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Joao P. Hespanha - One of the best experts on this subject based on the ideXlab platform.

  • brief paper Root Mean Square gains of switched linear systems a variational approach
    Automatica, 2008
    Co-Authors: Michael Margaliot, Joao P. Hespanha
    Abstract:

    We consider the problem of computing the Root-Mean-Square (RMS) gain of switched linear systems. We develop a new approach which is based on an attempt to characterize the ''worst-case'' switching law (WCSL), that is, the switching law that yields the maximal possible gain. Our main result provides a sufficient condition guaranteeing that the WCSL can be characterized explicitly using the differential Riccati equations (DREs) corresponding to the linear subsystems. This condition automatically holds for first-order SISO systems, so we obtain a complete solution to the RMS gain problem in this case.

  • Root Mean Square gains of switched linear systems a variational approach
    Conference on Decision and Control, 2007
    Co-Authors: Michael Margaliot, Joao P. Hespanha
    Abstract:

    We consider the problem of computing the Root- Mean-Square (RMS) gain of switched linear systems. We develop a new approach which is based on an attempt to characterize the "worst-case" switching law (WCSL), that is, the switching law that yields the maximal possible gain. Our main result provides a sufficient condition guaranteeing that the WCSL can be characterized explicitly using the differential Riccati equations corresponding to the linear subsystems. This condition automatically holds for first-order systems, so we obtain a complete solution to the RMS gain problem in this case. In particular, we show that in the first-order case there always exists a WCSL with no more than two switches.

  • Root-Mean-Square gains of switched linear systems
    IEEE Transactions on Automatic Control, 2003
    Co-Authors: Joao P. Hespanha
    Abstract:

    In this note, we compute the Root-Mean-Square (RMS) gain of a switched linear system when the interval between consecutive switchings is large. The algorithm proposed is based on the fact that a given constant /spl gamma/ provides an upper bound on the RMS gain whenever there is a separation between the stabilizing and the antistabilizing solutions to a set of /spl gamma/-dependent algebraic Riccati equations. The motivation for this problem is the application of robust stability tools to the analysis of hybrid systems.

  • HSCC - Computation of Root-Mean-Square Gains of Switched Linear Systems
    Hybrid Systems: Computation and Control, 2002
    Co-Authors: Joao P. Hespanha
    Abstract:

    In this paper we compute the Root-Mean-Square (RMS) gain of a switched linear system when the interval between consecutive switchings is large. The algorithm proposed is based on the fact that a given constant ? provides an upper bound on the RMS gain whenever there is a separation between the stabilizing and the antistabilizing solutions to a set of ?-dependent algebraic Riccati equations. The motivation for this problemis the application of robust stability tools to the analysis of hybrid systems.

  • Computation of Root-Mean-Square gains of switched linear systems - eScholarship
    2002
    Co-Authors: Joao P. Hespanha
    Abstract:

    In this paper we compute the Root-Mean-Square (RMS) gain of a switched linear system when the interval between consecutive switchings is large. The algorithm proposed is based on the fact that a given constant gamma provides an upper bound on the RMS gain whenever there is a separation between the stabilizing and the antistabilizing solutions to a set of gamma-dependent algebraic Riccati equations. The motivation for this problem is the application of robust stability tools to the analysis of hybrid systems.

Abolfazl Rahmani - One of the best experts on this subject based on the ideXlab platform.

  • Multi-fault diagnosis of ball bearing using FFT, wavelet energy entropy Mean and Root Mean Square (RMS)
    Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2010
    Co-Authors: Omid Rahmani Seryasat, Mahdi Aliyari Shoorehdeli, Farhang Honarvar, Abolfazl Rahmani
    Abstract:

    According to the non-stationary characteristics of ball bearing fault vibration signals, a ball bearing fault diagnosis method using FFT and wavelet energy entropy Mean and Root Mean Square (RMS), energy entropy Mean is put forward. in this paper, Firstly, original rushing vibration signals is transformed into a frequency domain, and is comminuted wavelet components, then the theory of energy entropy Mean and Root Mean Square is proposed. The analysis results from energy entropy and Root Mean Square of different vibration signals show that the energy and Root Mean Square of vibration signal will change in different frequency bands when bearing fault occurs. Therefore, to diagnose ball bearing faults, we run the test rig with faulty ball bearing in various speeds and loads and collect vibration signals in each run then, calculate the energy entropy Mean and Root Mean Square which indicate the fault types. The analysis results from ball bearing signals with six different faults in various working conditions show that the diagnosis approach based on using wavelet and FFT to extract the energy and Root Mean Square of different frequency bands can identify ball bearing faults accurately and effectively. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method has good computational efficiency, and has good resolution in both, time and frequency domains. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis.

