Damage estimation method using committee of neural networks

2003 ◽  
Author(s):  
Chung Bang Yun ◽  
Jong Won Lee ◽  
Jae Dong Kim ◽  
Kyung Won Min
Keyword(s):  
Neural Networks ◽  
Estimation Method ◽  
Damage Estimation ◽  
Committee Of Neural Networks

Spectral analysis of sine-sweep vibration: A fatigue damage estimation method

2021 ◽  
Vol 157 ◽  
pp. 107698
Author(s):  
M. Palmieri ◽  
F. Cianetti ◽  
G. Zucca ◽  
G. Morettini ◽  
C. Braccesi
Keyword(s):  
Spectral Analysis ◽  
Fatigue Damage ◽  
Estimation Method ◽  
Damage Estimation ◽  
Sine Sweep

An adaptive learning damage estimation method for structural health monitoring

2014 ◽  
Vol 26 (2) ◽  
pp. 125-143 ◽  
Author(s):  
Debejyo Chakraborty ◽  
Narayan Kovvali ◽  
Antonia Papandreou-Suppappola ◽  
Aditi Chattopadhyay
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Adaptive Learning ◽  
Estimation Method ◽  
Damage Estimation ◽  
Structural Health

scholarly journals Multiple Network Fusion with Low-Rank Representation for Image-Based Age Estimation

2018 ◽  
Vol 8 (9) ◽  
pp. 1601
Author(s):  
Chaoqun Hong ◽  
Zhiqiang Zeng ◽  
Xiaodong Wang ◽  
Weiwei Zhuang
Keyword(s):  
Neural Networks ◽  
Age Estimation ◽  
Feature Fusion ◽  
Estimation Method ◽  
Feature Space ◽  
Image Features ◽  
Low Rank ◽  
Face Images ◽  
Low Rank Representation ◽  
Multiple Network

Image-based age estimation is a challenging task since there are ambiguities between the apparent age of face images and the actual ages of people. Therefore, data-driven methods are popular. To improve data utilization and estimation performance, we propose an image-based age estimation method. Theoretically speaking, the key idea of the proposed method is to integrate multi-modal features of face images. In order to achieve it, we propose a multi-modal learning framework, which is called Multiple Network Fusion with Low-Rank Representation (MNF-LRR). In this process, different deep neural network (DNN) structures, such as autoencoders, Convolutional Neural Networks (CNNs), Recursive Neural Networks (RNNs), and so on, can be used to extract semantic information of facial images. The outputs of these neural networks are then represented in a low-rank feature space. In this way, feature fusion is obtained in this space, and robust multi-modal image features can be computed. An experimental evaluation is conducted on two challenging face datasets for image-based age estimation extracted from the Internet Move Database (IMDB) and Wikipedia (WIKI). The results show the effectiveness of the proposed MNF-LRR.

scholarly journals Fast battery capacity estimation using convolutional neural networks

2020 ◽  
pp. 014233122096642 ◽  
Author(s):  
Yihuan Li ◽  
Kang Li ◽  
Xuan Liu ◽  
Li Zhang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Smart Grids ◽  
Estimation Method ◽  
Lithium Ion ◽  
Accurate Estimation ◽  
Capacity Estimation ◽  
Starting Point ◽  
Battery Capacity ◽  
Mean Square Errors

Lithium-ion batteries have been widely used in electric vehicles, smart grids and many other applications as energy storage devices, for which the aging assessment is crucial to guarantee their safe and reliable operation. The battery capacity is a popular indicator for assessing the battery aging, however, its accurate estimation is challenging due to a range of time-varying situation-dependent internal and external factors. Traditional simplified models and machine learning tools are difficult to capture these characteristics. As a class of deep neural networks, the convolutional neural network (CNN) is powerful to capture hidden information from a huge amount of input data, making it an ideal tool for battery capacity estimation. This paper proposes a CNN-based battery capacity estimation method, which can accurately estimate the battery capacity using limited available measurements, without resorting to other offline information. Further, the proposed method only requires partial charging segment of voltage, current and temperature curves, making it possible to achieve fast online health monitoring. The partial charging curves have a fixed length of 225 consecutive points and a flexible starting point, thereby short-term charging data of the battery charged from any initial state-of-charge can be used to produce accurate capacity estimation. To employ CNN for capacity estimation using partial charging curves is however not trivial, this paper presents a comprehensive approach covering time series-to-image transformation, data segmentation, and CNN configuration. The CNN-based method is applied to two battery degradation datasets and achieves root mean square errors (RMSEs) of less than 0.0279 Ah (2.54%) and 0.0217 Ah (2.93% ), respectively, outperforming existing machine learning methods.

scholarly journals An Effective Tsunami Damage Estimation Method Considering Dike Height

2007 ◽  
Vol 54 ◽  
pp. 261-265
Author(s):  
Fuminori KATO ◽  
Masaya FUKUHAMA ◽  
Hiroyuki FUJII ◽  
Toshimitsu TAKAGI ◽  
Toshio KODAMA
Keyword(s):  
Estimation Method ◽  
Damage Estimation ◽  
Tsunami Damage

Analytical Method of Fatigue Damage Estimation for Compressor Stator Blades of Gas Turbines Experiencing Rotating Stall

2002 ◽  
Author(s):  
Yasushi Hayasaka ◽  
Shigeo Sakurai ◽  
Takeshi Kudo ◽  
Kunihiro Ichikawa
Keyword(s):  
Stress Analysis ◽  
Fatigue Damage ◽  
Analytical Method ◽  
Gas Turbines ◽  
Estimation Method ◽  
Rotating Stall ◽  
Environmental Data ◽  
Response Analysis ◽  
Damage Estimation ◽  
Stress Peak

To improve the reliability of compressor stator blades of gas turbines, an analytical method for estimating their fatigue damage has been developed. This method is based on blade-vibratory-stress analysis, stress-peak counting, and use of actual environmental data. The blade-vibratory-stress analysis takes the superposition of multi-peaks of the stress spectrum into account. The numerically simulated stress showed better agreement with measured stress than that from a conventional stress analysis, which is based on frequency-response analysis considering a single peak of the lowest single eigen-vibration-mode. A time-domain stress history was synthesized from the blade-vibratory-stress analysis results. Furthermore, the fatigue damage of the blade under rotating stall was estimated by the linear-damage-rule from the stress-peak counting of the stress and from material data. The estimated fatigue-damage agrees well with the measured results. This agreement means that our new fatigue-damage-estimation method is more accurate than the conventional one.

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