Pub Date : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00029
O. Mohammed, Sadok Sassi
This paper aims to propose an experimental method to detect and quantify the existence of cracks in spur gear teeth using modal analysis. Frequency Response Functions FRFs obtained analytically in previous work are considered to be validated experimentally in the current work. The analytical method using FRFs, which were applied in Ref. [28] for certain crack sizes, is validated here experimentally by using hammer test and measuring the vibration response of a neighbouring tooth. The results obtained from the modal analysis testing shows a considerable response peak deviation from the original peak location of the healthy case. The deviation becomes more for bigger crack sizes. This experimental result, using the method developed and explained in this work, validates the analytical method applied previously using FRFs.
{"title":"Gear Tooth Crack Detection Using Modal Analysis","authors":"O. Mohammed, Sadok Sassi","doi":"10.1109/PHM2022-London52454.2022.00029","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00029","url":null,"abstract":"This paper aims to propose an experimental method to detect and quantify the existence of cracks in spur gear teeth using modal analysis. Frequency Response Functions FRFs obtained analytically in previous work are considered to be validated experimentally in the current work. The analytical method using FRFs, which were applied in Ref. [28] for certain crack sizes, is validated here experimentally by using hammer test and measuring the vibration response of a neighbouring tooth. The results obtained from the modal analysis testing shows a considerable response peak deviation from the original peak location of the healthy case. The deviation becomes more for bigger crack sizes. This experimental result, using the method developed and explained in this work, validates the analytical method applied previously using FRFs.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124005389","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}
Pub Date : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00083
Junqi Lu, Su Cao, Xiangbo Meng, Xiangke Wang, Huangchao Yu
The tilt-rotor Unmanned Aerial Vehicles (UAVs) possess not only the ability of long-duration and high-speed cruising of fixed-wing aircraft, but also the advantages of flexible taking off, landing and hovering of rotor aircraft. The tilting system is what makes the tilt-rotor UAV convert between multi-rotor mode and fixed-wing mode. This paper establishes a multi-body dynamics model for the tilting system through Newton-Euler approach, based on which the system is optimized. Numerical examples are given to show the effectiveness of the proposed model and optimization method.
{"title":"Dynamics Modeling and Optimization for the Tilting System of Unmanned Aerial Vehicles","authors":"Junqi Lu, Su Cao, Xiangbo Meng, Xiangke Wang, Huangchao Yu","doi":"10.1109/PHM2022-London52454.2022.00083","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00083","url":null,"abstract":"The tilt-rotor Unmanned Aerial Vehicles (UAVs) possess not only the ability of long-duration and high-speed cruising of fixed-wing aircraft, but also the advantages of flexible taking off, landing and hovering of rotor aircraft. The tilting system is what makes the tilt-rotor UAV convert between multi-rotor mode and fixed-wing mode. This paper establishes a multi-body dynamics model for the tilting system through Newton-Euler approach, based on which the system is optimized. Numerical examples are given to show the effectiveness of the proposed model and optimization method.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124035705","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}
Pub Date : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00039
Jirui Zhu, D. Zhen, X. Liang, Guojin Feng, F. Gu, A. Ball
Planetary gearbox is numerously used in various mechanical transmission systems because of their extensive bearing range and high reliability. However, due to limitations in manufacturing technique and economic considerations, the gear tooth surface roughness inevitable exists in practical manufacturing process. To analyze the influence of the tooth surface roughness on the vibration signals of a planetary gear system, a nonlinear dynamic model considering multi-factor coupling is established. The dynamic model takes into account gear tooth surface roughness, the gear backlash, time-varying meshing stiffness (TVMS) and vibration transfer path. Via this nonlinear model, the effects of different tooth surface roughness on the system dynamics response are analyzed. Furthermore, the dynamic responses in the time domain and frequency domain are used to examine the influences in the main dynamic parameters, such as rotational speed and meshing force. The results show that the tooth surface roughness significantly affect the system dynamic characteristics, and with the increase of the roughness, the influence on the response of the system will enlarge. This paper can offer some theoretic guidance for the development, operation and fault identification of planetary gear transmission system.
