Fault Diagnosis Of Vibration For Hydro-power Unit Based On Empirical Mode Decompositio And Neural Network

Electric energy is the main energy nowadays,and power demand is growing in China.As the second largest power generation mode in China,hydro-power conforms with the sustainable development of our country by its characteristics of clean and low cost.Hydro-power unit is the key equipment of hydro-power station,and ensuring its safe and stable operation is very important to the the efficiency of power station and even the stability of the entire power system.However,there are some shortcomings of the tradition maintenance mode of hydroelectric generating units,so the new way of condition-based maintenance is attracting more and more attention.Condition-based maintenance needs the test signal to judge the running state of the hydroelectric unit.Therefore,it is a hot spot to do some researches on the fault diagnosis of the hydroelectric unit,especially on the vibration fault diagnosis.Due to the large amount of background noise in the acquisition process of vibration signals,we need to reduce the noise of vibration signals.The noise signals usually have high frequency and low energy.The empirical mode decomposition can reduce the noise effectively.The occurrence of the fault makes the vibration signal of the hydroelectric unit contain a lot of complex frequency components.After the empirical mode decomposition,the fault features are hidden in the intrinsic functions.The fault features can be extracted with some non-dimensional symptom parameters.The hydroelectric generating unit is a rather complex system,and its faults and characteristics have a nonlinear mapping.After training,neural network can simulate the nonlinear mapping relationship,so it can judge the running state of hydro-power units effectively.BP neural network is relatively mature and widely applied in the field of rotating machinery fault diagnosis.The main work of this paper are as follows:(1)Introduce the working principle and structure of the hydraulic turbine.Analyze the common fault types and the reasons in hydroelectric generating units.(2)Introduce the principle and characteristics of empirical mode decomposition.Point out the problems of empirical mode noise reduction.Propose an empirical mode threshold denoising method by simulating wavelet threshold denoising.Test the effect of this method with experiment.(3)Analyze the negative effects of endpoint effect and modal aliasing on the feature extraction.Point out the mirror extending method can restrain endpoint effect and ensemble empirical mode decomposition can restrain modal aliasing.Test the effect of two methods with experiment.(4)Select 6 non-dimensional symptom parameters which are sensitive to faults through the practical operation of feature extraction as fault features.(5)Introduce neural network theory and improved training algorithms of BP neural network.Obtain the final judgement of hydro-power unit with this method.