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