{"title":"Dynamic Model of Planetary Gear with Consideration of Tooth Surface Roughness","authors":"Jirui Zhu, D. Zhen, X. Liang, Guojin Feng, F. Gu, A. Ball","doi":"10.1109/PHM2022-London52454.2022.00039","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00039","url":null,"abstract":"Planetary gearbox is numerously used in various mechanical transmission systems because of their extensive bearing range and high reliability. However, due to limitations in manufacturing technique and economic considerations, the gear tooth surface roughness inevitable exists in practical manufacturing process. To analyze the influence of the tooth surface roughness on the vibration signals of a planetary gear system, a nonlinear dynamic model considering multi-factor coupling is established. The dynamic model takes into account gear tooth surface roughness, the gear backlash, time-varying meshing stiffness (TVMS) and vibration transfer path. Via this nonlinear model, the effects of different tooth surface roughness on the system dynamics response are analyzed. Furthermore, the dynamic responses in the time domain and frequency domain are used to examine the influences in the main dynamic parameters, such as rotational speed and meshing force. The results show that the tooth surface roughness significantly affect the system dynamic characteristics, and with the increase of the roughness, the influence on the response of the system will enlarge. This paper can offer some theoretic guidance for the development, operation and fault identification of planetary gear transmission system.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127873261","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}
Pub Date : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00087
Lifeng Zheng, Yangbin Yu, Haifeng Hu, Dihu Chen
Most of the purely unsupervised person Re-Identification (Re-ID) methods use memory dictionary to calculate loss, and use clustering to create memory dictionary and generate pseudo labels. But most of these methods neglect the camera style of the training images, which will severely affect the results of clustering. This paper aims at this challenge, proposes the Joint Memory mechanism with Distance Recalculation. We make full use of and group the feature vectors according to the camera IDs of the training images, and use the clustering algorithm to generate pseudo labels and create memory dictionary inside the same camera. Further, we take advantage of this memory dictionary to recalculate the distances between the training images across all cameras and second time use clustering algorithm to generate pseudo labels and create a new memory dictionary. By jointly utilizing these two kinds of memory dictionary, we can train the network more robustly. Our method accomplishes excellent performance compared to most of state-of-the-art unsupervised Re-ID methods on many datasets, e.g., 91.7%, 84.2%, 59.1% rank-1 accuracy and 80.9%, 71.1%, 32.4% mAP on the Market, Duke and MSMT17 datasets. We achieve this performance when we train the network with a very small batch size, and it is very possible that we can reach a better, maybe surpass state-of-the-art, performance when using a bigger batch size.
{"title":"Joint Memory with Distance Recalculation for Unsupervised Person Re-Identification","authors":"Lifeng Zheng, Yangbin Yu, Haifeng Hu, Dihu Chen","doi":"10.1109/PHM2022-London52454.2022.00087","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00087","url":null,"abstract":"Most of the purely unsupervised person Re-Identification (Re-ID) methods use memory dictionary to calculate loss, and use clustering to create memory dictionary and generate pseudo labels. But most of these methods neglect the camera style of the training images, which will severely affect the results of clustering. This paper aims at this challenge, proposes the Joint Memory mechanism with Distance Recalculation. We make full use of and group the feature vectors according to the camera IDs of the training images, and use the clustering algorithm to generate pseudo labels and create memory dictionary inside the same camera. Further, we take advantage of this memory dictionary to recalculate the distances between the training images across all cameras and second time use clustering algorithm to generate pseudo labels and create a new memory dictionary. By jointly utilizing these two kinds of memory dictionary, we can train the network more robustly. Our method accomplishes excellent performance compared to most of state-of-the-art unsupervised Re-ID methods on many datasets, e.g., 91.7%, 84.2%, 59.1% rank-1 accuracy and 80.9%, 71.1%, 32.4% mAP on the Market, Duke and MSMT17 datasets. We achieve this performance when we train the network with a very small batch size, and it is very possible that we can reach a better, maybe surpass state-of-the-art, performance when using a bigger batch size.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131319909","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}
Pub Date : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00071
Andong Xie, Zhi Yu, Xiaochun Cao, Yangyang Wang, Shoujing Yan
The images in the pavement distress dataset contain complex backgrounds, which makes manual identification more time consuming. In addition, manual identification requires expert experience and knowledge, which is inefficient and expensive. However, the general distress detection framework based on deep learning loses too much surface feature information, which is essential for crack detection. Therefore, we design an attention module that fuses spatial information and channel information and a feature fusion module that is good at integrating surface feature information. Experiments show that our simple method achieves good performance on the pavement distress dataset.
