The Experts below are selected from a list of 228 Experts worldwide ranked by ideXlab platform
Byeng D. Youn - One of the best experts on this subject based on the ideXlab platform.
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A co-training-based approach for prediction of remaining useful life utilizing both failure and Suspension data
Mechanical Systems and Signal Processing, 2015Co-Authors: Byeng D. Youn, Taejin Kim, Pingfeng WangAbstract:Abstract Traditional data-driven prognostics often requires some amount of failure data for the offline training in order to achieve good accuracy for the online prediction. Failure data refer to condition monitoring data collected from the very beginning of an engineered system׳s lifetime till the occurrence of its failure. However, in many engineered systems, failure data are fairly expensive and time-consuming to obtain while Suspension data are readily available. Suspension data refer to condition monitoring data acquired from the very beginning of an engineered system׳s lifetime till planned inspection or maintenance when the system is taken out of service. In such cases, it becomes essentially critical to utilize Suspension data which may carry rich information regarding the degradation trend and help achieve more accurate remaining useful life (RUL) prediction. To this end, this paper proposes a co-training-based data-driven prognostic approach, denoted by COPROG, which uses two data-driven algorithms with each predicting RULs of Suspension Units for the other. After a Suspension Unit is chosen and its RUL is predicted by an individual algorithm, it becomes a virtual failure Unit that is added to the training data set of the other individual algorithm. Results obtained from two case studies suggest that COPROG gives more accurate RUL prediction, as compared to any individual algorithm with no use of Suspension data, and that COPROG can effectively exploit Suspension data to improve the prognostic accuracy.
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A co-training-based approach for prediction of remaining useful life utilizing both failure and Suspension data
Mechanical Systems and Signal Processing, 2015Co-Authors: Byeng D. Youn, Taejin Kim, Pingfeng WangAbstract:Click on the DOI link to access the article (may not be free).Traditional data-driven prognostics often requires some amount of failure data for the offline training in order to achieve good accuracy for the online prediction. Failure data refer to condition monitoring data collected from the very beginning of an engineered system's lifetime till the occurrence of its failure. However, in many engineered systems, failure data are fairly expensive and time-consuming to obtain while Suspension data are readily available. Suspension data refer to condition monitoring data acquired from the very beginning of an engineered system's lifetime till planned inspection or maintenance when the system is taken out of service. In such cases, it becomes essentially critical to utilize Suspension data which may carry rich information regarding the degradation trend and help achieve more accurate remaining useful life (RUL) prediction. To this end, this paper proposes a co-training-based data-driven prognostic approach, denoted by COPROG, which uses two data-driven algorithms with each predicting RULs of Suspension Units for the other. After a Suspension Unit is chosen and its RUL is predicted by an individual algorithm, it becomes a virtual failure Unit that is added to the training data set of the other individual algorithm. Results obtained from two case studies suggest that COPROG gives more accurate RUL prediction, as compared to any individual algorithm with no use of Suspension data, and that COPROG can effectively exploit Suspension data to improve the prognostic accuracy.Mid-Career Researcher Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2013R1A2A2A01068627), and the Technology Innovation Program (10050980, System Reliability Improvement and Validation for New Growth Power Industry Equipment) funded by the Ministry of Trade, Industry & Energy (MI, Korea)
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Semi-supervised learning with co-training for data-driven prognostics
2012 IEEE Conference on Prognostics and Health Management, 2012Co-Authors: Chao Hu, Byeng D. YounAbstract:Traditional data-driven prognostics often requires a large amount of failure data for the offline training in order to achieve good accuracy for the online prediction. However, in many engineered systems, failure data are fairly expensive and time-consuming to obtain while Suspension data are readily available. In such cases, it becomes essentially critical to utilize Suspension data, which may carry rich information regarding the degradation trend and help achieve more accurate remaining useful life (RUL) prediction. To this end, this paper proposes a co-training-based data-driven prognostic algorithm, denoted by Cop rog, which uses two individual data-driven algorithms with each predicting RULs of Suspension Units for the other. The confidence of an individual data-driven algorithm in predicting the RUL of a Suspension Unit is quantified by the extent to which the inclusion of that Unit in the training data set reduces the sum square error (SSE) in RUL prediction on the failure Units. After a Suspension Unit is chosen and its RUL is predicted by an individual algorithm, it becomes a virtual failure Unit that is added to the training data set. Results obtained from two case studies suggest that Coprog gives more accurate RUL predictions compared to any individual algorithm without the consideration of Suspension data and that Coprog can effectively exploit Suspension data to improve the accuracy in data-driven prognostics.
