HOC Based Blind Identification of Hydroturbine Shaft Volterra System

Bai, Bing; Zhang, Lixiang

doi:10.1155/2017/6732704

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…e Volterra series can explain the nonlinear components of the frequency domain output of system. Bai and Zhang [17] proposed a blind identification method based on third-order cumulant and an inverse recursive method and used this method to identify the simplified secondary Volterra system of hydraulic turbine shafting. Tang et al [18] used the basic theory of Volterra series to model the rotating machinery and diagnose the rotating machinery.…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Feature Extraction Method Based on NOFRFs and Its Application in Rotor Faults

Liu

Zhao

et al. 2019

Shock and Vibration

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Rub-impact between the rotating and static parts is a more common fault. The occurrence of faults is often accompanied by the generation of nonlinear phenomena. However, it is difficult to find out because the nonlinear characteristics are not obvious at the beginning of the fault. As a new frequency domain-based method, nonlinear output frequency response functions (NOFRFs) use the vibration response to extract the nonlinear characteristics of the system. This method has a better recognition rate for fault detection. Also, it has been applied in structural damages detection, but the high-order NOFRFs have the characteristics that the signals are weak and the features are difficult to extract. On this basis, the concept of the weighted contribution rate of the NOFRFs is proposed in this paper. The variable weighted coefficients with orders are used to amplify the influence of high-order NOFRFs on the nonlinearity of the system so as to extract its fault characteristics. The new index RI is proposed based on Clenshaw–Curtis quadrature formula to eliminate the effect of artificially selected weighted coefficients on sensitivity. Especially in the early stage of the fault, the new index varies greatly with the deepening of the fault. Both simulation and experimental results verify the validity and practicability of the new index. The new index has certain guiding significance in the detection of mechanical system faults.

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Section: Introductionmentioning

confidence: 99%

Research on Fault Feature Extraction Method Based on NOFRFs and Its Application in Rotor Faults

Liu

Zhao

et al. 2019

Shock and Vibration

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Mixed third- and fourth-order cumulants-based algorithm for nonlinear kernels identification in cubic systems

Zidane¹,

Dinis

2021

SIViP

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Modeling and Identification of Nonlinear Effects in Massive MIMO Systems Using a Fifth-Order Cumulants-Based Blind Approach

Zidane¹,

Dinis

2022

Applied Sciences

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Pre-processing associated with massive multiple input-multiple output (MIMO) systems can lead to signals with high envelope fluctuations, which are very prone to nonlinear effects, especially when massive MIMO schemes are combined with orthogonal transform multiplexing (OFDM) modulations. If the nonlinear characteristics that affect a given system are known, we can design appropriate receivers that take into account the nonlinear effects introduced by the transmitter. Cubic systems are particularly important, not only because they can approximate many nonlinear effects (e.g., due to the power amplifier or clipping effects), but also because many more complex nonlinear characteristics in communication schemes can be replaced by equivalent lower-order nonlinear characteristics in general, and cubic characteristics in particular. To compensate the effects at the receiver side (e.g., by using the so-called Bussgang receivers), we need to estimate the nonlinear operation that was introduced at the transmitter, and this should be done blindly, without the need of training symbols. The paper contains a description of a mathematical approach for modeling and identification of nonlinear kernels in cubic systems. Based on theoretical tools of HOC in cubic systems, we build a new formula which relates the second- and fifth-order cumulants. Our performance results indicate that the proposed approach allows an accurate identification, yielding the desired kernels via fifth-order cumulants, and ensures a very good convergence, outperforming existing adaptive methods. This is achieved blindly, by exploiting the maximum information of the output system, making it suitable for many practical nonlinear effects.

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HOC Based Blind Identification of Hydroturbine Shaft Volterra System

Cited by 3 publications

References 22 publications

Research on Fault Feature Extraction Method Based on NOFRFs and Its Application in Rotor Faults

Research on Fault Feature Extraction Method Based on NOFRFs and Its Application in Rotor Faults

Mixed third- and fourth-order cumulants-based algorithm for nonlinear kernels identification in cubic systems

Modeling and Identification of Nonlinear Effects in Massive MIMO Systems Using a Fifth-Order Cumulants-Based Blind Approach

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