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Study on multi-fractal fault diagnosis based on EMD fusion in hydraulic engineering

机译：基于EMD融合的水力工程多分形故障诊断研究

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The vibration signal analysis of the hydraulic turbine unit aims at extracting the characteristic information of the unit vibration. The effective signal processing and information extraction are the key to state monitoring and fault diagnosis of the hydraulic turbine unit. In this paper, the vibration fault diagnosis model is established, which combines EMD, multi-fractal spectrum and modified BP neural network; the vibration signal waveform is identified and purified with EMD to obtain approximation coefficient of various fault signals; the characteristic vector of the vibration fault is acquired With the multi-fractal spectrum algorithm, which is classified and identified as input vector of BP neural network. The signal characteristics are extracted through, the waveform, the diagnosis and identification are carried out in combination of the multi-fractal spectrum to provide a new method for fault diagnosis of the hydraulic turbine unit. After the application test, the results show that the method can improve the intelligence and humanization of diagnosis, enhance the man-machine interaction, and produce satisfactory identification result. (C) 2016 Elsevier Ltd. All rights reserved.
机译：水轮机单元的振动信号分析旨在提取单元振动的特征信息。有效的信号处理和信息提取是水轮机状态监测和故障诊断的关键。本文建立了结合EMD，多重分形谱和改进的BP神经网络的振动故障诊断模型。识别振动信号波形，并用EMD进行净化，得到各种故障信号的近似系数。利用多重分形谱算法获取振动故障的特征向量，将其分类识别为BP神经网络的输入向量。通过提取信号特征，结合多重分形谱进行波形，诊断和识别，为水轮机机组的故障诊断提供了一种新的方法。经过应用测试，结果表明该方法可以提高诊断的智能性和人性化，增强人机交互性，并产生令人满意的识别结果。 （C）2016 Elsevier Ltd.保留所有权利。

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