Stochastic Change Detection in Uncertain Nonlinear Systems Using Data-drivenSystem Identification Methods
نویسندگان
چکیده
A stochastic change detection methodology for reliable monitoring complex nonlinear dynamic systems is proposed. For a semi-active magneto-rheological (MR) damper, the non-parametric, data-driven restoring force method was used to identify the nonlinear dynamic damping device. Both supervised and unsupervised statistical pattern recognition techniques were used to detect the changes in the physical characteristics of the MR damper with different input currents. The classification errors were analyzed to find the optimal strategy for designing change detection classifiers for reliable structural health monitoring (SHM) applications.
منابع مشابه
Identification of Multiple Input-multiple Output Non-linear System Cement Rotary Kiln using Stochastic Gradient-based Rough-neural Network
Because of the existing interactions among the variables of a multiple input-multiple output (MIMO) nonlinear system, its identification is a difficult task, particularly in the presence of uncertainties. Cement rotary kiln (CRK) is a MIMO nonlinear system in the cement factory with a complicated mechanism and uncertain disturbances. The identification of CRK is very important for different pur...
متن کاملSensor Fault Detection for a class of Uncertain Nonlinear Systems Using Sliding Mode Observers
This paper deals with the issues of sensor fault detection for a class of Lipschitz uncertain nonlinear system. By definition coordinate transformation matrix for system states and output system, at first the original system divided into two subsystems. the first subsystem includes uncertainties but without any sensor faults and the second subsystem has sensor faults but is free of uncertaintie...
متن کاملStable Rough Extreme Learning Machines for the Identification of Uncertain Continuous-Time Nonlinear Systems
Rough extreme learning machines (RELMs) are rough-neural networks with one hidden layer where the parameters between the inputs and hidden neurons are arbitrarily chosen and never updated. In this paper, we propose RELMs with a stable online learning algorithm for the identification of continuous-time nonlinear systems in the presence of noises and uncertainties, and we prove the global ...
متن کاملIdentification of Fraud in Banking Data and Financial Institutions Using Classification Algorithms
In recent years, due to the expansion of financial institutions,as well as the popularity of the World Wide Weband e-commerce, a significant increase in the volume offinancial transactions observed. In addition to the increasein turnover, a huge increase in the number of fraud by user’sabnormality is resulting in billions of dollars in lossesover the world. T...
متن کاملIdentification of Fraud in Banking Data and Financial Institutions Using Classification Algorithms
In recent years, due to the expansion of financial institutions,as well as the popularity of the World Wide Weband e-commerce, a significant increase in the volume offinancial transactions observed. In addition to the increasein turnover, a huge increase in the number of fraud by user’sabnormality is resulting in billions of dollars in lossesover the world. T...
متن کامل