2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)最新文献_第10页

An Intelligent Bearing Fault Diagnosis based on Modified Probabilistic Knowledge Distillation 基于改进概率知识精馏的轴承故障智能诊断

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612949

Ziqian Shen, Wei Guo

{"title":"An Intelligent Bearing Fault Diagnosis based on Modified Probabilistic Knowledge Distillation","authors":"Ziqian Shen, Wei Guo","doi":"10.1109/PHM-Nanjing52125.2021.9612949","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612949","url":null,"abstract":"Knowledge distillation (KD) is one of popular algorithms for compressing deep neural networks because it generates a compact but still powerful deep neural network for the cases of complicated situations and limited computation resources. In this study, an intelligent fault diagnosis method is developed based on the probabilistic knowledge distillation (PKD) and deep convolutional neural network (CNN) to determine the health states of bearings. First, the one-dimensional vibration signal is reshaped as a two-dimensional matrix to input the teacher or student network. Then, a deeper neural network and small network are trained as the teacher and student networks, respectively. The probability distribution (PD) is learned by minimizing the difference of the joint density probability estimation between the teacher and student networks, that is, the lightweight network learns to integrate the PD of the deeper neural network in the high-dimensional feature space and realizes the knowledge transfer from training samples to test samples. The results of experimental bearings indicate that the proposed diagnosis method has higher diagnosis accuracy than the other two popular knowledge distillation methods and its student network only has about one 700-th parameter of the teacher network. Therefore, the proposed method achieves a good balance between the classification accuracy and network compression, and demonstrates potential application to intelligent fault diagnosis of bearings under varying working conditions.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"183 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133384005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

UAV Actuator Fault Detection using Maximal Information Coefficient and 1-D Convolutional Neural Network 基于最大信息系数和一维卷积神经网络的无人机执行器故障检测

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9613071

Na Wang, Jie Ren, Yue Luo, Kaihua Guo, Datong Liu

{"title":"UAV Actuator Fault Detection using Maximal Information Coefficient and 1-D Convolutional Neural Network","authors":"Na Wang, Jie Ren, Yue Luo, Kaihua Guo, Datong Liu","doi":"10.1109/PHM-Nanjing52125.2021.9613071","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9613071","url":null,"abstract":"Actuator is a critical part of the unmanned aerial vehicle (UAV), for which accurate and speedy fault detection is of great significance in practical application. Data-driven method becomes more appealing due to its feasibility and high performance. However, the current fault detection method based on machine learning cannot realize feature selection and real-time detection, and its feature extraction and learning ability of time series is not high enough. To solve the above problems, we propose a new fault detection method based on maximal information coefficient and one dimensional convolutional neural network (MIC-1DCNN) approach. It combines the high feature extraction ability of one dimensional convolutional neural network (1DCNN) for time series and the good feature selection ability of maximal information coefficient (MIC) for nonlinear data, which complete UAV actuator fault detection well and improve its efficiency greatly. The benchmark flight data set of the UAV is adopted for conducting experimental verification. The experimental results indicate that the proposed method can achieve satisfied performance in UAV actuator fault detection regarding speed and accuracy indices.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124611024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research On Vibration Reduction Of Regular Hexahedral Honeycomb Structure With Periodic Strut 带周期支撑的正六面体蜂窝结构减振研究

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612991

Bin Wu, Xinhang Shen, Qingpeng Han, Rui Zhu, Daolei Wang, Binxia Yuan

{"title":"Research On Vibration Reduction Of Regular Hexahedral Honeycomb Structure With Periodic Strut","authors":"Bin Wu, Xinhang Shen, Qingpeng Han, Rui Zhu, Daolei Wang, Binxia Yuan","doi":"10.1109/PHM-Nanjing52125.2021.9612991","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612991","url":null,"abstract":"The honeycomb cavity periodic strut structure with 3D printing technology can achieve the purpose of lightweight and vibration reduction on the basis of ensuring the stiffness and strength of the bar. In this paper, the stiffness characteristics and antivibration (resonance / flutter) ability of solid strut and regular hexahedral honeycomb periodic strut are studied by finite element analysis. The results show that the maximum deformation and maximum stress of honeycomb periodic strut are greater than that of solid strut. The maximum deformation of two kinds of rods occurs at the top of the rod, and the maximum stress of solid strut occurs at the root of the rod. The maximum stress of the honeycomb periodic strut occurs at the root of the internal honeycomb structure near the fixed end. The first six frequencies of the regular hexahedral honeycomb periodic strut are lower than those of the solid strut. The first five and seventh modes of the regular hexahedral honeycomb periodic strut are the same. The sixth, eighth, ninth and tenth modes of deformation are different.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131736641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Correntropy Induced Metric and Cyclic Correntropy Spectrum Method Combined With Singular Value Decomposition for Weak Signal Detection 结合奇异值分解的熵致度量法和循环熵谱法弱信号检测

