A novel method for fault diagnosis of hydro generator based on NOFRFs

Xia, Xin; Zhou, Jing; Li, Chaoshun; Zhu, Weijie

doi:10.1016/j.ijepes.2015.02.022

Cited by 25 publications

(12 citation statements)

References 24 publications

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“…According to the analysis in Section 2.1, only the first four order NOFRFs of the system are considered here, so equation (24) can be expressed as…”

Section: Feature Extraction Based On Weighted Contribution Rate Of Nomentioning

confidence: 99%

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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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“…According to the analysis in Section 2.1, only the first four order NOFRFs of the system are considered here, so equation (24) can be expressed as…”

Section: Feature Extraction Based On Weighted Contribution Rate Of Nomentioning

confidence: 99%

“…is also implies that NOFRFs can be used to analyze the behavior of nonlinear systems. Xia et al [24] proposed a new method for online fault recognition of hydroelectric generators and studied the fault mechanism under different conditions. e results prove that this method is simple and efficient.…”

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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“…At present, the research and application of Volterra kernel have developed rapidly; especially the nonlinear spectrum research based on the generalized frequency response function (GFRF) and the output spectrum response function (OFRF) has been gradually applied to the fields of feature extraction, mechanism analysis, and fault diagnosis [7][8][9][10][11]; however, the complexity and accuracy of Volterra kernel calculation are the key factors to its application. Until now, the calculation of Volterra kernel is still difficult, and the main calculation methods of Volterra kernel are the recursive method and identification method.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Calculation Method of Volterra Time-Domain Kernel Based on Time-Delay Artificial Neural Network

Zhong

Chen

2020

Mathematical Problems in Engineering

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To solve the problems of high complexity and low accuracy in Volterra time-domain kernel calculation of a nonlinear system, this paper proposes an intelligent calculation method of Volterra time-domain kernel by time-delay artificial neural networks (TDANNs) and also designs a root mean square error (RMSE) index to choose the neuron number of the network input layer. Firstly, a three-layer TDANN is designed according to the characteristics of the Volterra model. Secondly, the relationship between parameters of TDANN and Volterra time-domain kernel is analyzed, and then three-order expressions of Volterra time-domain kernel are derived. The calculation of Volterra time-domain kernel is completed by network training. Finally, it is verified by a nonlinear system. Simulation results indicate that compared with traditional methods, the new method has higher accuracy, and it can realize the batch calculation of Volterra kernel, which not only improves the calculation efficiency but also provides accurate data for fault diagnosis based on Volterra kernel in further research work.

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“…The implementation of CBM systems for IMs [6] can reduce these risks, with the goal of detecting machine problems prior to failure [7], and can also help to optimize the schedule of maintenance stops, with the goal of reducing the production losses during the stop. From a broader point of view, CBM systems for IMs can be integrated in maintenance systems for electrical installations, along with CBMs for inverters [8], generators [9], transformers [10][11][12][13], power systems [14], transmission lines [15,16] or microgrids [17,18].…”

Section: Introductionmentioning

confidence: 99%

Induction machine model with space harmonics for fault diagnosis based on the convolution theorem

Sapena-Bañó

Martínez-Román

Puche-Panadero

et al. 2018

International Journal of Electrical Power & Energy Systems

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Condition based maintenance systems (CBM) of induction machines (IMs) require fast and accurate models that can reproduce the fault related harmonics generated by different kinds of faults, in order to help in developing new diagnostic algorithms for detecting the faults at an early stage, to analyse the physical interactions between simultaneous faults of different types, or to train expert systems that can supervise and identify these faults in an autonomous way. To achieve these goals, such models must take into account the space harmonics of the air gap magnetomotive force (MMF) generated by the machine windings under fault conditions, due to the complex interactions between spatial and time harmonics in a faulty machine. One of the most common faults in induction machines is the rotor eccentricity, which can cause significant radial forces and, in extreme cases, produce destructive rotor-stator rub. But the development of a fast, analytic model of the eccentric IM is a challenging task, due to the nonuniformity of the air gap. In this paper, a new method is proposed to obtain such a fast model. This method, which is theoretically justified, enables a fast calculation of the self and mutual inductances of the stator and rotor phases for every rotor position. The proposed method is validated first using a finite elements method (FEM) model, and then, through an experimental test-bed using commercial induction motors with a forced mixed eccentricity fault.

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A novel method for fault diagnosis of hydro generator based on NOFRFs

Cited by 25 publications

References 24 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

An Intelligent Calculation Method of Volterra Time-Domain Kernel Based on Time-Delay Artificial Neural Network

Induction machine model with space harmonics for fault diagnosis based on the convolution theorem

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