The Experts below are selected from a list of 36 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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time frequency analysis based on vold kalman filter and higher order energy separation for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions
Renewable Energy, 2016Co-Authors: Zhipeng Feng, Sifeng Qin, Ming LiangAbstract:Abstract Planetary gearbox fault diagnosis under nonstationary conditions is important for many engineering applications in general and for wind turbines in particular because of their time-varying operating conditions. This paper focuses on the identification of time-varying characteristic frequencies from complex nonstationary vibration signals for fault diagnosis of wind turbines under nonstationary conditions. We propose a time–frequency analysis method based on the Vold-Kalman filter and higher order energy separation (HOES) to extract fault symptoms. The Vold-Kalman filter is improved such that it is encoders/tachometers-free. It can decompose an arbitrarily complex signal into mono-Components without resorting to speed inputs, thus satisfying the mono-Component Requirement by the HOES algorithm. The HOES is then used to accurately estimate the instantaneous frequency because of its high adaptability to local signal changes. The derived time–frequency distribution features fine resolution without cross-term interferences and thus facilitates extracting time-varying frequency Components from highly complex and nonstationary signals. The method is illustrated and validated by analyzing simulated and experimental signals of a planetary gearbox in a wind turbine test rig under nonstationary running conditions. The results have shown that the method is effective in detecting both distributed (wear on every tooth) and localized (chipping on one tooth) gear faults.
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joint amplitude and frequency demodulation analysis based on local mean decomposition for fault diagnosis of planetary gearboxes
Mechanical Systems and Signal Processing, 2013Co-Authors: Zhipeng Feng, Ming J Zuo, Tao Tian, Zhiliang LiuAbstract:Abstract The vibration signals of faulty planetary gearboxes have complicated spectral structures due to the amplitude modulation and frequency modulation (AMFM) nature of gear damage induced vibration and the additional multiplicative amplitude modulation (AM) effect caused by the time-varying vibration transfer paths (for local gear damage case) and the passing planets (for distributed gear damage case). The spectral complexity leads to the difficulty in fault diagnosis of planetary gearboxes. Observing that both the amplitude envelope and the instantaneous frequency of planetary gearbox vibration signals are associated with the characteristic frequency of the faulty gear, a joint amplitude and frequency demodulation method is proposed for fault diagnosis of planetary gearboxes. In order to satisfy the mono-Component Requirement by instantaneous frequency estimation, a signal is firstly decomposed into product functions (PF) using the local mean decomposition (LMD) method. Then, the earliest extracted PF that has an instantaneous frequency fluctuating around the gear meshing frequency or its harmonics is chosen for further analysis, because it contains most of the information about the gear fault. The amplitude demodulation analysis can be accomplished through Fourier transforming the amplitude envelope of the chosen PF. For the frequency demodulation analysis, Fourier transform is applied to the estimated instantaneous frequency of the chosen PF to reveal its fluctuating frequency, thus obtaining the spectrum of the instantaneous frequency. By joint application of the amplitude and frequency demodulation methods, planetary gearbox faults can be diagnosed by matching the dominant peaks in the envelope spectrum and the spectrum of instantaneous frequency with the theoretical characteristic frequencies of faulty gears. The performance of the proposed method is illustrated by simulated signal analysis, and is validated by experimental signal analysis of a lab planetary gearbox with intentionally created pitting and naturally developed wear.
Uday Kumar Thakur - One of the best experts on this subject based on the ideXlab platform.
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a multivariate analysis of factors affecting blood Component Requirement in pediatric open heart surgeries
Indian Journal of Thoracic and Cardiovascular Surgery, 2012Co-Authors: R N Makroo, R C Joshi, R Gupta, Aakanksha Bhatia, Uday Kumar ThakurAbstract:Background Bleeding in children after Cardiopulmonary Bypass (CPB) can be an important cause of morbidity and mortality. Multiple perioperative factors have been evaluated to predict bleeding and hence blood and blood Component Requirements in such children.
Bryan A Cotton - One of the best experts on this subject based on the ideXlab platform.
