Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942888
Bingxiu Guo, Xiaohui Wang, Yanyan Wang, Haoyun Su, Sijian Chao
Rubber is widely used in aviation, aerospace and other important fields. Monitoring properties of rubber and predicting its remaining life is the key to ensuring timely repair and replacement, and it is related to the safety and reliability of equipment. The traditional methods of life calculation is limited by the study of environment and mechanism. The data-driven is more concise and efficient and it can characterize the coupling effect of many factors for the life of rubber. Support Vector Machine (SVM) is a data-driven method for solving small sample and nonlinear problems with good robustness. In this paper the support vector regression(SVR) algorithm was applied to the prediction of rubber life. We used a certain type Polymerized Styrene Butadiene Rubber cable insulation as an example, the temperature and the concentration of oil mist were set as the features to predict the remaining life. The model was trained by accelerated aging test data, and its remaining life was calculated according to its break elongation retention rate at the end of life. Compared with the actual test results and the pridicted results of linear regression model, the applicability of the method was discussed.
{"title":"Application of Support Vector Regression to predict the Remaining useful life of Polymerized Styrene Butadiene Rubber of cable insulation","authors":"Bingxiu Guo, Xiaohui Wang, Yanyan Wang, Haoyun Su, Sijian Chao","doi":"10.1109/phm-qingdao46334.2019.8942888","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942888","url":null,"abstract":"Rubber is widely used in aviation, aerospace and other important fields. Monitoring properties of rubber and predicting its remaining life is the key to ensuring timely repair and replacement, and it is related to the safety and reliability of equipment. The traditional methods of life calculation is limited by the study of environment and mechanism. The data-driven is more concise and efficient and it can characterize the coupling effect of many factors for the life of rubber. Support Vector Machine (SVM) is a data-driven method for solving small sample and nonlinear problems with good robustness. In this paper the support vector regression(SVR) algorithm was applied to the prediction of rubber life. We used a certain type Polymerized Styrene Butadiene Rubber cable insulation as an example, the temperature and the concentration of oil mist were set as the features to predict the remaining life. The model was trained by accelerated aging test data, and its remaining life was calculated according to its break elongation retention rate at the end of life. Compared with the actual test results and the pridicted results of linear regression model, the applicability of the method was discussed.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131192542","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942976
Liang Tang, Shunong Zhang, Xuesong Yang, Shuli Hu
Prognostics and health management (PHM) technology has been successfully applied in many complex equipment. However, with the equipment becoming more and more complex, the working conditions changing with time, and the equipment status information increasing, it is difficult by traditional technologies to cope with the new situation and new application scenarios. The application of deep learning method in many fields proves the ability of this method to deal with massive and complex data. In this paper, the special recurrent neural networks (RNN) called long-short term memory (LSTM) network are used to estimate the remaining life of engines with the data of PHM08 Challenge Competition. First, standardize the original data and add life labels in the data preprocessing stage. Then the influences of different data input methods on the prediction results are studied, and the results show that proper method is to input all the time series information at one time. The over-fitting phenomenon can be reduced to some extent by reducing the complexity of the neural network. Thus, a remaining life prediction method based on multi-dimensional data is obtained. The final result was uploaded to the competition’s scoring system and got good results, which confirmed the accuracy of this method. Therefore, the article summarizes a highly accurate LSTM-based multidimensional data failure prediction method.
{"title":"Research on Prognosis for Engines by LSTM Deep Learning Method","authors":"Liang Tang, Shunong Zhang, Xuesong Yang, Shuli Hu","doi":"10.1109/phm-qingdao46334.2019.8942976","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942976","url":null,"abstract":"Prognostics and health management (PHM) technology has been successfully applied in many complex equipment. However, with the equipment becoming more and more complex, the working conditions changing with time, and the equipment status information increasing, it is difficult by traditional technologies to cope with the new situation and new application scenarios. The application of deep learning method in many fields proves the ability of this method to deal with massive and complex data. In this paper, the special recurrent neural networks (RNN) called long-short term memory (LSTM) network are used to estimate the remaining life of engines with the data of PHM08 Challenge Competition. First, standardize the original data and add life labels in the data preprocessing stage. Then the influences of different data input methods on the prediction results are studied, and the results show that proper method is to input all the time series information at one time. The over-fitting phenomenon can be reduced to some extent by reducing the complexity of the neural network. Thus, a remaining life prediction method based on multi-dimensional data is obtained. The final result was uploaded to the competition’s scoring system and got good results, which confirmed the accuracy of this method. Therefore, the article summarizes a highly accurate LSTM-based multidimensional data failure prediction method.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115161143","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942894
Kun He, Lianghua Zeng, Qin Shui, Jianyu Long, Chuan Li, Diego Cabrera
With the 3D printing rapidly expanding into various fields, 3D printers, as the equipment, should adopt a low-cost and small-sample fault diagnosis methods. A fault diagnosis method based on echo state networks (ESN) for 3D printers is proposed in this paper. A low-cost attitude sensor installed on the 3D printer is employed to collect raw fault data. Subsequently, feature extraction is carried out on the raw fault data. Using these features, ESN, as a shallow learning network, is modeled to diagnose faults of 3D printers. Experimental results show that the fault diagnosis method based on ESN still effective for 3D printers in low-cost and small-sample, which can make the fault recognition accuracy of 3D printer reach to 97.26%. Furthermore, contrast results indicated that the fault diagnosis accuracy of ESN is higher and most stable when compare with support vector machine (SVM), locality preserving projection support vector machine (LPPSVM) and principal component analysis support vector machine (PCASVM).
