The Experts below are selected from a list of 198561 Experts worldwide ranked by ideXlab platform
Jin Chen - One of the best experts on this subject based on the ideXlab platform.
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An intelligent Performance Degradation assessment method for bearings
Journal of Vibration and Control, 2016Co-Authors: Huiming Jiang, Jin Chen, Guangming Dong, Ran WangAbstract:Bearings are one of the most frequently used components in the rotatory machinery, so the Performance Degradation assessment of bearings plays an important role in the prognostics and health management of systems. Hidden Markov model (HMM) is a widely applied data-driven model used for bearing Performance Degradation assessment and has many successful applications. A normal HMM needs to be trained in advance, which has close relationship with the evaluation system. However, the trained HMM is quite influenced by many issues, such as the data integrity and the feature space. In this paper, an intelligent bearing Performance Degradation assessment method based on HMM and nuisance attribute projection (NAP) is proposed. The proposed method can combine the information from the experimental data and the real-time data effectively and assess the Performance since the beginning of the monitoring. The effectiveness of the proposed method is verified through an accelerated life test of rolling element bearings.
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The changes of complexity in the Performance Degradation process of rolling element bearing
Journal of Vibration and Control, 2014Co-Authors: Jin ChenAbstract:Rolling element bearing is not only the most important failure unit in rotary machinery, but it is also the most common, and its Performance Degradation assessment has been proposed to realize equipment’s near-zero downtime and maximum productivity. Therefore, exploring effective indices is crucial. Rolling element bearing’s vibration signals often contain complex, non-stationary and nonlinear characteristics. In this work, the changes of complexity, which are measured by two nonlinear methods: correlation dimension and approximate entropy, in the Performance Degradation process of rolling element bearing based on simulation and bearing accelerated life test data have been studied. Results show that the complexity decreases with the development of obvious defects, and that correlation dimension and approximate entropy have different sensitivities to different Performance Degradation stages, with the different ability of reflecting the first Degradation stage being explained by their different computation ...
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Kolmogorov-Smirnov test for rolling bearing Performance Degradation assessment and prognosis:
Journal of Vibration and Control, 2010Co-Authors: Feiyun Cong, Jin ChenAbstract:Exploring an effective assessment index is significant for Performance Degradation assessment, which has been proposed to realize equipments’ near-zero downtime and maximum productivity. In this paper, the Kolmogorov-Smirnov test based on an autoregressive model is proposed to assess the Performance Degradation of rolling bearings. Accelerated life test (in Hangzhou Bearing Test and Research Center) of rolling bearings was performed to collect vibration data over a whole lifetime (normal-fault-failure). The result shows that the Kolmogorov-Smirnov test method can obviously detect incipient weak defects and can reflect Performance Degradation process well. In particular, it can detect abnormal stages earlier before the bearing steps into failure, which is significant in condition maintenance and prognosis.
Chunlei Liu - One of the best experts on this subject based on the ideXlab platform.
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Leading causes of TCP Performance Degradation over wireless links
Lecture Notes in Computer Science, 2005Co-Authors: Chunlei LiuAbstract:TCP is known to have Performance Degradation over wireless links but causes of the Performance Degradation have not been well studied. In order to understand the causes and to gain insight for future enhancements, we design a series of simulations to collect Performance data and use stepwise multiple regression to find the leading causes. Our analysis indicates that timeout is the dominant cause of wireless TCP Performance Degradation. Simulations show current enhancements fail to improve the timeout behavior and thus have limited improvement. Based on these findings, we propose a new enhancement that uses ECN to deliver congestion signals and utilizes the coherence among congestion signals to distinguish wireless losses from congestion losses. Simulation results demonstrate that this enhancement thoroughly changes TCP's timeout behavior and improves the overall Performance to a new level.
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ICESS - Leading causes of TCP Performance Degradation over wireless links
Embedded Software and Systems, 2005Co-Authors: Chunlei LiuAbstract:TCP is known to have Performance Degradation over wireless links but causes of the Performance Degradation have not been well studied. In order to understand the causes and to gain insight for future enhancements, we design a series of simulations to collect Performance data and use stepwise multiple regression to find the leading causes. Our analysis indicates that timeout is the dominant cause of wireless TCP Performance Degradation. Simulations show current enhancements fail to improve the timeout behavior and thus have limited improvement. Based on these findings, we propose a new enhancement that uses ECN to deliver congestion signals and utilizes the coherence among congestion signals to distinguish wireless losses from congestion losses. Simulation results demonstrate that this enhancement thoroughly changes TCP’s timeout behavior and improves the overall Performance to a new level.
Feiyun Cong - One of the best experts on this subject based on the ideXlab platform.
