Complex industrial and aerospatial systems require efficient monitoring and fault detection schemes to ease prognosis and health monitoring tasks. In this work we rely upon the model-based approach to perform robust f...
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ISBN:
(纸本)9781936263011
Complex industrial and aerospatial systems require efficient monitoring and fault detection schemes to ease prognosis and health monitoring tasks. In this work we rely upon the model-based approach to perform robust fault detection and isolation using analytical and statistical models. We have combined Principal Component Analysis (PCA) together with Possible Conflicts (PCs), to improve the overall diagnosis process for complex system. Our proposal uses residuals computed using PCs as the input for the PCA tool. The PCA tool is able to accurately determine significant deviations in the residuals, that will be identified as faults. The integration of both techniques provides more robust results for fault detection, while avoiding false alarms in PCAs due to changes in operation modes. Moreover, it provides the PCA approach with the necessary mechanisms to perform fault isolation. This approach has been tested on a laboratory plant with real data, obtaining promising results.
The intersubcarrier interference(ICI) degrades the performance of the pilot-aided channel estimation in fast time-varying orthogonal frequency division multiplexing(OFDM) *** solve the error propagation in joint chann...
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The intersubcarrier interference(ICI) degrades the performance of the pilot-aided channel estimation in fast time-varying orthogonal frequency division multiplexing(OFDM) *** solve the error propagation in joint channel estimation and data detection due to this ICI,a scheme of error propagation determined iterative estimation is proposed,where in the first iteration,Kalman filter based on signal to interference and noise is designed with ICI transformed to be part of the noise,and for the later iterations,a determined iterative estimation algorithm obtains an optimal output from all iterations using the iterative updating *** results present the significant improvement in the performance of the proposed scheme in high-mobility situation in comparison with the existing ones.
Root counting and stability criteria involving polynomial roots inside the unit circle are available using the Schur-Cohn-Fujiwara matrix and certain Cauchy index calculations. This note illustrates a close connection...
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Root counting and stability criteria involving polynomial roots inside the unit circle are available using the Schur-Cohn-Fujiwara matrix and certain Cauchy index calculations. This note illustrates a close connection between these criteria, including reduced versions of them.
The present paper deals with the production of Saccharomyces cerevisiae, described by a sixth order nonlinear state space model. The control objective is to ensure the process stability and desirable specifications in...
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A general orthogonal parameter estimation algorithm is derived to estimate both the structure and the parameters for a wide range of stochastic nonlinear systems which can be described by a nonlinear rational model. S...
A general orthogonal parameter estimation algorithm is derived to estimate both the structure and the parameters for a wide range of stochastic nonlinear systems which can be described by a nonlinear rational model. Simulation studies are included to demonstrate the performance of the algorithm.
Wave excitations cause structural vibrations on the Oscillating Water Columns (OWC) lowering the generated power and reducing the life expectancy. The problem of generator deterioration has been considered for the Mut...
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A general structure detection and parameter estimation algorithm is derived for the identification of nonlinear systems that can be approximated by a stochastic nonlinear rational model defined as the ratio of two pol...
A general structure detection and parameter estimation algorithm is derived for the identification of nonlinear systems that can be approximated by a stochastic nonlinear rational model defined as the ratio of two polynomial expansions of past inputs, outputs and noise sequences. The algorithm includes an intelligent structure detection module that learns the structure of the model from the input/output data. Simulation results are included to illustrate the application of the new algorithm.
As to the real-time positioning demands for micro assembly process, this paper proposes a way which is based on Relevance Vector Machine Regression (RVMR). It solves the low efficiency problem which usually accompanie...
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ISBN:
(纸本)9781479907571
As to the real-time positioning demands for micro assembly process, this paper proposes a way which is based on Relevance Vector Machine Regression (RVMR). It solves the low efficiency problem which usually accompanies other common regression algorithms because the regression pattern is not sparse enough. This paper brings out grading RVMR, adopting the thought what is called "From coarse to fine". In this way, the number of training samples is greatly reduced while guaranteeing precision. So the off-line training efficiency is improved, meeting various parts in micro assembly process. In this algorithm, the algebra feature of the part image is extracted as the RVM's input, using Principal Component Analysis (PCA). Experiments on many regression algorithms and grading RVMR are both carried on. The results show that RVMR gets the shortest measuring time and the highest accuracy. The single axis estimation precision of part attitude is better than 0.5°.
This paper is concerned with the network-theoretic properties of so-called k-nearest neighbor intelligent vehicular platoons, where each vehicle communicates with k vehicles, both in front and behind. The network-theo...
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New higher order correlation tests which use model residuals combined with system inputs and outputs are presented to check the validity of a general class of nonlinear models. The new method is illustrated by testing...
New higher order correlation tests which use model residuals combined with system inputs and outputs are presented to check the validity of a general class of nonlinear models. The new method is illustrated by testing both simple and complex nonlinear system models.
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