{"title":"Low-cost and Small-sample Fault Diagnosis for 3D Printers Based on Echo State Networks","authors":"Kun He, Lianghua Zeng, Qin Shui, Jianyu Long, Chuan Li, Diego Cabrera","doi":"10.1109/phm-qingdao46334.2019.8942894","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942894","url":null,"abstract":"With the 3D printing rapidly expanding into various fields, 3D printers, as the equipment, should adopt a low-cost and small-sample fault diagnosis methods. A fault diagnosis method based on echo state networks (ESN) for 3D printers is proposed in this paper. A low-cost attitude sensor installed on the 3D printer is employed to collect raw fault data. Subsequently, feature extraction is carried out on the raw fault data. Using these features, ESN, as a shallow learning network, is modeled to diagnose faults of 3D printers. Experimental results show that the fault diagnosis method based on ESN still effective for 3D printers in low-cost and small-sample, which can make the fault recognition accuracy of 3D printer reach to 97.26%. Furthermore, contrast results indicated that the fault diagnosis accuracy of ESN is higher and most stable when compare with support vector machine (SVM), locality preserving projection support vector machine (LPPSVM) and principal component analysis support vector machine (PCASVM).","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133625225","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8943037
Da-peng Ren, Yuwei An, Z. Li
In order to establish a quantitative model to measure mental fatigue of the human body, subjects electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR) are collected through the designed experimental method. Through optimized parameters setting, subjects states of mental fatigue are comprehensively analyzed and evaluated. Moreover, the entropy weight method is used for analyzing and verifying the above three kinds of data including EEG, ECG and GSR as well as comparing the data acquired from the subjects with various mental fatigue states. Thus, a parametric model is constructed to determine the degree of mental fatigue.
{"title":"The Discriminative Model of Mental Fatigue Based on Comprehensive Parameter Analysis","authors":"Da-peng Ren, Yuwei An, Z. Li","doi":"10.1109/phm-qingdao46334.2019.8943037","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943037","url":null,"abstract":"In order to establish a quantitative model to measure mental fatigue of the human body, subjects electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR) are collected through the designed experimental method. Through optimized parameters setting, subjects states of mental fatigue are comprehensively analyzed and evaluated. Moreover, the entropy weight method is used for analyzing and verifying the above three kinds of data including EEG, ECG and GSR as well as comparing the data acquired from the subjects with various mental fatigue states. Thus, a parametric model is constructed to determine the degree of mental fatigue.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131897119","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8943062
Xiaokai Huang, Zemin Yao, Shouqing Huang, Dazhi Liu
Axial piston pumps are key components in hydraulic systems, and their real-time performance degradation analysis has received more and more attention in engineering practice. This paper proposes a degradation trajectory method based on self-organizing map (SOM), which is used to analyze the performance degradation of axial piston pumps. Firstly, a selfadaptive Morlet wavelet filter is applied to process the vibration signals of axial piston pumps, and time-domain metrics of filtered signal is used as eigenvectors which can reflect the performance degradation degree. Then data from typical status are used to train SOM, and trajectory on the output layer of SOM is used to represent the real-time performance of degradation degree. Lastly, the performance degradation experiment of axial piston pumps was carried out and the results showed that the proposed method can describe performance degradation process of axial piston pumps effectively.
