Structural modal parameter identifi cation and damage diagnosis based on Hilbert-Huang transform

Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.

NON-UNIFORM LINEAR ARRAY CONFIGURATION FOR MIMO RADAR

Array configuration of multiple-input multiple-output （MIMO） radar with non-uniform linear array （NLA） is proposed. Unlike a standard phased-array radar where NLA is used to generate thinner beam patterns, in MIMO radar the property of NLA is exploited to get more distinct virtual array elements so as to improve pa- rameter identifiability, which means the maximum number of targets that can be uniquely identified by the radar. A class of NLA called minimum redundancy linear array （MRLA） is employed and a new method to construct large MRLAs is descrihed. The numerical results verify that compared to uniform linear array （ULA） MIMO radars, NLA MIMO radars can retain the same parameter identifiability with fewer physical antennas and achieve larger aperture length and lower Cramer-Rao bound with the same number of the physical antennas.

Fitting dynamic models to epidemic outbreaks with quantified uncertainty:A primer for parameter uncertainty,identifiability,and forecasts

Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales,generate estimates of key kinetic parameters,assess the impact of interventions,optimize the impact of control strategies,and generate forecasts.We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models using time series data describing the temporal progression of case counts relating,for instance,to population growth or infectious disease transmission dynamics.In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters,this frequentist approach relies on modeling the error structure in the data.We discuss issues related to parameter identifiability,uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets.

Virtual test system for permanent-magnet DC motor

In order to obtain the primary parameters and operating characteristics of a DC motor without directly measuring its torque and rational speed, it is proposed to use a PC and a data acquisition card to acquire both the dynamic and static data of armature current to establish the performance of a DC permanent magnet motor. The accuracy and validity of this virtual test system proposed were verified by comparing the measurements made with the system proposed with the measurements made with conventional torque meters. It is concluded from the results of comparison that from the mathematic model established for the DC permant magnet motors, both major parameters and operating characteristics can be directly established for the DC motors without measuring their torques and rotational speed, a perfect on line measurement and test system has been established for the DC permanent magnet motors using the theory of virtual test system. The system proposed features shorter test time, higher efficiency and lower cost.

Bending-Torsional Coupled Vibration Test of Large Power Gear Transmission System

In order to test the bending-torsional coupled vibration characteristics of the multi-shafts gear transmission system of large power vehicles,a torsional vibration exciter was used to apply torsional excitation on the gear transmission systems and thirty-two acceleration sensors were used to measure the tangential acceleration of each shaft.Torsional vibration signals and bending vibration signals of each measuring point were obtained by calculation of the four-point-response signal.The modal parameters of gear transmission systems including nature frequency,modal shape and modal damping ratio were obtained by identifying modal parameters of the torsional vibration signal and bending vibration signal.The characteristic of the bending vibration and torsional vibration of the gear systems were studied through the analysis of the nature frequency and modal shape.The nonlinearity characteristic of the gear transmission system was investigated through single frequency excitation test,which can be the foundation for further nonlinearity research.