In order to achieve quick and accurate lifetime prediction of LED lighting products under the testing time of 2 000 h, a method of online testing of luminous flux is proposed under the condition of temperature stress....In order to achieve quick and accurate lifetime prediction of LED lighting products under the testing time of 2 000 h, a method of online testing of luminous flux is proposed under the condition of temperature stress.Exponential fitting of lumen maintenance, the Bayesian estimation of failure probability, the Weibull distribution of lifetime and the Arrhenius model of the decay rate are used in combination to acquire the distribution of failure probability over time at the ambient temperatures of 25 ℃. The lifetime test of the same lamps based on the Energy Star standard under the testing time of 6 000 h is also implemented to verify the effectiveness of the method. The errors of lifetimes acquired with the proposed method are 7%, 4%, 3% and 1% at the failure probabilities of 62. 3%, 10%, 5% and 1%,respectively.展开更多
Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivate...Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivated to propose a new monitoring method by compensating the principal component analysis with a weight approach.The proposed monitor consists of two tiers. The first tier uses the principal component analysis method to extract cross-correlation structure among process data, expressed by independent components. The second tier estimates auto-correlation structure among the extracted components as auto-regressive models. It is therefore named a dynamic weighted principal component analysis with hybrid correlation structure. The essential of the proposed method is to incorporate a weight approach into principal component analysis to construct two new subspaces, namely the important component subspace and the residual subspace, and two new statistics are defined to monitor them respectively. Through computing the weight values upon a new observation, the proposed method increases the weights along directions of components that have large estimation errors while reduces the influences of other directions. The rationale behind comes from the observations that the fault information is associated with online estimation errors of auto-regressive models. The proposed monitoring method is exemplified by the Tennessee Eastman process. The monitoring results show that the proposed method outperforms conventional principal component analysis, dynamic principal component analysis and dynamic latent variable.展开更多
High-temperature heating surface such as superheater and reheater of large-sized utility boiler all experiences a relatively severe working conditions. The failure of boiler tubes will directly impact the safe and eco...High-temperature heating surface such as superheater and reheater of large-sized utility boiler all experiences a relatively severe working conditions. The failure of boiler tubes will directly impact the safe and economic operation of boiler. An on-line life monitoring model of high-temperature heating surface was set up according to the well-known L-M formula of the creep damages. The tube wall metal temperature and working stress was measured by on-line monitoring, and with this model, the real-time calculation of the life expenditure of the heating surface tube bundles were realized. Based on the technique the on-line life monitoring and management system of high-temperature heating surface was developed for a 300 MW utility boiler. An effective device was thus suggested for the implementation of the safe operation and the condition-based maintenance of utility boilers.展开更多
基金The Cui Can Project of Chinese Academy of Sciences(No.KZCC-EW-102)the National High Technology Research and Development Program of China(863 Program)(No.2015AA03A101,2013AA03A116)
文摘In order to achieve quick and accurate lifetime prediction of LED lighting products under the testing time of 2 000 h, a method of online testing of luminous flux is proposed under the condition of temperature stress.Exponential fitting of lumen maintenance, the Bayesian estimation of failure probability, the Weibull distribution of lifetime and the Arrhenius model of the decay rate are used in combination to acquire the distribution of failure probability over time at the ambient temperatures of 25 ℃. The lifetime test of the same lamps based on the Energy Star standard under the testing time of 6 000 h is also implemented to verify the effectiveness of the method. The errors of lifetimes acquired with the proposed method are 7%, 4%, 3% and 1% at the failure probabilities of 62. 3%, 10%, 5% and 1%,respectively.
基金Supported by the National Natural Science Foundation of China(61174114)the Research Fund for the Doctoral Program of Higher Education in China(20120101130016)+1 种基金the Natural Science Foundation of Zhejiang Province(LQ15F030006)and the Science and Technology Program Project of Zhejiang Province(2015C33033)
文摘Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivated to propose a new monitoring method by compensating the principal component analysis with a weight approach.The proposed monitor consists of two tiers. The first tier uses the principal component analysis method to extract cross-correlation structure among process data, expressed by independent components. The second tier estimates auto-correlation structure among the extracted components as auto-regressive models. It is therefore named a dynamic weighted principal component analysis with hybrid correlation structure. The essential of the proposed method is to incorporate a weight approach into principal component analysis to construct two new subspaces, namely the important component subspace and the residual subspace, and two new statistics are defined to monitor them respectively. Through computing the weight values upon a new observation, the proposed method increases the weights along directions of components that have large estimation errors while reduces the influences of other directions. The rationale behind comes from the observations that the fault information is associated with online estimation errors of auto-regressive models. The proposed monitoring method is exemplified by the Tennessee Eastman process. The monitoring results show that the proposed method outperforms conventional principal component analysis, dynamic principal component analysis and dynamic latent variable.
文摘High-temperature heating surface such as superheater and reheater of large-sized utility boiler all experiences a relatively severe working conditions. The failure of boiler tubes will directly impact the safe and economic operation of boiler. An on-line life monitoring model of high-temperature heating surface was set up according to the well-known L-M formula of the creep damages. The tube wall metal temperature and working stress was measured by on-line monitoring, and with this model, the real-time calculation of the life expenditure of the heating surface tube bundles were realized. Based on the technique the on-line life monitoring and management system of high-temperature heating surface was developed for a 300 MW utility boiler. An effective device was thus suggested for the implementation of the safe operation and the condition-based maintenance of utility boilers.