{"title":"Efficient pavement Distress Detection Based on Attention Fusion and Feature Integration","authors":"Andong Xie, Zhi Yu, Xiaochun Cao, Yangyang Wang, Shoujing Yan","doi":"10.1109/PHM2022-London52454.2022.00071","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00071","url":null,"abstract":"The images in the pavement distress dataset contain complex backgrounds, which makes manual identification more time consuming. In addition, manual identification requires expert experience and knowledge, which is inefficient and expensive. However, the general distress detection framework based on deep learning loses too much surface feature information, which is essential for crack detection. Therefore, we design an attention module that fuses spatial information and channel information and a feature fusion module that is good at integrating surface feature information. Experiments show that our simple method achieves good performance on the pavement distress dataset.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126409425","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}
By collecting and studying the time-domain characteristics of the vibration signal on the surface of shunt reactor, it is found that the vibration signal in each period fluctuates violently when the reactor has mechanical failure. The moving average sequence information entropy of vibration signal is extracted as the feature vector, and a One-Class Support Vector Machine (OCSVM) mechanical fault diagnosis model is constructed to realize the health state evaluation of shunt reactor with 99.2% accuracy. Furthermore, a fast fault detection method is proposed. This method only uses four random sampling points, which reduces the difficulty of field operation on the premise of ensuring the average fault diagnosis rate of 98.5%. Therefore, the information entropy feature of moving average sequence is an important feature of fault diagnosis of reactor mechanical equipment, which has strong practical engineering significance for reactor health diagnosis.
{"title":"Fault Diagnosis of Reactor Based on Vibration Signal Information Entropy","authors":"Jing Zhang, Yi Jiang, Qinqing Huang, Haidan Lin, Tiancheng Zhao, Yongka Qi","doi":"10.1109/PHM2022-London52454.2022.00090","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00090","url":null,"abstract":"By collecting and studying the time-domain characteristics of the vibration signal on the surface of shunt reactor, it is found that the vibration signal in each period fluctuates violently when the reactor has mechanical failure. The moving average sequence information entropy of vibration signal is extracted as the feature vector, and a One-Class Support Vector Machine (OCSVM) mechanical fault diagnosis model is constructed to realize the health state evaluation of shunt reactor with 99.2% accuracy. Furthermore, a fast fault detection method is proposed. This method only uses four random sampling points, which reduces the difficulty of field operation on the premise of ensuring the average fault diagnosis rate of 98.5%. Therefore, the information entropy feature of moving average sequence is an important feature of fault diagnosis of reactor mechanical equipment, which has strong practical engineering significance for reactor health diagnosis.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126549750","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}
Pub Date : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00095
Jiachen Kuang, Guanghua Xu, Sicong Zhang, T. Tao, Fan Wei, Yunhui Yu
Recently, the deep transfer learning-based methods have been widely applied in intelligent fault diagnosis of modern manufacturing equipment in real-industrial scenarios, which are capable of identifying the health conditions of unlabeled target samples under various working conditions. In transfer learning- based intelligent fault diagnosis, the source diagnostic knowledge, which is usually extracted by supervised learning approaches, is transferred and reused in related target fault identification tasks. However, the tremendous success of these transfer learning methods is mainly achieved in the field of close-set cross-domain fault diagnosis. But in practical applications, a partial cross-domain scenario is more common and difficult, where the health conditions of the target domain are less than that of the source domain. To address this issue, a deep partial adversarial transfer learning network (PATLN) based on convolutional neural networks and adversarial training is proposed. Experiments on a public rolling element bearing dataset verify the effectiveness of the PATLN method.
