Weak fault detection method of rolling bearing based on testing signal far away from fault source

Zhiyuan He,Guo Chen,Tengfei Hao,Chunyu Teng,Minli Hou,Zhenjie Chen 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.3

In some cases, because of the complex internal structure of the machines, the positions of the vibration sensors are far away from the rolling bearings, such as in an aeroengine, causing the fault features to become extremely weak, which brings great challenge to the detection of rolling bearings. To address this problem, an integrated detection method is proposed. First, a method named MEDL is proposed to determine the optimal filter length in minimum entropy deconvolution (MED) to enhance the periodic fault impulse component in the weak signal, which accuracy is 1. After that, the MEDL is combined with variational mode decomposition (VMD) and autocorrelation to extract fault features from strong background noise. A series of fault simulation experiments for rolling bearings were conducted by using an aeroengine rotor experimental rig with casing. The results verify that the accuracy of the integrated detection method is 100 % in different measuring points, speeds and fault types. At the same time, it compared with spectral kurtosis (SK) and empirical wavelet transform (EWT). It proves that the integrated detection method is more robust in extracting the weak fault characteristic of rolling bearings from the casing signals effectively.

Pole-to-Ground Fault Analysis for MMC-HVDC Grid

Zhen He,Jiabing Hu,Lei Lin,Zhiyuan He 전력전자학회 2019 ICPE(ISPE)논문집 Vol.2019 No.5

Pole-to-ground (PTG) fault analysis is of vital importance for HVDC grid. However, many factors are not considered in the existing studies, such as the asymmetrical property of PTG fault, the coupling issue between dc transmission lines and the complexity of dc grid’s structure. This paper presents a PTG fault analysis method, which is based on common- and differential-mode (CDM) transformation. Similar to the symmetrical component method in ac system, the transformation decomposes the HVDC grid into CDM networks at first. Then, under the perspective of CDM components, a transfer impedance based analysis is performed to obtain the analytical expressions of PTG fault characteristics. The proposed PTG fault analysis method is applicable to arbitrary HVDC grid topologies. And the analytical expressions can give theoretical guidance for fault protection. The validity of the proposed PTG fault analysis method is verified by comparison with the simulation results in PSCAD/EMTDC.

Preparation of Bi/Bi2MoO6 Plasmonic Photocatalyst with High Photocatalytic Activity Under Visible Light Irradiation

Chentao Zou,Zhiyuan Yang,Mengjun Liang,Yunpeng He,Yun Yang,Shuijin Yang 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2018 NANO Vol.13 No.11

Bi metal deposited on Bi2MoO6 composite photocatalysts have been successfully synthesized via a simple reduction method at room temperature with using NaBH4 as the reducing agent. The photocatalytic activity of the composite was evaluated by degradation of rhodamine B (RhB) and bisphenol A (BPA) solution under visible light. The rate constant of Bi/Bi2MoO6 composite to RhB is 10.8 times that of Bi2MoO6, and the degradation rate constant of BPA is 6.9 times of that of Bi2MoO6. Nitrogen absorption–desorption isotherm proved that the increase of specific surface area is one of the reasons for the improvement of photocatalytic degradation activity of Bi/Bi2MoO6 composites. The higher charge transfer efficiency of Bi/Bi2MoO6 is found through the characterization of the photocurrent and impedance, which are attributed to the surface plasmon resonance (SPR) effect produced by the introduction of the metal Bi monomer in the composite. Free radical capture experiments proved that cavitation is the main active species. Based on the above conclusions, a possible mechanism of photocatalytic degradation is proposed.

Power Loss and Junction Temperature Analysis in the Modular Multilevel Converters for HVDC Transmission Systems

Haitian Wang,Guangfu Tang,Zhiyuan He,Junzheng Cao 전력전자학회 2015 JOURNAL OF POWER ELECTRONICS Vol.15 No.3

The power loss of the controllable switches in modular multilevel converter (MMC) HVDC transmission systems is an important factor, which can determine the design of the operating junction temperatures. Due to the dc current component, the approximate calculation tool provided by the manufacturer of the switches cannot be used for the losses of the switches in the MMC. Based on the enabled probabilities of each SM in an arm, the current analytical models of the switches can be determined. The average and RMS currents can be obtained from the corresponding current analytical model. Then, the conduction losses can be calculated, and the switching losses of the switches can be estimated according to the upper limit of the switching frequency. Finally, the thermal resistance model of the switches can be utilized, and the junction temperatures can be estimated. A comparison between the calculation and PSCAD simulation results shows that the proposed method is effective for estimating the junction temperatures of the switches in the MMC.