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Semi-Supervised Learning With Co-Training for Data-Driven Prognostics
Volume 2: 31st Computers and Information in Engineering Conference Parts A and B, 2011Co-Authors: Byeng D. Youn, Taejin KimAbstract:Traditional data-driven prognostics often requires a large amount of failure data for the offline training in order to achieve good accuracy for the online prediction. However, in many engineered systems, failure data are fairly expensive and time-consuming to obtain while Suspension data are readily available. In such cases, it becomes essentially critical to utilize Suspension data, which may carry rich information regarding the degradation trend and help achieve more accurate remaining useful life (RUL) prediction. To this end, this paper proposes a co-training-based data-driven prognostic algorithm, denoted by Coprog , which uses two individual data-driven algorithms with each predicting RULs of Suspension Units for the other. The confidence of an individual data-driven algorithm in predicting the RUL of a Suspension Unit is quantified by the extent to which the inclusion of that Unit in the training data set reduces the sum square error (SSE) in RUL prediction on the failure Units. After a Suspension Unit is chosen and its RUL is predicted by an individual algorithm, it becomes a virtual failure Unit that is added to the training data set. Results obtained from two case studies suggest that Coprog gives more accurate RUL predictions compared to any individual algorithm without the consideration of Suspension data and that Coprog can effectively exploit Suspension data to improve the accuracy in data-driven prognostics.Copyright © 2011 by ASME
Pingfeng Wang - One of the best experts on this subject based on the ideXlab platform.
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A co-training-based approach for prediction of remaining useful life utilizing both failure and Suspension data
Mechanical Systems and Signal Processing, 2015Co-Authors: Byeng D. Youn, Taejin Kim, Pingfeng WangAbstract:Abstract Traditional data-driven prognostics often requires some amount of failure data for the offline training in order to achieve good accuracy for the online prediction. Failure data refer to condition monitoring data collected from the very beginning of an engineered system׳s lifetime till the occurrence of its failure. However, in many engineered systems, failure data are fairly expensive and time-consuming to obtain while Suspension data are readily available. Suspension data refer to condition monitoring data acquired from the very beginning of an engineered system׳s lifetime till planned inspection or maintenance when the system is taken out of service. In such cases, it becomes essentially critical to utilize Suspension data which may carry rich information regarding the degradation trend and help achieve more accurate remaining useful life (RUL) prediction. To this end, this paper proposes a co-training-based data-driven prognostic approach, denoted by COPROG, which uses two data-driven algorithms with each predicting RULs of Suspension Units for the other. After a Suspension Unit is chosen and its RUL is predicted by an individual algorithm, it becomes a virtual failure Unit that is added to the training data set of the other individual algorithm. Results obtained from two case studies suggest that COPROG gives more accurate RUL prediction, as compared to any individual algorithm with no use of Suspension data, and that COPROG can effectively exploit Suspension data to improve the prognostic accuracy.