{"title":"Performance Degradation Analysis of Axial Piston Pumps Based on Self-Organizing Map","authors":"Xiaokai Huang, Zemin Yao, Shouqing Huang, Dazhi Liu","doi":"10.1109/phm-qingdao46334.2019.8943062","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943062","url":null,"abstract":"Axial piston pumps are key components in hydraulic systems, and their real-time performance degradation analysis has received more and more attention in engineering practice. This paper proposes a degradation trajectory method based on self-organizing map (SOM), which is used to analyze the performance degradation of axial piston pumps. Firstly, a selfadaptive Morlet wavelet filter is applied to process the vibration signals of axial piston pumps, and time-domain metrics of filtered signal is used as eigenvectors which can reflect the performance degradation degree. Then data from typical status are used to train SOM, and trajectory on the output layer of SOM is used to represent the real-time performance of degradation degree. Lastly, the performance degradation experiment of axial piston pumps was carried out and the results showed that the proposed method can describe performance degradation process of axial piston pumps effectively.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134478865","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942861
Y. Yu, Yunqiang Wu, Lin Yue
The blade tip-timing has become the most promising technique in the field of rotating blade vibration monitoring with its advantages of non-contacting. However the signal can be disturbed by many factors, especially the noise and drift of the blade vibration displacement curve caused by the centrifugal force changed with rotating speed. The main difficulty to data zeroing is to prevent the peak amplitude from being attenuated or eliminated. In this paper, a method was developed using blade vibration displacement to identify the areas of resonance by calculating the correlation of the data over a number of assembly revolutions from the multi-probe. The blade vibration simulator is carried out to study the relationship between the number of probes and the window width in the correlation. Applying this method into the experimental data, and verify the superiority of the correlation method.
{"title":"Data Zeroing Based on Correlation and Linear Interpolation of the Blade Tip-Timing Data","authors":"Y. Yu, Yunqiang Wu, Lin Yue","doi":"10.1109/phm-qingdao46334.2019.8942861","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942861","url":null,"abstract":"The blade tip-timing has become the most promising technique in the field of rotating blade vibration monitoring with its advantages of non-contacting. However the signal can be disturbed by many factors, especially the noise and drift of the blade vibration displacement curve caused by the centrifugal force changed with rotating speed. The main difficulty to data zeroing is to prevent the peak amplitude from being attenuated or eliminated. In this paper, a method was developed using blade vibration displacement to identify the areas of resonance by calculating the correlation of the data over a number of assembly revolutions from the multi-probe. The blade vibration simulator is carried out to study the relationship between the number of probes and the window width in the correlation. Applying this method into the experimental data, and verify the superiority of the correlation method.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"24 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125672459","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942949
Xueyi Li, Jialin Li, Chengying Zhao, Yongzhi Qu, D. He
The early gear pitting fault diagnosis has received much attention in the industry. In recent decades, with the popularity growth of artificial neural network, researchers have applied deep learning methods to figure out early gear pitting faults. However, the classical fault diagnosis methods usually use deep neural networks according to the time sequence of the collected signals. In this case, the feature extraction in the direction of the inverse time-domain signals is usually ignored. Aimed at overcoming this shortage, ground on a traditional Long Short Term Memory (LSTM) network, this paper proposes a Bidirectional LSTM (Bi-LSTM) to construct a fault diagnosis model of early gear pitting using raw vibration signals. Using the Bi-LSTM network, feature extraction of the vibrational signals in both directions is simultaneously carried out to evaluate the degree of the early gear pitting faults to better extract the gear pitting characteristics from the raw vibration signals of the gear. Through the analysis of the experimental data, compared with the traditional LSTM model, the Bi-directional LSTM has a classification accuracy of over 96% for early gear pitting fault diagnosis, which is an increase of 4.1%.
{"title":"Early Gear Pitting Fault Diagnosis Based on Bi-directional LSTM","authors":"Xueyi Li, Jialin Li, Chengying Zhao, Yongzhi Qu, D. He","doi":"10.1109/phm-qingdao46334.2019.8942949","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942949","url":null,"abstract":"The early gear pitting fault diagnosis has received much attention in the industry. In recent decades, with the popularity growth of artificial neural network, researchers have applied deep learning methods to figure out early gear pitting faults. However, the classical fault diagnosis methods usually use deep neural networks according to the time sequence of the collected signals. In this case, the feature extraction in the direction of the inverse time-domain signals is usually ignored. Aimed at overcoming this shortage, ground on a traditional Long Short Term Memory (LSTM) network, this paper proposes a Bidirectional LSTM (Bi-LSTM) to construct a fault diagnosis model of early gear pitting using raw vibration signals. Using the Bi-LSTM network, feature extraction of the vibrational signals in both directions is simultaneously carried out to evaluate the degree of the early gear pitting faults to better extract the gear pitting characteristics from the raw vibration signals of the gear. Through the analysis of the experimental data, compared with the traditional LSTM model, the Bi-directional LSTM has a classification accuracy of over 96% for early gear pitting fault diagnosis, which is an increase of 4.1%.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127758380","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8943002
Xiaohang Jin, Z. Que, Yi Sun
Bearing failure can cause their host system shutdown, and even some catastrophic accidents. These will lead to a high maintenance cost and a huge economic loss. Thus, health monitoring and fault prognosis for bearings becomes increasingly important. Developing an effective health index (HI) will do help in these works. Hence, three different HIs are developed by using root mean square, Kolmogorov-Smirnov test, and Mahalanobis distance to reflect bearings’ online health conditions. Four degradation models are constructed to estimate bearings remaining useful life (RUL) by using particle filter algorithm. Bearing life data are used to test the performance of fault prognostic approaches. Results show that all HIs reflect the degradation process of bearing effectively, and the proposed degradation model has the best performance in bearing RUL prediction.
