Pub Date : 2017-05-01DOI: 10.1109/ICMSC.2017.7959448
Wu Wanrong, Wu Weiwei, Huang Qian, Wang Songlai
The traditional excitation system usually adopts the eletro-hydraulic excitation valve, of which the structure is complicated and requires externally being connected to the servo motor and control system, but cannot meet the requirements of high frequency and high flow. So a cartridge vibration valve is proposed. The valve has a simple structure, which is mainly composed of four two-way cartridge valves, a two-position four-way hydraulic control directional valve and two variable throttle orifices. Its frequency modulation mode is simple, and it can achieve high-frequency commutation and large-flow output. To study the effect of the parameters on the vibration characteristics of the vibration valve, the mathematical model and AMESim simulation model were established. The result shows that: the excitation frequency increases with the increasing of the working pressure, the area ratio and the spring stiffness of the two-way cartridge valve. Adjusting the orifice diameter and the working pressure can regulate the excitation frequency.
{"title":"Simulation study on vibration characteristics of excitation valve","authors":"Wu Wanrong, Wu Weiwei, Huang Qian, Wang Songlai","doi":"10.1109/ICMSC.2017.7959448","DOIUrl":"https://doi.org/10.1109/ICMSC.2017.7959448","url":null,"abstract":"The traditional excitation system usually adopts the eletro-hydraulic excitation valve, of which the structure is complicated and requires externally being connected to the servo motor and control system, but cannot meet the requirements of high frequency and high flow. So a cartridge vibration valve is proposed. The valve has a simple structure, which is mainly composed of four two-way cartridge valves, a two-position four-way hydraulic control directional valve and two variable throttle orifices. Its frequency modulation mode is simple, and it can achieve high-frequency commutation and large-flow output. To study the effect of the parameters on the vibration characteristics of the vibration valve, the mathematical model and AMESim simulation model were established. The result shows that: the excitation frequency increases with the increasing of the working pressure, the area ratio and the spring stiffness of the two-way cartridge valve. Adjusting the orifice diameter and the working pressure can regulate the excitation frequency.","PeriodicalId":356055,"journal":{"name":"2017 International Conference on Mechanical, System and Control Engineering (ICMSC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127853584","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 : 2017-05-01DOI: 10.1109/ICMSC.2017.7959483
Jie Liu, Youmin Hu, Yanglong Lu, Yan Wang, L. Xiao, Kunming Zheng
Data-driven health condition monitoring has received increasing attentions. However, the bandwidth of transmission channels imposes the limit on the amount of sensor data to be used in remote condition monitoring systems in real-time applications. In this paper, a remote health condition monitoring (RHCM) method based on compressed sensing (CS) is proposed for machine state classification and signal reconstruction. Compressed sensor signals can be directly used to identify different machine states based on a pre-constructed dictionary without the need of traditional feature extraction process. Alternatively, the complete signals can also be reconstructed from the compressed signals and traditional classification approaches can be applied. A case study based on rolling bearing is used to show that the proposed RHCM method can effectively recognize and classify the machine states under different operation conditions using low-volume sensor signals, and the reconstructed signals are accurate enough for post-evaluation or quality assessment of on-site machine process.
{"title":"A remote health condition monitoring system based on compressed sensing","authors":"Jie Liu, Youmin Hu, Yanglong Lu, Yan Wang, L. Xiao, Kunming Zheng","doi":"10.1109/ICMSC.2017.7959483","DOIUrl":"https://doi.org/10.1109/ICMSC.2017.7959483","url":null,"abstract":"Data-driven health condition monitoring has received increasing attentions. However, the bandwidth of transmission channels imposes the limit on the amount of sensor data to be used in remote condition monitoring systems in real-time applications. In this paper, a remote health condition monitoring (RHCM) method based on compressed sensing (CS) is proposed for machine state classification and signal reconstruction. Compressed sensor signals can be directly used to identify different machine states based on a pre-constructed dictionary without the need of traditional feature extraction process. Alternatively, the complete signals can also be reconstructed from the compressed signals and traditional classification approaches can be applied. A case study based on rolling bearing is used to show that the proposed RHCM method can effectively recognize and classify the machine states under different operation conditions using low-volume sensor signals, and the reconstructed signals are accurate enough for post-evaluation or quality assessment of on-site machine process.","PeriodicalId":356055,"journal":{"name":"2017 International Conference on Mechanical, System and Control Engineering (ICMSC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133750889","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 : 2017-05-01DOI: 10.1109/icmsc.2017.7959437
M. Tamarkin, E. Tischenko, D. Kazakov
The article introduces the results of process research of the centrifugal and rotary processing of parts of steel and nonferrous alloys in the medium of steel spheres. The complex of the reliability indicators forming models of a technology process which allows providing an increase in the efficiency and the achievement of set size and stability of quality parameters of a workpiece has been received. The techniques of the technology process design have been developed.