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A co-training-based approach for prediction of remaining useful life utilizing both failure and Suspension data
Mechanical Systems and Signal Processing, 2015Co-Authors: Byeng D. Youn, Taejin Kim, Pingfeng WangAbstract:Click on the DOI link to access the article (may not be free).Traditional data-driven prognostics often requires some amount of failure data for the offline training in order to achieve good accuracy for the online prediction. Failure data refer to condition monitoring data collected from the very beginning of an engineered system's lifetime till the occurrence of its failure. However, in many engineered systems, failure data are fairly expensive and time-consuming to obtain while Suspension data are readily available. Suspension data refer to condition monitoring data acquired from the very beginning of an engineered system's lifetime till planned inspection or maintenance when the system is taken out of service. In such cases, it becomes essentially critical to utilize Suspension data which may carry rich information regarding the degradation trend and help achieve more accurate remaining useful life (RUL) prediction. To this end, this paper proposes a co-training-based data-driven prognostic approach, denoted by COPROG, which uses two data-driven algorithms with each predicting RULs of Suspension Units for the other. After a Suspension Unit is chosen and its RUL is predicted by an individual algorithm, it becomes a virtual failure Unit that is added to the training data set of the other individual algorithm. Results obtained from two case studies suggest that COPROG gives more accurate RUL prediction, as compared to any individual algorithm with no use of Suspension data, and that COPROG can effectively exploit Suspension data to improve the prognostic accuracy.Mid-Career Researcher Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2013R1A2A2A01068627), and the Technology Innovation Program (10050980, System Reliability Improvement and Validation for New Growth Power Industry Equipment) funded by the Ministry of Trade, Industry & Energy (MI, Korea)
Taejin Kim - One of the best experts on this subject based on the ideXlab platform.
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A co-training-based approach for prediction of remaining useful life utilizing both failure and Suspension data
Mechanical Systems and Signal Processing, 2015Co-Authors: Byeng D. Youn, Taejin Kim, Pingfeng WangAbstract:Abstract Traditional data-driven prognostics often requires some amount of failure data for the offline training in order to achieve good accuracy for the online prediction. Failure data refer to condition monitoring data collected from the very beginning of an engineered system׳s lifetime till the occurrence of its failure. However, in many engineered systems, failure data are fairly expensive and time-consuming to obtain while Suspension data are readily available. Suspension data refer to condition monitoring data acquired from the very beginning of an engineered system׳s lifetime till planned inspection or maintenance when the system is taken out of service. In such cases, it becomes essentially critical to utilize Suspension data which may carry rich information regarding the degradation trend and help achieve more accurate remaining useful life (RUL) prediction. To this end, this paper proposes a co-training-based data-driven prognostic approach, denoted by COPROG, which uses two data-driven algorithms with each predicting RULs of Suspension Units for the other. After a Suspension Unit is chosen and its RUL is predicted by an individual algorithm, it becomes a virtual failure Unit that is added to the training data set of the other individual algorithm. Results obtained from two case studies suggest that COPROG gives more accurate RUL prediction, as compared to any individual algorithm with no use of Suspension data, and that COPROG can effectively exploit Suspension data to improve the prognostic accuracy.