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Kolmogorov-Smirnov test for rolling bearing Performance Degradation assessment and prognosis:
Journal of Vibration and Control, 2010Co-Authors: Feiyun Cong, Jin ChenAbstract:Exploring an effective assessment index is significant for Performance Degradation assessment, which has been proposed to realize equipments’ near-zero downtime and maximum productivity. In this paper, the Kolmogorov-Smirnov test based on an autoregressive model is proposed to assess the Performance Degradation of rolling bearings. Accelerated life test (in Hangzhou Bearing Test and Research Center) of rolling bearings was performed to collect vibration data over a whole lifetime (normal-fault-failure). The result shows that the Kolmogorov-Smirnov test method can obviously detect incipient weak defects and can reflect Performance Degradation process well. In particular, it can detect abnormal stages earlier before the bearing steps into failure, which is significant in condition maintenance and prognosis.
Fang Liu - One of the best experts on this subject based on the ideXlab platform.
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A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM
IEEE Access, 2020Co-Authors: Chunguang Zhang, Aibin Guo, Fang LiuAbstract:In order to better characterize the Performance Degradation trend of rolling bearings, a new Performance Degradation evaluation method based on principal component analysis (PCA), phase space reconstruction (PSR) and kernel extreme learning machine (KELM), namely PAPRKM is proposed to evaluate the Performance Degradation of rolling bearings in this paper. In the PAPRKM method, the time-domain and frequency-domain features of the vibration signal are extracted to construct the high-dimension feature matrix. Then the PCA is used to reduce the dimension of the feature matrix in order to represent the running state and the declining trend of rolling bearings, so as to eliminate the redundancy and information conflict among these features. Nextly, the PSR is adopted to obtain more relevant information from the time series. By determining the delay time and embedding dimension, the time series are reconstructed to obtain a new Performance Degradation index, which is regarded as the input data to input into KELM, and the Degradation trend prediction model is established to realize the Performance Degradation trend prediction. Finally, the actual vibration signals of rolling bearings are applied to prove the effectiveness of the PAPRKM. The obtained experimental results show that the PAPRKM method can effectively predict the Performance Degradation trend of rolling bearings. The predicted results are more accurate than the other compared methods.
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Performance Degradation Prediction of Rolling Bearing based on KJADE and Holt–Winters
2020 International Conference on Sensing Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), 2020Co-Authors: Ran Wei, Zheng Cao, Fang Liu, Yongbin LiuAbstract:A Performance Degradation prediction method is proposed in this paper for condition monitoring and bearings Performance Degradation prediction. This method is the combination of kernel joint approximate diagonalization of eigen-matrices (KJADE) and Holt–Winters. First, the vibration signals acquired from running bearing are processed through multi-domain features extraction. An optimal feature set was obtained from the multi-domain features through dimensionality reduction and feature fusion using the KJADE algorithm. Then, the between- and within-class scatters were calculated to acquire the Performance Degradation indicators. Finally, the Performance Degradation pre- diction model based on Holt–Winters was established to predict the bearing Performance Degradation. Results show that bearing Degradation trend can be effectively identified by the proposed method. Moreover, the prediction accuracy of this method is higher than that of extreme learning machine (ELM).
Youxian Sun - One of the best experts on this subject based on the ideXlab platform.
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Performance Degradation Monitoring for Onboard Speed Sensors of Trains
IEEE Transactions on Intelligent Transportation Systems, 2012Co-Authors: Wenhai Wang, Youxian SunAbstract:Photoelectric speed sensors (PSSs), which are used for velocity measuring and positioning, are key components of train control systems. In real applications, the Performance of PSSs may degrade, such as the decrease in the number of the output pulses, which is caused by the existence of jammed code tracks on the shading plates of PSSs. Considering this kind of Performance Degradation, this paper proposes an online Performance-Degradation-monitoring approach that can detect the existence of the jammed code tracks and estimate the number of them. Based on the results from the Performance-Degradation-monitoring approach, this paper also provides a compensation algorithm for the distorted speed readings resulting from the existence of the Performance Degradation. The results from the mathematical analysis and numerical examples verify the effectiveness of the proposed Performance-Degradation-monitoring approach for PSSs.
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Performance Degradation monitoring and speed reading compensation method for on-board radar speed sensors of trains
China Railway Science, 2011Co-Authors: Wenhai Wang, Youxian SunAbstract:According to the working principle of the on-board radar speed sensors (RSSs) and the variation law of the radar antenna angle, the mathematical model for Performance Degradation of speed sensors is developed. Using the established Performance Degradation model and the train operation distance given by track-side balise, the mathematic relation between train operation distance and the antenna angle deviation is derived to realize the on-line monitoring of the RSSs Performance Degradation. Based on the on-line monitoring results, the speed reading compensation approach for speed sensors is also proposed to guarantee the accuracy of speed measurement results. A numerical example is adopted to simulate and verify the proposed on-line Performance Degradation monitoring approach and the speed reading compensation method. The result shows that these approaches can correctly monitor the Performance Degradation of the on-board RSSs and compensate the speed reading, which can guarantee the safe operation of trains.