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emergency department blood transfusion predicts early massive transfusion and early blood Component Requirement
Transfusion, 2010Co-Authors: Timothy C Nunez, William D Dutton, Addison K May, John B Holcomb, Pampee P Young, Bryan A CottonAbstract:BACKGROUND: The purpose of this study was to evaluate the ability of uncrossmatched transfusions in the emergency department (ED) to predict early (<6 hr) massive transfusion (MT) of red blood cells (RBCs) and blood Components. STUDY DESIGN AND METHODS: All patients admitted to a Level 1 trauma center between July 2005 and June 2007 who received any transfusions and were transported directly from the scene of injury were included. Early MT was defined as the need for 10 U or more or RBCs in the first 6 hours. Early MT plasma was defined as 6 U or more of plasma in the first 6 hours. Early MT platelets (PLTs) were defined as two or more apheresis transfusions in the first 6 hours. Univariate and multivariate analyses were performed. RESULTS: A total of 485 patients (34%) received ED transfusions (ED RBC+) and 956 (66%) did not receive ED transfusions (ED RBC–). ED RBC+ patients were younger, were more likely to be male, and arrived with more severe injuries. Multivariate regression identified ED transfusion of uncrossmatched RBC as an independent predictor of requiring early MT of RBCs (odds ratio [OR], 3.5; 95% confidence interval [CI], 1.36-7.59; p = 0.001), plasma (OR, 2.7; 95% CI, 1.66-4.39; p < 0.001), and PLTs (OR, 1.9; 95% CI, 1.08-3.41; p = 0.025). CONCLUSION: Patients receiving uncrossmatched RBCs in the ED are more than three times more likely to receive early MT of RBCs. Additionally, patients transfused with ED RBCs are more likely to receive 6 units or more of plasma and two or more apheresis PLT transfusions. Given these findings, ED transfusion of uncrossmatched RBCs should be considered a potential trigger for activation of an institution's MT protocol.
Ming Liang - One of the best experts on this subject based on the ideXlab platform.
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time frequency analysis based on vold kalman filter and higher order energy separation for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions
Renewable Energy, 2016Co-Authors: Zhipeng Feng, Sifeng Qin, Ming LiangAbstract:Abstract Planetary gearbox fault diagnosis under nonstationary conditions is important for many engineering applications in general and for wind turbines in particular because of their time-varying operating conditions. This paper focuses on the identification of time-varying characteristic frequencies from complex nonstationary vibration signals for fault diagnosis of wind turbines under nonstationary conditions. We propose a time–frequency analysis method based on the Vold-Kalman filter and higher order energy separation (HOES) to extract fault symptoms. The Vold-Kalman filter is improved such that it is encoders/tachometers-free. It can decompose an arbitrarily complex signal into mono-Components without resorting to speed inputs, thus satisfying the mono-Component Requirement by the HOES algorithm. The HOES is then used to accurately estimate the instantaneous frequency because of its high adaptability to local signal changes. The derived time–frequency distribution features fine resolution without cross-term interferences and thus facilitates extracting time-varying frequency Components from highly complex and nonstationary signals. The method is illustrated and validated by analyzing simulated and experimental signals of a planetary gearbox in a wind turbine test rig under nonstationary running conditions. The results have shown that the method is effective in detecting both distributed (wear on every tooth) and localized (chipping on one tooth) gear faults.
Zhiliang Liu - One of the best experts on this subject based on the ideXlab platform.
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joint amplitude and frequency demodulation analysis based on local mean decomposition for fault diagnosis of planetary gearboxes
Mechanical Systems and Signal Processing, 2013Co-Authors: Zhipeng Feng, Ming J Zuo, Tao Tian, Zhiliang LiuAbstract:Abstract The vibration signals of faulty planetary gearboxes have complicated spectral structures due to the amplitude modulation and frequency modulation (AMFM) nature of gear damage induced vibration and the additional multiplicative amplitude modulation (AM) effect caused by the time-varying vibration transfer paths (for local gear damage case) and the passing planets (for distributed gear damage case). The spectral complexity leads to the difficulty in fault diagnosis of planetary gearboxes. Observing that both the amplitude envelope and the instantaneous frequency of planetary gearbox vibration signals are associated with the characteristic frequency of the faulty gear, a joint amplitude and frequency demodulation method is proposed for fault diagnosis of planetary gearboxes. In order to satisfy the mono-Component Requirement by instantaneous frequency estimation, a signal is firstly decomposed into product functions (PF) using the local mean decomposition (LMD) method. Then, the earliest extracted PF that has an instantaneous frequency fluctuating around the gear meshing frequency or its harmonics is chosen for further analysis, because it contains most of the information about the gear fault. The amplitude demodulation analysis can be accomplished through Fourier transforming the amplitude envelope of the chosen PF. For the frequency demodulation analysis, Fourier transform is applied to the estimated instantaneous frequency of the chosen PF to reveal its fluctuating frequency, thus obtaining the spectrum of the instantaneous frequency. By joint application of the amplitude and frequency demodulation methods, planetary gearbox faults can be diagnosed by matching the dominant peaks in the envelope spectrum and the spectrum of instantaneous frequency with the theoretical characteristic frequencies of faulty gears. The performance of the proposed method is illustrated by simulated signal analysis, and is validated by experimental signal analysis of a lab planetary gearbox with intentionally created pitting and naturally developed wear.