{"title":"Development of Vibration-Based Health Indexes for Bearing Remaining Useful Life Prediction","authors":"Xiaohang Jin, Z. Que, Yi Sun","doi":"10.1109/phm-qingdao46334.2019.8943002","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943002","url":null,"abstract":"Bearing failure can cause their host system shutdown, and even some catastrophic accidents. These will lead to a high maintenance cost and a huge economic loss. Thus, health monitoring and fault prognosis for bearings becomes increasingly important. Developing an effective health index (HI) will do help in these works. Hence, three different HIs are developed by using root mean square, Kolmogorov-Smirnov test, and Mahalanobis distance to reflect bearings’ online health conditions. Four degradation models are constructed to estimate bearings remaining useful life (RUL) by using particle filter algorithm. Bearing life data are used to test the performance of fault prognostic approaches. Results show that all HIs reflect the degradation process of bearing effectively, and the proposed degradation model has the best performance in bearing RUL prediction.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124537915","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8943035
Ting Yu, Guicui Fu, Y. Qiu, Ye Wang
CMOS image sensors are extensively utilized in digital imaging systems for their excellent performance and low power consumption. As an essential components in the system, CMOS image sensors are expected with low noise levels. The images captured by CMOS image sensor contain random noise (RN), digital noise (DN), and fixed pattern noise (FPN). FPN of CMOS image sensors has a greater impact on the perceived image quality than random noise, which seriously restricts the development and application of CMOS image sensors. This paper proposed a noise power spectrum (NPS) method for estimating column FPN of CMOS image sensor based on AR model. First, dozens of images under uniform illumination are acquired by established test vehicle. Second, random noise of the images is restrained by using the multi-frame averaging method. Then, column FPN is modeled by an autoregressive (AR) random process subsequently. Ultimately, column FPN is estimated by calculating NPS of the image based on the AR model. A case application was proposed by using this method.
{"title":"Noise Power Spectrum Estimation of Column Fixed Pattern Noise in CMOS Image Sensors Based on AR Model","authors":"Ting Yu, Guicui Fu, Y. Qiu, Ye Wang","doi":"10.1109/phm-qingdao46334.2019.8943035","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943035","url":null,"abstract":"CMOS image sensors are extensively utilized in digital imaging systems for their excellent performance and low power consumption. As an essential components in the system, CMOS image sensors are expected with low noise levels. The images captured by CMOS image sensor contain random noise (RN), digital noise (DN), and fixed pattern noise (FPN). FPN of CMOS image sensors has a greater impact on the perceived image quality than random noise, which seriously restricts the development and application of CMOS image sensors. This paper proposed a noise power spectrum (NPS) method for estimating column FPN of CMOS image sensor based on AR model. First, dozens of images under uniform illumination are acquired by established test vehicle. Second, random noise of the images is restrained by using the multi-frame averaging method. Then, column FPN is modeled by an autoregressive (AR) random process subsequently. Ultimately, column FPN is estimated by calculating NPS of the image based on the AR model. A case application was proposed by using this method.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125237454","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942940
Li Zhang, Zhiqian Lu, Shufeng Zhang, J. Tao
Because of machining tolerance as well as wear, joint clearances are inevitable in multibody systems. It can seriously degrade the dynamic performance of connected parts and significantly increase the operating noise. Most of previous studies about clearance joints were conducted on planar multibody systems, but actually the relative motion of journal and bearing includes radial and axial component. Therefore, the wear of joint is also three-dimensional. The influence of different spatial clearances on the dynamic response of multibody system is studied with a four-bar mechanism as the research object. In addition, in the ABAQUS/Standard environment, wear behavior of three-dimensional clearance joint is simulated and the spatial shape of contact surface after wear is obtained.
{"title":"Dynamic Behavior of Four-bar Mechanism with Three-dimensional Clearance and Wear","authors":"Li Zhang, Zhiqian Lu, Shufeng Zhang, J. Tao","doi":"10.1109/phm-qingdao46334.2019.8942940","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942940","url":null,"abstract":"Because of machining tolerance as well as wear, joint clearances are inevitable in multibody systems. It can seriously degrade the dynamic performance of connected parts and significantly increase the operating noise. Most of previous studies about clearance joints were conducted on planar multibody systems, but actually the relative motion of journal and bearing includes radial and axial component. Therefore, the wear of joint is also three-dimensional. The influence of different spatial clearances on the dynamic response of multibody system is studied with a four-bar mechanism as the research object. In addition, in the ABAQUS/Standard environment, wear behavior of three-dimensional clearance joint is simulated and the spatial shape of contact surface after wear is obtained.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131740705","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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