Bayesian neural networks for damage identification of a cable-stayed bridge

Damage identification of composite structures is a major ongoing challenge for a secure operational life-cycle due to the complex, gradual damage behaviour of composite materials. Especially for composite rotors in aero-engines and wind-turbines, a cost-intensive maintenance service has to be performed in order to avoid critical failure. A major advantage of composite structures is that they are able to safely operate after damage initiation and under ongoing damage propagation. Therefore, a robust, efficient diagnostic damage identification method would allow monitoring the damage process with intervention occurring only when necessary. This study investigates the structural vibration response of composite rotors by applying machine learning methods and the ability to identify, localise and quantify the present damage. To this end, multiple fully connected neural networks and convolutional neural networks were trained on vibration response spectra from damaged composite rotors with barely visible damage, mostly matrix cracks and local delaminations using dimensionality reduction and data augmentation. A databank containing 720 simulated test cases with different damage states is used as a basis for the generation of multiple data sets. The trained models are tested using k-fold cross validation and they are evaluated based on the sensitivity, specificity and accuracy. Convolutional neural networks perform slightly better providing a performance accuracy of up to 99.3% for the damage localisation and quantification.

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Bayesian Neural Networks with Maximum Mean Discrepancy regularization

Neurocomputing ◽

10.1016/j.neucom.2021.01.090 ◽

2021 ◽

Author(s):

Jary Pomponi ◽

Simone Scardapane ◽

Aurelio Uncini

Keyword(s):

Neural Networks ◽

Maximum Mean Discrepancy ◽

Bayesian Neural Networks

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Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization

10.1109/cvpr46437.2021.00381 ◽

2021 ◽

Author(s):

Jiaru Zhang ◽

Yang Hua ◽

Zhengui Xue ◽

Tao Song ◽

Chengyu Zheng ◽

...

Keyword(s):

Neural Networks ◽

Bayesian Neural Networks

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A Simple Method of Residential Electricity Load Forecasting by Improved Bayesian Neural Networks

Mathematical Problems in Engineering ◽

10.1155/2018/4276176 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 4

Author(s):

Shubin Zheng ◽

Qianwen Zhong ◽

Lele Peng ◽

Xiaodong Chai

Keyword(s):

Neural Networks ◽

Time Series ◽

Load Forecasting ◽

Forecast Model ◽

Time Range ◽

Simple Method ◽

Load Forecast ◽

Bayesian Neural Networks ◽

Electricity Load ◽

Electricity Load Forecasting

Electricity load forecasting is becoming one of the key issues to solve energy crisis problem, and time-series Bayesian Neural Network is one popular method used in load forecast models. However, it has long running time and relatively strong dependence on time and weather factors at a residential level. To solve these problems, this article presents an improved Bayesian Neural Networks (IBNN) forecast model by augmenting historical load data as inputs based on simple feedforward structure. From the load time delays correlations and impact factors analysis, containing different inputs, number of hidden neurons, historic period of data, forecasting time range, and range requirement of sample data, some advices are given on how to better choose these factors. To validate the performance of improved Bayesian Neural Networks model, several residential sample datasets of one whole year from Ausgrid have been selected to build the improved Bayesian Neural Networks model. The results compared with the time-series load forecast model show that the improved Bayesian Neural Networks model can significantly reduce calculating time by more than 30 times and even when the time or meteorological factors are missing, it can still predict the load with a high accuracy. Compared with other widely used prediction methods, the IBNN also performs a better accuracy and relatively shorter computing time. This improved Bayesian Neural Networks forecasting method can be applied in residential energy management.

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