Design for life-time performance and proper maintenance measures are usually needed to prolong the mean-time-between-failures of complex equipments such as internal combustion engines.To reach this,it is important to ...Design for life-time performance and proper maintenance measures are usually needed to prolong the mean-time-between-failures of complex equipments such as internal combustion engines.To reach this,it is important to obtain the information of time-varying system performance in design stage and to identify the structural change at each moment.So a multidisciplinary model based method is studied in this paper to unify the time-varying performance(TVP) prediction and system identification(SI) of equipments.The related multidisciplinary model in this paper should be not only precise to give simulation results but also sensitive to the variation of system parameters.So the varying history of system performance along with the structural change can be obtained from the model.Then the value of system parameters can be identified by seeking roots with given detected responding data and relationship between system responding data and system parameters.A case study on a low power gasoline engine shows that the method presented in this paper can provide useful information for the development and maintenance of complex equipments.展开更多
In traditional system identification (SI), actual values of system parameters are concealed in the input and output data;hence, it is necessary to apply estimation methods to determine the parameters. In signal proces...In traditional system identification (SI), actual values of system parameters are concealed in the input and output data;hence, it is necessary to apply estimation methods to determine the parameters. In signal processing, a signal with N elements must be sampled at least N times. Thus, most SI methods use N or more sample data to identify a model with N parameters;however, this can be improved by a new sampling theory called compressive sensing (CS). Based on CS, an SI method called compressive measurement identification (CMI) is proposed for reducing the data needed for estimation, by measuring the parameters using a series of linear measurements, rather than the measurements in sequence. In addition, the accuracy of the measurement process is guaranteed by a criterion called the restrict isometric principle. Simulations demonstrate the accuracy and robustness of CMI in an underdetermined case. Further, the dynamic process of a DC motor is identified experimentally, establishing that CMI can shorten the identification process and increase the prediction accuracy.展开更多
基金the National Natural Science Foundation of China (Nos. 50805091 and 50705055)the National Basic Research Program (973) of China(No. 2006CB705402)the Basic Research Programs of Science and Technology Commission of Shanghai City(No. 07JC14027)
文摘Design for life-time performance and proper maintenance measures are usually needed to prolong the mean-time-between-failures of complex equipments such as internal combustion engines.To reach this,it is important to obtain the information of time-varying system performance in design stage and to identify the structural change at each moment.So a multidisciplinary model based method is studied in this paper to unify the time-varying performance(TVP) prediction and system identification(SI) of equipments.The related multidisciplinary model in this paper should be not only precise to give simulation results but also sensitive to the variation of system parameters.So the varying history of system performance along with the structural change can be obtained from the model.Then the value of system parameters can be identified by seeking roots with given detected responding data and relationship between system responding data and system parameters.A case study on a low power gasoline engine shows that the method presented in this paper can provide useful information for the development and maintenance of complex equipments.
基金Supported by the National Natural Science Foundation of China(61605218)National Defense Science and Technology Innovation Foundation of Chinese Academy of Sciences(CXJJ-17S023)
文摘In traditional system identification (SI), actual values of system parameters are concealed in the input and output data;hence, it is necessary to apply estimation methods to determine the parameters. In signal processing, a signal with N elements must be sampled at least N times. Thus, most SI methods use N or more sample data to identify a model with N parameters;however, this can be improved by a new sampling theory called compressive sensing (CS). Based on CS, an SI method called compressive measurement identification (CMI) is proposed for reducing the data needed for estimation, by measuring the parameters using a series of linear measurements, rather than the measurements in sequence. In addition, the accuracy of the measurement process is guaranteed by a criterion called the restrict isometric principle. Simulations demonstrate the accuracy and robustness of CMI in an underdetermined case. Further, the dynamic process of a DC motor is identified experimentally, establishing that CMI can shorten the identification process and increase the prediction accuracy.