{"title":"A deep partial adversarial transfer learning network for cross-domain fault diagnosis of machinery","authors":"Jiachen Kuang, Guanghua Xu, Sicong Zhang, T. Tao, Fan Wei, Yunhui Yu","doi":"10.1109/PHM2022-London52454.2022.00095","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00095","url":null,"abstract":"Recently, the deep transfer learning-based methods have been widely applied in intelligent fault diagnosis of modern manufacturing equipment in real-industrial scenarios, which are capable of identifying the health conditions of unlabeled target samples under various working conditions. In transfer learning- based intelligent fault diagnosis, the source diagnostic knowledge, which is usually extracted by supervised learning approaches, is transferred and reused in related target fault identification tasks. However, the tremendous success of these transfer learning methods is mainly achieved in the field of close-set cross-domain fault diagnosis. But in practical applications, a partial cross-domain scenario is more common and difficult, where the health conditions of the target domain are less than that of the source domain. To address this issue, a deep partial adversarial transfer learning network (PATLN) based on convolutional neural networks and adversarial training is proposed. Experiments on a public rolling element bearing dataset verify the effectiveness of the PATLN method.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122339455","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}
Pub Date : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00025
Jiayu Chen, Cuiying Lin, Jingjing Cui, Hongjuan Ge
Data imbalance, usually occurring in practical industrial engineering, restricts the effective application of intelligent fault diagnosis. To solve the data imbalance between faulty and healthy samples, an enhancement fault diagnosis method is proposed based on Deep Residual Network and Auxiliary Classifier Generative Adversarial Network (DRN-ACGAN). To improve the data enhancement effect, the ACGAN is optimized in two ways. Firstly, the generator uses DRN to prevent the gradient disappearing and over fitting problems caused by the deepening of network layers, improve the learning effect of useful features, and generate better quality samples. Secondly, Instance Normalization (IN) is incorporated into each layer of the generator network to avoid deviation of data. The validation experiments, as well as comparisons with the existing methods, are carried out for the bearing fault diagnosis under practical fault conditions. The results reveal that the proposed method can effectively improve the diagnostic performance for the imbalanced data.
{"title":"An Fault Diagnostic Method Based on DRN-ACGAN for Data Imbalance","authors":"Jiayu Chen, Cuiying Lin, Jingjing Cui, Hongjuan Ge","doi":"10.1109/PHM2022-London52454.2022.00025","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00025","url":null,"abstract":"Data imbalance, usually occurring in practical industrial engineering, restricts the effective application of intelligent fault diagnosis. To solve the data imbalance between faulty and healthy samples, an enhancement fault diagnosis method is proposed based on Deep Residual Network and Auxiliary Classifier Generative Adversarial Network (DRN-ACGAN). To improve the data enhancement effect, the ACGAN is optimized in two ways. Firstly, the generator uses DRN to prevent the gradient disappearing and over fitting problems caused by the deepening of network layers, improve the learning effect of useful features, and generate better quality samples. Secondly, Instance Normalization (IN) is incorporated into each layer of the generator network to avoid deviation of data. The validation experiments, as well as comparisons with the existing methods, are carried out for the bearing fault diagnosis under practical fault conditions. The results reveal that the proposed method can effectively improve the diagnostic performance for the imbalanced data.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115259349","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}
Pub Date : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00030
Hao Zhang, D. Zhen, Fang Zeng, Guojin Feng, Zhaozong Meng, F. Gu
The Multiple Signal Classification (MUSIC) algorithm has become a landmark algorithm in the theoretical system of spatial spectrum estimation. This technology has excellent estimation performance and wide application prospects. Accurate Direction of Arrival (DOA) estimation plays a pivotal role in the detection of narrow wave sources. Nevertheless, when the signals are partially correlated or even coherent, the performance of the traditional MUSIC algorithm is greatly reduced. Methods such as spatial smoothing and Toeplitz matrix reconstruction have been proposed to decoherence and minimize the DOA estimation error in the MUSIC algorithm. However, these methods can only be applied to uniform linear arrays, which greatly reduces the practicability of the algorithm. This paper proposes to combine a decoherence method with MUSIC algorithm to estimate the azimuth angle (θ) and elevation angle (φ) of the source in a planar array which is composed of two orthogonal minimum redundant linear arrays (MRLA). The algorithm is implemented under different Signal-to-Noise Ratio (SNR) and compared with other decoherence methods. Simulation results show the proposed decoherence algorithm can achieve higher DOA estimation accuracy for coherent sources.