{"title":"The research process of the finishing strengthening centrifugal and rotary processing method of parts considering the reliability assurance of the technology process","authors":"M. Tamarkin, E. Tischenko, D. Kazakov","doi":"10.1109/icmsc.2017.7959437","DOIUrl":"https://doi.org/10.1109/icmsc.2017.7959437","url":null,"abstract":"The article introduces the results of process research of the centrifugal and rotary processing of parts of steel and nonferrous alloys in the medium of steel spheres. The complex of the reliability indicators forming models of a technology process which allows providing an increase in the efficiency and the achievement of set size and stability of quality parameters of a workpiece has been received. The techniques of the technology process design have been developed.","PeriodicalId":356055,"journal":{"name":"2017 International Conference on Mechanical, System and Control Engineering (ICMSC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131414950","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 : 2017-05-01DOI: 10.1109/ICMSC.2017.7959474
A. Kostoglotov, Oksana Andreevna Kostoglotova, Igor Deryabkin, Igor Evgenevich Kirillov, S. Lazarenko
The variational methods of parametric identification which use the physical features of the studied systems in the form of the Hamilton — Ostrogradskii variational principle are studied. On this basis the identification algorithms resistant to measurement noise and having high convergence rate of their estimates to the actual values are produced. This is confirmed by comparing the results of mathematical simulation of the developed algorithms with the Kalman filter.
{"title":"Comparison of identification algorithms based on the combining maximum principle and the regularization","authors":"A. Kostoglotov, Oksana Andreevna Kostoglotova, Igor Deryabkin, Igor Evgenevich Kirillov, S. Lazarenko","doi":"10.1109/ICMSC.2017.7959474","DOIUrl":"https://doi.org/10.1109/ICMSC.2017.7959474","url":null,"abstract":"The variational methods of parametric identification which use the physical features of the studied systems in the form of the Hamilton — Ostrogradskii variational principle are studied. On this basis the identification algorithms resistant to measurement noise and having high convergence rate of their estimates to the actual values are produced. This is confirmed by comparing the results of mathematical simulation of the developed algorithms with the Kalman filter.","PeriodicalId":356055,"journal":{"name":"2017 International Conference on Mechanical, System and Control Engineering (ICMSC)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124795147","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 : 2017-05-01DOI: 10.1109/ICMSC.2017.7959439
Jing Gan
The complex mechanical structure and working characteristics of crane determine it is a kind of construction machinery with larger risk factors. In order to ensure the safety and reliability of the crane during operation process, also avoid serious failure which affects the efficiency and progress of the engineering project, this paper uses discrete Hopfield neural network approach to evaluate and monitor the running state of the crane. The experimental results show that the discrete Hopfield neural network mode can accurately evaluate the running state of the crane, and thus provides an effective technical way to improve its security and reliability.
{"title":"Discrete Hopfield neural network approach for crane safety evaluation","authors":"Jing Gan","doi":"10.1109/ICMSC.2017.7959439","DOIUrl":"https://doi.org/10.1109/ICMSC.2017.7959439","url":null,"abstract":"The complex mechanical structure and working characteristics of crane determine it is a kind of construction machinery with larger risk factors. In order to ensure the safety and reliability of the crane during operation process, also avoid serious failure which affects the efficiency and progress of the engineering project, this paper uses discrete Hopfield neural network approach to evaluate and monitor the running state of the crane. The experimental results show that the discrete Hopfield neural network mode can accurately evaluate the running state of the crane, and thus provides an effective technical way to improve its security and reliability.","PeriodicalId":356055,"journal":{"name":"2017 International Conference on Mechanical, System and Control Engineering (ICMSC)","volume":"1632 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132155085","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 : 2017-05-01DOI: 10.1109/ICMSC.2017.7959501
Xuanze Wang, Yi Luo, Zhongsheng Zhai, Liangen Yang, Tao He
In the process of curve fitting, the unknown relationship between the data sampling rate and the frequency of the measured signal as well as signal's frequency fluctuations can cause remarkable error. In this paper, a sine curve fitting algorithm with frequency precise estimation is proposed to solve this problem. Firstly, ellipse fitting algorithm is used to estimate the measured signal frequency. On the basis of this, the three-parameter sine fitting calculation based on the least squares method is applied to the sampling data, and the accuracy of the sine curve fitting result is improved. The experimental results show that the algorithm proposed in this paper can avoid the problem of frequency fluctuation and error caused by the unknown relationship between the sampling frequencies. Moreover, the fitting has a good randomness after frequency estimation, and the fitting result has nothing to do with the signal amplitude. Compared without frequency accurate estimation, the fitting error is greatly reduced and the fitting precision is higher.