  • SMC - Multi-fault diagnosis of ball bearing using FFT, wavelet energy entropy Mean and Root Mean Square (RMS)
    2010 IEEE International Conference on Systems Man and Cybernetics, 2010
    Co-Authors: Omid Rahmani Seryasat, Farhang Honarvar, M. Aliyari Shoorehdeli, Abolfazl Rahmani
    Abstract:

    According to the non-stationary characteristics of ball bearing fault vibration signals, a ball bearing fault diagnosis method using FFT and wavelet energy entropy Mean and Root Mean Square (RMS), energy entropy Mean is put forward. in this paper, Firstly, original rushing vibration signals is transformed into a frequency domain, and is comminuted wavelet components, then the theory of energy entropy Mean and Root Mean Square is proposed. The analysis results from energy entropy and Root Mean Square of different vibration signals show that the energy and Root Mean Square of vibration signal will change in different frequency bands when bearing fault occurs. Therefore, to diagnose ball bearing faults, we run the test rig with faulty ball bearing in various speeds and loads and collect vibration signals in each run then, calculate the energy entropy Mean and Root Mean Square which indicate the fault types. The analysis results from ball bearing signals with six different faults in various working conditions show that the diagnosis approach based on using wavelet and FFT to extract the energy and Root Mean Square of different frequency bands can identify ball bearing faults accurately and effectively. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method has good computational efficiency, and has good resolution in both, time and frequency domains. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis.

Omid Rahmani Seryasat - One of the best experts on this subject based on the ideXlab platform.

  • Multi-fault diagnosis of ball bearing using FFT, wavelet energy entropy Mean and Root Mean Square (RMS)
    Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2010
    Co-Authors: Omid Rahmani Seryasat, Mahdi Aliyari Shoorehdeli, Farhang Honarvar, Abolfazl Rahmani
    Abstract:

    According to the non-stationary characteristics of ball bearing fault vibration signals, a ball bearing fault diagnosis method using FFT and wavelet energy entropy Mean and Root Mean Square (RMS), energy entropy Mean is put forward. in this paper, Firstly, original rushing vibration signals is transformed into a frequency domain, and is comminuted wavelet components, then the theory of energy entropy Mean and Root Mean Square is proposed. The analysis results from energy entropy and Root Mean Square of different vibration signals show that the energy and Root Mean Square of vibration signal will change in different frequency bands when bearing fault occurs. Therefore, to diagnose ball bearing faults, we run the test rig with faulty ball bearing in various speeds and loads and collect vibration signals in each run then, calculate the energy entropy Mean and Root Mean Square which indicate the fault types. The analysis results from ball bearing signals with six different faults in various working conditions show that the diagnosis approach based on using wavelet and FFT to extract the energy and Root Mean Square of different frequency bands can identify ball bearing faults accurately and effectively. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method has good computational efficiency, and has good resolution in both, time and frequency domains. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis.

  • SMC - Multi-fault diagnosis of ball bearing using FFT, wavelet energy entropy Mean and Root Mean Square (RMS)
    2010 IEEE International Conference on Systems Man and Cybernetics, 2010
    Co-Authors: Omid Rahmani Seryasat, Farhang Honarvar, M. Aliyari Shoorehdeli, Abolfazl Rahmani
    Abstract:

    According to the non-stationary characteristics of ball bearing fault vibration signals, a ball bearing fault diagnosis method using FFT and wavelet energy entropy Mean and Root Mean Square (RMS), energy entropy Mean is put forward. in this paper, Firstly, original rushing vibration signals is transformed into a frequency domain, and is comminuted wavelet components, then the theory of energy entropy Mean and Root Mean Square is proposed. The analysis results from energy entropy and Root Mean Square of different vibration signals show that the energy and Root Mean Square of vibration signal will change in different frequency bands when bearing fault occurs. Therefore, to diagnose ball bearing faults, we run the test rig with faulty ball bearing in various speeds and loads and collect vibration signals in each run then, calculate the energy entropy Mean and Root Mean Square which indicate the fault types. The analysis results from ball bearing signals with six different faults in various working conditions show that the diagnosis approach based on using wavelet and FFT to extract the energy and Root Mean Square of different frequency bands can identify ball bearing faults accurately and effectively. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method has good computational efficiency, and has good resolution in both, time and frequency domains. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis.

Farhang Honarvar - One of the best experts on this subject based on the ideXlab platform.