A three-stage deep-learning-based method for crack detection of high-resolution steel box girder image

Ying Zhou,Shiqiao Meng,Zhiyuan Gao,Bin He,Qingzhao Kong 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1

Crack detection plays an important role in the maintenance and protection of steel box girder of bridges. However, since the cracks only occupy an extremely small region of the high-resolution images captured from actual conditions, the existing methods cannot deal with this kind of image effectively. To solve this problem, this paper proposed a novel three-stage method based on deep learning technology and morphology operations. The training set and test set used in this paper are composed of 360 images (4928 × 3264 pixels) in steel girder box. The first stage of the proposed model converted highresolution images into sub-images by using patch-based method and located the region of cracks by CBAM ResNet-50 model. The <i>Recall</i> reaches 0.95 on the test set. The second stage of our method uses the Attention U-Net model to get the accurate geometric edges of cracks based on results in the first stage. The <i>IoU</i> of the segmentation model implemented in this stage attains 0.48. In the third stage of the model, we remove the wrong-predicted isolated points in the predicted results through dilate operation and outlier elimination algorithm. The <i>IoU</i> of test set ascends to 0.70 after this stage. Ablation experiments are conducted to optimize the parameters and further promote the accuracy of the proposed method. The result shows that: (1) the best patch size of sub-images is 1024 × 1024. (2) the CBAM ResNet-50 and the Attention U-Net achieved the best results in the first and the second stage, respectively. (3) Pre-training the model of the first two stages can improve the <i>IoU</i> by 2.9%. In general, our method is of great significance for crack detection.

Power Loss and Junction Temperature Analysis in the Modular Multilevel Converters for HVDC Transmission Systems

Wang, Haitian,Tang, Guangfu,He, Zhiyuan,Cao, Junzheng The Korean Institute of Power Electronics 2015 JOURNAL OF POWER ELECTRONICS Vol.15 No.3

The power loss of the controllable switches in modular multilevel converter (MMC) HVDC transmission systems is an important factor, which can determine the design of the operating junction temperatures. Due to the dc current component, the approximate calculation tool provided by the manufacturer of the switches cannot be used for the losses of the switches in the MMC. Based on the enabled probabilities of each SM in an arm, the current analytical models of the switches can be determined. The average and RMS currents can be obtained from the corresponding current analytical model. Then, the conduction losses can be calculated, and the switching losses of the switches can be estimated according to the upper limit of the switching frequency. Finally, the thermal resistance model of the switches can be utilized, and the junction temperatures can be estimated. A comparison between the calculation and PSCAD simulation results shows that the proposed method is effective for estimating the junction temperatures of the switches in the MMC.

Preparation and Photoelectric Properties of Silver Nanowire/ZnO Thin Film Ultraviolet Detector

Zhenfeng Li,Wei Xiao,Hongzhi Zhou,Zhiyuan Shi,Rongqing Li,Jia Zhang,Yang Li,Peng He,Shuye Y. Zhang 대한금속·재료학회 2023 ELECTRONIC MATERIALS LETTERS Vol.19 No.5

Ultraviolet (UV) detectors have important applications in many fi elds. ZnO is an excellent semiconductor material for the preparation of UV detectors because of its large direct gap in forbidden bandwidth, its intrinsic response band in the UV region, and its high exciton binding energy. In this paper, high-performance ZnO thin fi lms with the optically advantageous nonpolar structure were prepared by using an atomic layer deposition, and the dominant crystal plane gradually changes from the amorphous phase to the (100) crystal plane. The conventional photoconductor structure ZnO UV detector was enhanced by the surface plasmon exciton eff ect of Ag nanostructure. When the operating voltage is 5 V and the response light is 350 nm, there is a maximum optical responsiveness of up to 131 A/W. The UV/visible rejection ratio can reach 1824 times. When the ZnO thin fi lm deposition thickness is 400 deposition cycles and about 72 nm, the ZnO thin fi lm UV detector obtains the highest responsiveness (5 V, 365 nm) of 365 A/W. Comparing the photovoltaic performance of the ZnO thin-fi lm detector with the enhanced ZnO thin-fi lm detector and its optimal response wavelength, it is found that the enhanced ZnO thin-fi lm detector increased the photoresponse value by about 100 times. The optimal response wavelength in the UV region is blueshifted, and the UV-visible rejection ratio and optical response rate are signifi cantly improved.