{"title":"Algorithm for sine wave cure fit based on frequency precise estimation","authors":"Xuanze Wang, Yi Luo, Zhongsheng Zhai, Liangen Yang, Tao He","doi":"10.1109/ICMSC.2017.7959501","DOIUrl":"https://doi.org/10.1109/ICMSC.2017.7959501","url":null,"abstract":"In the process of curve fitting, the unknown relationship between the data sampling rate and the frequency of the measured signal as well as signal's frequency fluctuations can cause remarkable error. In this paper, a sine curve fitting algorithm with frequency precise estimation is proposed to solve this problem. Firstly, ellipse fitting algorithm is used to estimate the measured signal frequency. On the basis of this, the three-parameter sine fitting calculation based on the least squares method is applied to the sampling data, and the accuracy of the sine curve fitting result is improved. The experimental results show that the algorithm proposed in this paper can avoid the problem of frequency fluctuation and error caused by the unknown relationship between the sampling frequencies. Moreover, the fitting has a good randomness after frequency estimation, and the fitting result has nothing to do with the signal amplitude. Compared without frequency accurate estimation, the fitting error is greatly reduced and the fitting precision is higher.","PeriodicalId":356055,"journal":{"name":"2017 International Conference on Mechanical, System and Control Engineering (ICMSC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121534790","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 : 2017-05-01DOI: 10.1109/ICMSC.2017.7959484
S. Lazarenko, I. Pugachev, A. Kostoglotov, Igor Deryabkin
The problem of current determining the parameters of the adaptive dynamic systems by minimizing the residuals of observations. The developed identification method allows to obtain the new structures of feedbacks with respect to the state variables and the parameters by constructing invariants. They allow to formalize a priori uncertainty in the parameters dynamics through the use of additional properties that are exposed at the true trajectory in accordance with the variation method, which follows from the Hamilton — Ostrogradskii principle which is the base for the dynamics of the adaptive system and the parameters. The variations properties allow to decompose the identification system into the subsystems of the parameter and status estimation. The relationship between them is determined by the equation for the sensitivity function. The constructiveness of the synthesized algorithms of identification is confirmed by the results of solution the problem of identifying the parameters of the nonlinear dynamic system regulator.
{"title":"The synthesis of algorithms for parameters estimation of adaptive systems","authors":"S. Lazarenko, I. Pugachev, A. Kostoglotov, Igor Deryabkin","doi":"10.1109/ICMSC.2017.7959484","DOIUrl":"https://doi.org/10.1109/ICMSC.2017.7959484","url":null,"abstract":"The problem of current determining the parameters of the adaptive dynamic systems by minimizing the residuals of observations. The developed identification method allows to obtain the new structures of feedbacks with respect to the state variables and the parameters by constructing invariants. They allow to formalize a priori uncertainty in the parameters dynamics through the use of additional properties that are exposed at the true trajectory in accordance with the variation method, which follows from the Hamilton — Ostrogradskii principle which is the base for the dynamics of the adaptive system and the parameters. The variations properties allow to decompose the identification system into the subsystems of the parameter and status estimation. The relationship between them is determined by the equation for the sensitivity function. The constructiveness of the synthesized algorithms of identification is confirmed by the results of solution the problem of identifying the parameters of the nonlinear dynamic system regulator.","PeriodicalId":356055,"journal":{"name":"2017 International Conference on Mechanical, System and Control Engineering (ICMSC)","volume":"60 48","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114005828","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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