scholarly journals Evaluation method for moisture content of oil‐paper insulation based on segmented frequency domain spectroscopy: From curve fitting to machine learning

2021 ◽  
Author(s):  
Huanmin Yao ◽  
Haibao Mu ◽  
Ning Ding ◽  
Daning Zhang ◽  
ZhaoJie Liang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Moisture Content ◽  
Frequency Domain ◽  
Curve Fitting ◽  
Evaluation Method ◽  
Paper Insulation

scholarly journals A New Method for Evaluating Moisture Content and Aging Degree of Transformer Oil-Paper Insulation Based on Frequency Domain Spectroscopy

Energies ◽  
2017 ◽  
Vol 10 (8) ◽  
pp. 1195 ◽  
Author(s):  
Guoqiang Xia ◽  
Guangning Wu ◽  
Bo Gao ◽  
Haojie Yin ◽  
Feibao Yang
Keyword(s):  
Moisture Content ◽  
Frequency Domain ◽  
Transformer Oil ◽  
New Method ◽  
Paper Insulation

scholarly journals Time-Efficient Convolutional Neural Network-Assisted Brillouin Optical Frequency Domain Analysis

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2724
Author(s):  
Christos Karapanagiotis ◽  
Aleksander Wosniok ◽  
Konstantin Hicke ◽  
Katerina Krebber
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Curve Fitting ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Processing Method ◽  
Optical Frequency ◽  
Post Processing

To our knowledge, this is the first report on a machine-learning-assisted Brillouin optical frequency domain analysis (BOFDA) for time-efficient temperature measurements. We propose a convolutional neural network (CNN)-based signal post-processing method that, compared to the conventional Lorentzian curve fitting approach, facilitates temperature extraction. Due to its robustness against noise, it can enhance the performance of the system. The CNN-assisted BOFDA is expected to shorten the measurement time by more than nine times and open the way for applications, where faster monitoring is essential.

scholarly journals Machine Learning Empowered Trust Evaluation Method for IoT Devices

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 65066-65077
Author(s):  
Wei Ma ◽  
Xing Wang ◽  
Mingsheng Hu ◽  
Qinglei Zhou
Keyword(s):  
Machine Learning ◽  
Evaluation Method ◽  
Trust Evaluation ◽  
Iot Devices

scholarly journals Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy

2021 ◽  
Author(s):  
Michael J. Burns ◽  
Jonathan S. Renk ◽  
David P. Eickholt ◽  
Amanda M. Gilbert ◽  
Travis J. Hattery ◽  
...  
Keyword(s):  
Machine Learning ◽  
Moisture Content ◽  
Nir Spectroscopy

scholarly journals Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation

2021 ◽  
Vol 11 (3) ◽  
pp. 1084
Author(s):  
Peng Wu ◽  
Ailan Che
Keyword(s):  
Neural Network ◽  
Elastic Wave ◽  
Frequency Domain ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Monitoring And Evaluation ◽  
Strong Nonlinearity ◽  
Filling Process ◽  
Time Frequency ◽  
Feature Parameters

The sand-filling method has been widely used in immersed tube tunnel engineering. However, for the problem of monitoring during the sand-filling process, the traditional methods can be inadequate for evaluating the state of sand deposits in real-time. Based on the high efficiency of elastic wave monitoring, and the superiority of the backpropagation (BP) neural network on solving nonlinear problems, a spatiotemporal monitoring and evaluation method is proposed for the filling performance of foundation cushion. Elastic wave data were collected during the sand-filling process, and the waveform, frequency spectrum, and time–frequency features were analysed. The feature parameters of the elastic wave were characterized by the time domain, frequency domain, and time-frequency domain. By analysing the changes of feature parameters with the sand-filling process, the feature parameters exhibited dynamic and strong nonlinearity. The data of elastic wave feature parameters and the corresponding sand-filling state were trained to establish the evaluation model using the BP neural network. The accuracy of the trained network model reached 93%. The side holes and middle holes were classified and analysed, revealing the characteristics of the dynamic expansion of the sand deposit along the diffusion radius. The evaluation results are consistent with the pressure gauge monitoring data, indicating the effectiveness of the evaluation and monitoring model for the spatiotemporal performance of sand deposits. For the sand-filling and grouting engineering, the machine-learning method could offer a better solution for spatiotemporal monitoring and evaluation in a complex environment.

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