Rolling bearing fault convolutional neural network diagnosis method based on casing signal

Xiangyang Zhang,Guo Chen,Tengfei Hao,Zhiyuan He 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.6

Affected by the transmission path, it is very difficult to diagnose the vibration signal of the rolling bearing on the aircraft engine casing. A fault diagnosis method based on convolutional neural network is proposed for the weak vibration signal of the casing under the excitation of rolling bearing fault. Firstly, the processing method of vibration signal is studied. Through comparison and analysis, it is found that the fault characteristics of rolling bearing are more easily expressed by continuous wavelet scale spectrum, and a better recognition rate is obtained. Finally, the experiment was carried out with an aero-engine rotor tester with a casing, and the method based on wavelet scale spectrum and convolutional neural network was used for diagnosis. The results were compared with the support vector machine method. The results show that the method has a high recognition rate for the weak fault signals of different fault types collected on the aero engine case, and its fault recognition rate reaches 95.82 %, which verifies the superiority and potential of the method for rolling bearing fault diagnosis.

A dual-experience pool deep reinforcement learning method and its application in fault diagnosis of rolling bearing with unbalanced data

Yuxiang Kang,Guo Chen,Wenping Pan,Xunkai Wei,Hao Wang,Zhiyuan He 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.6

A dual-experience pool deep reinforcement learning (DEPDRL) model is proposed for rolling bearing fault diagnosis with unbalanced data. In this method, a dualexperience pool structure is designed to store the sample data of majority and minority classes. A parallel double residual network model is established to extract deep features of the majority and minority input samples, respectively. In the process of training, the proposed balanced cross-sampling technique is used to randomly select samples from dual-experience pool in a certain proportion to realize the training of a double residual network model. We show the effectiveness of our method on three standard data sets, and compared with Resnet18, DCNN, DQN and DQNimb methods, the results show that DEPDRL has the best performance. Finally, with wavelet time-frequency graph as input, DEPDRL is applied to rolling bearing fault diagnosis with unbalanced test data. The results show that on a variety of unbalanced data sets, both the diagnostic accuracy and the G-means value of the DEPDRL are more than 5 % higher than other algorithms, which fully indicates that the DEPDRL has a very high fault diagnosis ability of rolling bearing with unbalanced data.

Molecular detection and genetic diversity of bovine papillomavirus in dairy cows in Xinjiang, China

Qingling Meng,Chengcheng Ning,Lixia Wang,Yan Ren,Jie Li,Chencheng Xiao,Yanfang Li,Zhiyuan Li,Zhihao He,Xuepeng Cai,Jun Qiao 대한수의학회 2021 Journal of Veterinary Science Vol.22 No.4

Background: Bovine papillomatosis is a type of proliferative tumor disease of skin and mucosae caused by bovine papillomavirus (BPV). As a transboundary and emerging disease in cattle, it poses a potential threat to the dairy industry. Objectives: The aim of this study is to detect and clarify the genetic diversity of BPV circulating in dairy cows in Xinjiang, China. Methods: 122 papilloma skin lesions from 8 intensive dairy farms located in different regions of Xinjiang, China were detected by polymerase chain reaction. The genetic evolution relationships of various types of BPVs were analyzed by examining this phylogenetic tree. Results: Ten genotypes of BPV (BPV1, BPV2, BPV3, BPV6, BPV7, BPV8, BPV10, BPV11, BPV13, and BPV14) were detected and identified in dairy cows. These were the first reported detections of BPV13 and BPV14 in Xinjiang, Mixed infections were detected, and there were geographical differences in the distribution of the BPV genotypes. Notably, the BPV infection rate among young cattle (< 1-year-old) developed from the same supply of frozen sperm was higher than that of the other young cows naturally raised under the same environmental conditions. Conclusions: Genotyping based on the L1 gene of BPV showed that BPVs circulating in Xinjiang China displayed substantial genetic diversity. This study provided valuable data at the molecular epidemiology level, which is conducive to developing deep insights into the genetic diversity and pathogenic characteristics of BPVs in dairy cows.