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A co-training-based approach for prediction of remaining useful life utilizing both failure and Suspension data
Mechanical Systems and Signal Processing, 2015Co-Authors: Byeng D. Youn, Taejin Kim, Pingfeng WangAbstract:Click on the DOI link to access the article (may not be free).Traditional data-driven prognostics often requires some amount of failure data for the offline training in order to achieve good accuracy for the online prediction. Failure data refer to condition monitoring data collected from the very beginning of an engineered system's lifetime till the occurrence of its failure. However, in many engineered systems, failure data are fairly expensive and time-consuming to obtain while Suspension data are readily available. Suspension data refer to condition monitoring data acquired from the very beginning of an engineered system's lifetime till planned inspection or maintenance when the system is taken out of service. In such cases, it becomes essentially critical to utilize Suspension data which may carry rich information regarding the degradation trend and help achieve more accurate remaining useful life (RUL) prediction. To this end, this paper proposes a co-training-based data-driven prognostic approach, denoted by COPROG, which uses two data-driven algorithms with each predicting RULs of Suspension Units for the other. After a Suspension Unit is chosen and its RUL is predicted by an individual algorithm, it becomes a virtual failure Unit that is added to the training data set of the other individual algorithm. Results obtained from two case studies suggest that COPROG gives more accurate RUL prediction, as compared to any individual algorithm with no use of Suspension data, and that COPROG can effectively exploit Suspension data to improve the prognostic accuracy.Mid-Career Researcher Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2013R1A2A2A01068627), and the Technology Innovation Program (10050980, System Reliability Improvement and Validation for New Growth Power Industry Equipment) funded by the Ministry of Trade, Industry & Energy (MI, Korea)
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Semi-Supervised Learning With Co-Training for Data-Driven Prognostics
Volume 2: 31st Computers and Information in Engineering Conference Parts A and B, 2011Co-Authors: Byeng D. Youn, Taejin KimAbstract:Traditional data-driven prognostics often requires a large amount of failure data for the offline training in order to achieve good accuracy for the online prediction. However, in many engineered systems, failure data are fairly expensive and time-consuming to obtain while Suspension data are readily available. In such cases, it becomes essentially critical to utilize Suspension data, which may carry rich information regarding the degradation trend and help achieve more accurate remaining useful life (RUL) prediction. To this end, this paper proposes a co-training-based data-driven prognostic algorithm, denoted by Coprog , which uses two individual data-driven algorithms with each predicting RULs of Suspension Units for the other. The confidence of an individual data-driven algorithm in predicting the RUL of a Suspension Unit is quantified by the extent to which the inclusion of that Unit in the training data set reduces the sum square error (SSE) in RUL prediction on the failure Units. After a Suspension Unit is chosen and its RUL is predicted by an individual algorithm, it becomes a virtual failure Unit that is added to the training data set. Results obtained from two case studies suggest that Coprog gives more accurate RUL predictions compared to any individual algorithm without the consideration of Suspension data and that Coprog can effectively exploit Suspension data to improve the accuracy in data-driven prognostics.Copyright © 2011 by ASME
Per-olof Gutman - One of the best experts on this subject based on the ideXlab platform.
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Active Suspension for single wheel station of off‐road track vehicle
International Journal of Robust and Nonlinear Control, 2001Co-Authors: Alex Liberzon, D. Rubinstein, Per-olof GutmanAbstract:The main objective of ground vehicle Suspension systems is to isolate a vehicle body (sprung mass) from road irregularities in order to maximize passenger ride comfort, control the attitude of the vehicle on the road, and retain continuous road–wheel contact in order to provide vehicle holding quality. An appropriate active Suspension design must resolve the inherent tradeoffs between ride comfort, road holding quality, and Suspension travel. In this study, a robust controller for the active Suspension of an off-road, high-mobility tracked vehicle is designed, for the first time, using quantitative feedback theory (QFT). A simulation model of a single Suspension Unit of the M-113 armored personnel carrier vehicle was used to achieve a proper active Suspension design. Two measured states of the 3-degrees-of-freedom mathematical model were used as feedback signals in a cascaded SISO control system. The nonlinear dynamics of the tracked vehicle Suspension Unit was represented as a set of linear time invariant (LTI) transfer functions, which were identified with the Fourier integral method. Computer simulations of the vehicle with passive and active Suspension systems over different terrain profiles are provided. A significant reduction of the vertical accelerations induced to the sprung mass (e.g., ride comfort improvement) was achieved while keeping the road-arm between Suspension travel limits (e.g., handling quality). Copyright © 2001 John Wiley & Sons, Ltd.
