The Experts below are selected from a list of 729894 Experts worldwide ranked by ideXlab platform
Zhipeng Feng - One of the best experts on this subject based on the ideXlab platform.
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adaptive iterative generalized demodulation for nonstationary complex Signal Analysis principle and application in rotating machinery fault diagnosis
Mechanical Systems and Signal Processing, 2018Co-Authors: Zhipeng Feng, Xiaowang ChenAbstract:Abstract Effective identification of Signal frequency contents and their time variability is a key to success in rotating machinery fault diagnosis under nonstationary conditions. Fine time-frequency resolution and free from both inner and outer interferences are necessary to achieve this purpose. Iterative generalized demodulation (IGD) can separate a nonstationary multi-component Signal into constituent mono-components, and derive quality time-frequency representation based on Hilbert spectrum of each mono-component, thus offering an effective approach to nonstationary complex Signal Analysis. Nevertheless, it requires prior expertise knowledge about Signal structure to ascertain true constituent components and construct proper demodulation phase functions. This leads to a major difficulty in real Signal Analysis tasks, where expertise knowledge is usually unavailable and Signal time-frequency structure identification via visual observation is susceptible to noise interferences. To address this issue, the capability of surrogate test technique in recognizing true Signal components is exploited, and thereby an adaptive iterative generalized demodulation (AIGD) is proposed. Proper demodulation phase functions corresponding to constituent components are derived accordingly, and Hilbert spectra of all constituent mono-components are superposed to generate time-frequency representation (TFR). This method features good merits, such as fine time-frequency resolution and free of both inner and outer interferences. Additionally, it neither relies on prior expertise knowledge about Signals, nor needs to construct any basis function. It is highly adaptive to reveal the frequency contents and track their time variability of a Signal, and provides an effective approach to nonstationary complex multi-component Signal Analysis. It is illustrated and validated via numerical simulations, lab experiments of a planetary gearbox and rolling bearings, and on-site tests of a hydraulic turbine in a hydraulic power plant.
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time frequency representation based on robust local mean decomposition for multicomponent am fm Signal Analysis
Mechanical Systems and Signal Processing, 2017Co-Authors: Zhipeng FengAbstract:Abstract Local mean decomposition (LMD) is a promising approach to implement time-frequency representation (TFR) for multicomponent amplitude-modulated (AM) and frequency-modulated (FM) Signal Analysis; however, its performance usually suffers from end effect and mode mixing problems. To address this issue, this paper proposes a novel comprehensive scheme to improve LMD performance. The novel scheme can automatically determine the fix subset size of the moving average algorithm and the optimal number of sifting iterations in a sifting process. Extensive simulations have been explored for multicomponent AM-FM Signal Analysis by means of TFR with the improved LMD. Moreover, the improved LMD shows potential application in bearing fault diagnosis in conjunction with the well-known fast kurtogram.
G Spiazzi - One of the best experts on this subject based on the ideXlab platform.
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small Signal Analysis of dc dc converters with sliding mode control
IEEE Transactions on Power Electronics, 1997Co-Authors: Paolo Mattavelli, L Rossetto, G SpiazziAbstract:This paper deals with small-Signal Analysis of DC-D converters with sliding mode control. A suitable small Signal model is developed which allows selection of control coefficients, Analysis of parameter variation effects, characterization of the closed loop behavior in terms of audiosusceptibility, output and input impedances, and reference to output transfer function. Unlike previous analyses, the model includes effects of the filters used to evaluate state variable errors. Simulated and experimental results demonstrate model potentialities.
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small Signal Analysis of dc dc converters with sliding mode control
Applied Power Electronics Conference, 1995Co-Authors: Paolo Mattavelli, L Rossetto, G SpiazziAbstract:The paper deals with small-Signal Analysis of DC-DC power converters with sliding mode control. A suitable small-Signal model is developed, which allows selection of control coefficients, Analysis of parameter variation effects and characterization of the closed-loop behavior in terms of audiosusceptibility, output and input impedances and reference-to-output transfer function. Unlike previous analyses, the model includes effects of the filters used to evaluate state variable errors. Simulated and experimental results demonstrate model potentialities. >
Robert S. Balog - One of the best experts on this subject based on the ideXlab platform.
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arc fault and flash Signal Analysis in dc distribution systems using wavelet transformation
IEEE Transactions on Smart Grid, 2015Co-Authors: Zhan Wang, Robert S. BalogAbstract:Arc faults have always been a concern for electrical systems, as they can cause fires, personnel shock hazard, and system failure. Existing commercialized techniques that rely on pattern recognition in the time domain or frequency domain Analysis using a Fourier transform do not work well, because the Signal-to-noise ratio is low and the arc Signal is not periodic. Instead, wavelet transform (WT) provides a time and frequency approach to analyze target Signals with multiple resolutions. In this paper, a new approach using WT for arc fault Analysis in dc systems is proposed. The process of detecting an arc fault involves Signal Analysis and then feature identification. The focus of this paper is on the former. Simulation models are synthesized to study the theoretical results of the proposed methodology and traditional fast Fourier transform Analysis on arcing faults. Experimental data from the dc system of a photovoltaic array is also shown to validate the approach.
Petar Popovski - One of the best experts on this subject based on the ideXlab platform.
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small Signal Analysis of the microgrid secondary control considering a communication time delay
arXiv: Systems and Control, 2017Co-Authors: E A A Coelho, Josep M Guerrero, Juan C Vasquez, Tomislav Dragicevic, Cedomir Stefanovic, Petar PopovskiAbstract:This paper presents a small-Signal Analysis of an islanded microgrid composed of two or more voltage source inverters connected in parallel. The primary control of each inverter is integrated through internal current and voltage loops using PR compensators, a virtual impedance, and an external power controller based on frequency and voltage droops. The frequency restoration function is implemented at the secondary control level, which executes a consensus algorithm that consists of a load-frequency control and a single time delay communication network. The consensus network consists of a time-invariant directed graph and the output power of each inverter is the information shared among the units, which is affected by the time delay. The proposed small-Signal model is validated through simulation results and experimental results. A root locus Analysis is presented that shows the behavior of the system considering control parameters and time delay variation.
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small Signal Analysis of the microgrid secondary control considering a communication time delay
IEEE Transactions on Industrial Electronics, 2016Co-Authors: E A A Coelho, Josep M Guerrero, Juan C Vasquez, Tomislav Dragicevic, Cedomir Stefanovic, Petar PopovskiAbstract:This paper presents a small-Signal Analysis of an islanded microgrid composed of two or more voltage-source inverters connected in parallel. The primary control of each inverter is integrated through an internal current and voltage loops using proportional resonant compensators, a virtual impedance, and an external power controller based on frequency and voltage droops. The frequency restoration function is implemented at the secondary control level, which executes a consensus algorithm that consists of a load-frequency control and a single time delay communication network. The consensus network consists of a time-invariant directed graph and the output power of each inverter is the information shared among the units, which is affected by the time delay. The proposed small-Signal model is validated through simulation results and experimental results. A root locus Analysis is presented that shows the behavior of the system considering control parameters and time delay variation.
Andrew All - One of the best experts on this subject based on the ideXlab platform.
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fault diagnosis of motor drives using stator current Signal Analysis based on dynamic time warping
Mechanical Systems and Signal Processing, 2013Co-Authors: Dong Zhe, Tie Wang, Andrew AllAbstract:Electrical motor stator current Signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current Signal Analysis is based on Fourier transform (FT) to extract weak fault sideband components from Signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current Signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference Signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw Signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current Signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual Signal Analysis through the introduction of a reference Signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual Signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the Analysis of motor current Signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.