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612773

Siqi Gong, Jiantao Lu, Shunming Li, Huijie Ma, Wang Yan-feng, Teng Guang-rong

{"title":"The Correntropy Induced Metric and Cyclic Correntropy Spectrum Method Combined With Singular Value Decomposition for Weak Signal Detection","authors":"Siqi Gong, Jiantao Lu, Shunming Li, Huijie Ma, Wang Yan-feng, Teng Guang-rong","doi":"10.1109/PHM-Nanjing52125.2021.9612773","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612773","url":null,"abstract":"In recent years, as a simple and effective method of noise reduction, singular value decomposition (SVD) has been widely concerned and applied. The idea of SVD to denoising is mainly to drop out singular components (SCs) with small singular value (SV), which ignores the weak signals buried in strong noise. Aiming to extract the weak signals in strong noise, this paper proposed a method of selecting SCs by the correntropy induced metric (CIM). Then the frequency components of characteristic signals can be found through cyclic correntropy spectrum (CCES) which is the extension of the correntropy (CE). The proposed method SVD-CIM firstly performs SVD on the signal, secondly calculates the CIM between SCs and the original signal, thirdly selects the SCs by CIM, fourthly reconstructs the retained SCs, and finally performs the CCES on the reconstructed signal to enhance the frequency of the characteristic signal. Experimental results have demonstrated that the proposed method can enhance the weak signal features effectively.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134093938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Design of Multimedia Assisted English online teaching model based on flipped classroom 基于翻转课堂的多媒体辅助英语在线教学模式设计

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612661

Yingying Wu

引用次数: 1

Cross-domain Intelligent Fault Diagnosis Using Transferable Bilinear Neural Network 基于可转移双线性神经网络的跨域智能故障诊断

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612986

Yimin Jiang, L. Cao, Rourou Li, Kaigan Zhang, Tangbin Xia

{"title":"Cross-domain Intelligent Fault Diagnosis Using Transferable Bilinear Neural Network","authors":"Yimin Jiang, L. Cao, Rourou Li, Kaigan Zhang, Tangbin Xia","doi":"10.1109/PHM-Nanjing52125.2021.9612986","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612986","url":null,"abstract":"The effectiveness of conventional deep learning-based intelligent fault diagnosis models depends on the training data and testing data following the same probability distribution. But the discrepancy in cross-domain distributions is inherent because of changes in external and internal conditions, resulting in a decline in diagnosis performance. Recently, transfer learning is employed to induce an adaptive diagnosis network in the scenario of distribution discrepancies. However, little attention has been paid to fully consider the cross-layer interaction and feature transferability for traditional transfer learning-based diagnosis networks. To overcome these problems, this paper presents a novel transferable bilinear neural network for cross-domain diagnosis. First, the bilinear map between bi-layer features is used to implement a novel information fusion and significantly improves the feature representation capability. It also realizes the embedding of bi-layer joint distributions into the reproducing kernel Hilbert space. Based on the embedding and feature transferability analysis, a reliable adaptive framework is designed to enable effective cross-domain transfer learning. The effectiveness of the proposed approach is validated using experiments with various transfer scenarios.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132233201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Accelerated Degradation Testing Method for Quantifying Lifetime of DC-DC Power Supply 一种量化直流-直流电源寿命的加速退化试验方法

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612767

Qingchuan He, Jun Pan, Wh H. Chen

引用次数: 0

PHM-Nanjing 2021 Blank Page phm -南京2021空白页

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/phm-nanjing52125.2021.9612822

引用次数: 0

Imbalanced Fault Diagnosis of Bearing-Rotor System via Normalized Conditional Variational Auto-Encoder with Adaptive Focal Loss 自适应焦损归一化条件变分自编码器诊断轴承-转子系统不平衡故障

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612924

Xiaoli Zhao, Jianyong Yao, W. Deng, M. Jia

引用次数: 1

Elevator Performance Evaluation Based on the Analysis of the Running Sound 基于运行声分析的电梯性能评价

2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Pub Date : 2021-10-15 DOI: 10.1109/PHM-Nanjing52125.2021.9612891

Jun Pan, Hui Li, Wenhua Chen, Yimin Wei

引用次数: 0