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Active control for single wheel Suspension of off-road track vehicle
IFAC Proceedings Volumes, 1998Co-Authors: Alex Liberzon, Per-olof Gutman, D. RubinsteinAbstract:The main objective of ground vehicles Suspension system is to isolate a vehicle body (sprung mass) from road irregularities in order to maximize the passenger ride comfort and produce continuous road-wheel contact in order to provide vehicle holding quality. An appropriate active Suspension design must resolve the inherent tradeoffs between ride comfort, road holding quality and Suspension travel. In this study a robust controller for the active Suspension of an off-road, high-mobility tracked vehicle is designed using Quantitative Feedback Theory (QFT). A simulation model of a single Suspension Unit of the armored personnel carrier vehicle M-113 was used to get a proper active Suspension design. Two measured states of the 3 degrees-of-freedom mathematical model were used as feedback signals in a cascaded SISO control system. The nonlinear dynamics of the tracked vehicle Suspension Unit was represented as a set of linear time invariant (LTI) transfer functions which were identified with the Fourier integral method. Computer simulations of the vehicle with passive and active Suspension systems over different terrain profiles are provided. Significant reduction of the vertical accelerations induced to the sprung mass (i.e. ride comfort improvement) was achieved while keeping the road-arm between Suspension travel limits (i.e. handling quality). Hence a potential important improvement in cross-country mobility for a wide range of ride conditions could possibly be achieved by application of linear robust control for active Suspension design for off-road tracked vehicles.
Alex Liberzon - One of the best experts on this subject based on the ideXlab platform.
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Active Suspension for single wheel station of off‐road track vehicle
International Journal of Robust and Nonlinear Control, 2001Co-Authors: Alex Liberzon, D. Rubinstein, Per-olof GutmanAbstract:The main objective of ground vehicle Suspension systems is to isolate a vehicle body (sprung mass) from road irregularities in order to maximize passenger ride comfort, control the attitude of the vehicle on the road, and retain continuous road–wheel contact in order to provide vehicle holding quality. An appropriate active Suspension design must resolve the inherent tradeoffs between ride comfort, road holding quality, and Suspension travel. In this study, a robust controller for the active Suspension of an off-road, high-mobility tracked vehicle is designed, for the first time, using quantitative feedback theory (QFT). A simulation model of a single Suspension Unit of the M-113 armored personnel carrier vehicle was used to achieve a proper active Suspension design. Two measured states of the 3-degrees-of-freedom mathematical model were used as feedback signals in a cascaded SISO control system. The nonlinear dynamics of the tracked vehicle Suspension Unit was represented as a set of linear time invariant (LTI) transfer functions, which were identified with the Fourier integral method. Computer simulations of the vehicle with passive and active Suspension systems over different terrain profiles are provided. A significant reduction of the vertical accelerations induced to the sprung mass (e.g., ride comfort improvement) was achieved while keeping the road-arm between Suspension travel limits (e.g., handling quality). Copyright © 2001 John Wiley & Sons, Ltd.
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Active control for single wheel Suspension of off-road track vehicle
IFAC Proceedings Volumes, 1998Co-Authors: Alex Liberzon, Per-olof Gutman, D. RubinsteinAbstract:The main objective of ground vehicles Suspension system is to isolate a vehicle body (sprung mass) from road irregularities in order to maximize the passenger ride comfort and produce continuous road-wheel contact in order to provide vehicle holding quality. An appropriate active Suspension design must resolve the inherent tradeoffs between ride comfort, road holding quality and Suspension travel. In this study a robust controller for the active Suspension of an off-road, high-mobility tracked vehicle is designed using Quantitative Feedback Theory (QFT). A simulation model of a single Suspension Unit of the armored personnel carrier vehicle M-113 was used to get a proper active Suspension design. Two measured states of the 3 degrees-of-freedom mathematical model were used as feedback signals in a cascaded SISO control system. The nonlinear dynamics of the tracked vehicle Suspension Unit was represented as a set of linear time invariant (LTI) transfer functions which were identified with the Fourier integral method. Computer simulations of the vehicle with passive and active Suspension systems over different terrain profiles are provided. Significant reduction of the vertical accelerations induced to the sprung mass (i.e. ride comfort improvement) was achieved while keeping the road-arm between Suspension travel limits (i.e. handling quality). Hence a potential important improvement in cross-country mobility for a wide range of ride conditions could possibly be achieved by application of linear robust control for active Suspension design for off-road tracked vehicles.
