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Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and one-dimensional convolutional neural network

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Chinese Journal of Mechanical Engineering 2023年第3期36卷 252-261页

作者： Shengkang Zong Sheng Wang Zhitao Luo Xinkai Wu Hui Zhang Zhonghua Ni Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments School of Mechanical EngineeringSoutheast UniversityNanjing 211189China

Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of civil and mechanical structures.This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network(1D-CNN).After singular value decomposition-based feature extraction processing,a temporal robust damage index(TRDI)is extracted,and the effect of EOCs is well removed.Hence,even for the signals with a very large temperature-varying range and low signal-to-noise ratios(SNRs),the final damage detection and localization accuracy retain perfect 100%.Verifications are conducted on two different experimental datasets.The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises,and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20℃to 60℃.It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly,showing great potential for application in complex and unknown EOC.

关键词： Ultrasonic guided waves Singular value decomposition Damage detection and localization Environmental and operational conditions one-dimensional convolutional neural network

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Fault Line Detection Using Waveform Fusion and one-dimensional convolutional neural network in Resonant Grounding Distribution Systems

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CSEE Journal of Power and Energy Systems 2021年第2期7卷 250-260页

作者： Jianhong Gao Moufa Guo Duan-Yu Chen College of Electrical Engineering and Automation Fuzhou UniversityFuzhou 350108China Department of Electrical Engineering Yuan Ze UniversityTaoyuan 32003TaiwanChina

Effective features are essential for fault diagnosis.Due to the faint characteristics of a single line-to-ground(SLG)fault,fault line detection has become a challenge in resonant grounding distribution systems.This paper proposes a novel fault line detection method using waveform fusion and one-dimensional convolutional neural networks(1-D CNN).After an SLG fault occurs,the first-half waves of zero-sequence currents are collected and superimposed with each other to achieve waveform fusion.The compelling feature of fused waveforms is extracted by 1-D CNN to determine whether the fused waveform source contains the fault line.Then,the 1-D CNN output is used to update the value of the counter in order to identify the fault line.Given the lack of fault data in existing distribution systems,the proposed method only needs a small quantity of data for model training and fault line detection.In addition,the proposed method owns fault-tolerant performance.Even if a few samples are misjudged,the fault line can still be detected correctly based on the full output results of 1-D CNN.Experimental results verified that the proposed method can work effectively under various fault conditions.

关键词： Fault line detection one-dimensional convolutional neural network resonant grounding distribution systems waveform fusion

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1D-convolutional neural network based Vibration Signal Fault Detection

1D-Convolutional Neural Network based Vibration Signal Fault...

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第32届中国过程控制会议(CPCC2021)

作者： Jixiang Wei Peng Lin Shicheng Yu Jiuwen Cao Xiaoping Lai Machine Learning and I-health International Cooperation Base of Zhejiang Province and with the Artificial Intelligence InstituteHangzhou Dianzi University

convolutional neural networks(CNNs) are very good at picking up on patterns in the input image and have become widely used in computer vision.With the help of the excellent structured data processing ability of CNNs,a one-dimensional convolutional neural network(1 D-CNN) is constructed for fault detection and classification of time series data from vibration system of rotating machinery under various operating conditions.Firstly,a compact 1 D-CNN model with a wide convolution kernel is proposed in the first layer,and Dropout technology is used in the convolution layer to simulate training interference to enhance the anti-noise ability.Secondly,a double 1 D-CNN model is proposed for cross-condition fault detection.Finally,the effectiveness of the method is verified by simulation of compressor instability data.

关键词： Fault detection one-dimensional convolutional neural network Domain adaptation Rotating machinery

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