  • Multi-fault diagnosis of ball bearing using FFT, wavelet energy entropy Mean and Root Mean Square (RMS)
    Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2010
    Co-Authors: Omid Rahmani Seryasat, Mahdi Aliyari Shoorehdeli, Farhang Honarvar, Abolfazl Rahmani
    Abstract:

    According to the non-stationary characteristics of ball bearing fault vibration signals, a ball bearing fault diagnosis method using FFT and wavelet energy entropy Mean and Root Mean Square (RMS), energy entropy Mean is put forward. in this paper, Firstly, original rushing vibration signals is transformed into a frequency domain, and is comminuted wavelet components, then the theory of energy entropy Mean and Root Mean Square is proposed. The analysis results from energy entropy and Root Mean Square of different vibration signals show that the energy and Root Mean Square of vibration signal will change in different frequency bands when bearing fault occurs. Therefore, to diagnose ball bearing faults, we run the test rig with faulty ball bearing in various speeds and loads and collect vibration signals in each run then, calculate the energy entropy Mean and Root Mean Square which indicate the fault types. The analysis results from ball bearing signals with six different faults in various working conditions show that the diagnosis approach based on using wavelet and FFT to extract the energy and Root Mean Square of different frequency bands can identify ball bearing faults accurately and effectively. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method has good computational efficiency, and has good resolution in both, time and frequency domains. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis.

  • SMC - Multi-fault diagnosis of ball bearing using FFT, wavelet energy entropy Mean and Root Mean Square (RMS)
    2010 IEEE International Conference on Systems Man and Cybernetics, 2010
    Co-Authors: Omid Rahmani Seryasat, Farhang Honarvar, M. Aliyari Shoorehdeli, Abolfazl Rahmani
    Abstract:

    According to the non-stationary characteristics of ball bearing fault vibration signals, a ball bearing fault diagnosis method using FFT and wavelet energy entropy Mean and Root Mean Square (RMS), energy entropy Mean is put forward. in this paper, Firstly, original rushing vibration signals is transformed into a frequency domain, and is comminuted wavelet components, then the theory of energy entropy Mean and Root Mean Square is proposed. The analysis results from energy entropy and Root Mean Square of different vibration signals show that the energy and Root Mean Square of vibration signal will change in different frequency bands when bearing fault occurs. Therefore, to diagnose ball bearing faults, we run the test rig with faulty ball bearing in various speeds and loads and collect vibration signals in each run then, calculate the energy entropy Mean and Root Mean Square which indicate the fault types. The analysis results from ball bearing signals with six different faults in various working conditions show that the diagnosis approach based on using wavelet and FFT to extract the energy and Root Mean Square of different frequency bands can identify ball bearing faults accurately and effectively. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method has good computational efficiency, and has good resolution in both, time and frequency domains. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis.

Raymond J. Carroll - One of the best experts on this subject based on the ideXlab platform.

  • testing hardy weinberg equilibrium with a simple Root Mean Square statistic
    Biostatistics, 2014
    Co-Authors: Rachel Ward, Raymond J. Carroll
    Abstract:

    SUMMARY We provide evidence that, in certain circumstances, a Root-Mean-Square test of goodness of fit can be significantly more powerful than state-of-the-art tests in detecting deviations from Hardy–Weinberg equilibrium. Unlike Pearson’s χ 2 test, the log-likelihood-ratio test, and Fisher’s exact test, which are sensitive to relative discrepancies between genotypic frequencies, the Root-Mean-Square test is sensitive to absolute discrepancies. This can increase statistical power, as we demonstrate using benchmark data sets and simulations, and through asymptotic analysis.

  • Testing Hardy–Weinberg equilibrium with a simple Root-Mean-Square statistic
    Biostatistics (Oxford England), 2013
    Co-Authors: Rachel Ward, Raymond J. Carroll
    Abstract:

    SUMMARY We provide evidence that, in certain circumstances, a Root-Mean-Square test of goodness of fit can be significantly more powerful than state-of-the-art tests in detecting deviations from Hardy–Weinberg equilibrium. Unlike Pearson’s χ 2 test, the log-likelihood-ratio test, and Fisher’s exact test, which are sensitive to relative discrepancies between genotypic frequencies, the Root-Mean-Square test is sensitive to absolute discrepancies. This can increase statistical power, as we demonstrate using benchmark data sets and simulations, and through asymptotic analysis.

  • Testing Hardy-Weinberg equilibrium with a simple Root-Mean-Square statistic
    arXiv: Methodology, 2012
    Co-Authors: Rachel Ward, Raymond J. Carroll
    Abstract:

    We provide evidence that a Root-Mean-Square test of goodness-of-fit can be significantly more powerful than state-of-the-art exact tests in detecting deviations from Hardy-Weinberg equilibrium. Unlike Pearson's chi-Square test, the log--likelihood-ratio test, and Fisher's exact test, which are sensitive to relative discrepancies between genotypic frequencies, the Root-Mean-Square test is sensitive to absolute discrepancies. This can increase statistical power, as we demonstrate using benchmark datasets and through asymptotic analysis. With the aid of computers, exact P-values for the Root-Mean-Square statistic can be calculated eeffortlessly, and can be easily implemented using the author's freely available code.