{"title":"Matrix Reconstruction to Estimate Direction of Arrival of Coherent Sources Based on Planar Array","authors":"Hao Zhang, D. Zhen, Fang Zeng, Guojin Feng, Zhaozong Meng, F. Gu","doi":"10.1109/PHM2022-London52454.2022.00030","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00030","url":null,"abstract":"The Multiple Signal Classification (MUSIC) algorithm has become a landmark algorithm in the theoretical system of spatial spectrum estimation. This technology has excellent estimation performance and wide application prospects. Accurate Direction of Arrival (DOA) estimation plays a pivotal role in the detection of narrow wave sources. Nevertheless, when the signals are partially correlated or even coherent, the performance of the traditional MUSIC algorithm is greatly reduced. Methods such as spatial smoothing and Toeplitz matrix reconstruction have been proposed to decoherence and minimize the DOA estimation error in the MUSIC algorithm. However, these methods can only be applied to uniform linear arrays, which greatly reduces the practicability of the algorithm. This paper proposes to combine a decoherence method with MUSIC algorithm to estimate the azimuth angle (θ) and elevation angle (φ) of the source in a planar array which is composed of two orthogonal minimum redundant linear arrays (MRLA). The algorithm is implemented under different Signal-to-Noise Ratio (SNR) and compared with other decoherence methods. Simulation results show the proposed decoherence algorithm can achieve higher DOA estimation accuracy for coherent sources.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134575623","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}
Pub Date : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00070
Huiyun Hou, Jian-Kun Lu, Chen Yang
In this paper, based on the traditional single closed-loop voltage-type control method, a current sensorless current sharing control method is proposed to solve the current sharing problem in two-phase staggered Boost converters. Based on the characteristics and structure of the circuit itself, the equivalent dc circuit model of the converter is established. It is found that the main reason of phase current imbalance is the mismatch of effective duty cycle and phase parasitic resistance. In order to eliminate the current imbalance caused by them, the relationship between effective duty cycle mismatch compensation, parasitic resistance mismatch of each phase and duty cycle compensation is estimated, and the duty cycle of the second corresponding compensation is obtained based on the first phase. Compared with the traditional current-sharing control strategy, the proposed current-sharing control strategy does not need to collect phase current to judge and process the detected current, which reduces the complexity of the control loop in the controller, and also reduces the influence caused by low precision and high design cost of the current sensor. Finally, the simulation results on Matlab platform show that the equalizing effect of phase current has been significantly improved under various mismatches and different loads, which verifies the effectiveness of the equalizing current control.
{"title":"Current Sharing Control of Two - phase Interleaved Parallel Boost Converter Based on Sensorless Current","authors":"Huiyun Hou, Jian-Kun Lu, Chen Yang","doi":"10.1109/PHM2022-London52454.2022.00070","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00070","url":null,"abstract":"In this paper, based on the traditional single closed-loop voltage-type control method, a current sensorless current sharing control method is proposed to solve the current sharing problem in two-phase staggered Boost converters. Based on the characteristics and structure of the circuit itself, the equivalent dc circuit model of the converter is established. It is found that the main reason of phase current imbalance is the mismatch of effective duty cycle and phase parasitic resistance. In order to eliminate the current imbalance caused by them, the relationship between effective duty cycle mismatch compensation, parasitic resistance mismatch of each phase and duty cycle compensation is estimated, and the duty cycle of the second corresponding compensation is obtained based on the first phase. Compared with the traditional current-sharing control strategy, the proposed current-sharing control strategy does not need to collect phase current to judge and process the detected current, which reduces the complexity of the control loop in the controller, and also reduces the influence caused by low precision and high design cost of the current sensor. Finally, the simulation results on Matlab platform show that the equalizing effect of phase current has been significantly improved under various mismatches and different loads, which verifies the effectiveness of the equalizing current control.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115048763","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}
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