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Based on particle filter Gearbox Fault Diagnosis

Author: WangXiaoBin
Tutor: ZuoHongWei；PanHongXia
School: University of North
Course: Mechanical Design and Theory
Keywords: Particle filter Troubleshooting ARMA Neural Network
CLC: TH165.3
Type: Master's thesis
Year: 2011
Downloads: 100
Quote: 2
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Abstract


The subject comes from the National Natural Science Fund Project \Gearbox mechanical equipment is the most commonly used transmission device, usually in the long-term load operation , due to manufacturing and assembly errors exist , and fatigue , aging effect exists , gearbox failure will always occur in the work , and its working state is directly related to the operation of the entire device , so its condition monitoring and fault diagnosis significance. Particle filter is a model-based system to solve non-Gaussian estimation problem of nonlinear stochastic effective way . The particle filter is applied to fault diagnosis of gearbox gearbox vibration signals can solve non- Gaussian nonlinear problems . With a particle filter for fault diagnosis system needs to know the state-space model . Article by establishing the gearbox vibration signal ARMA model, ARMA model parameters using the gearbox as state-space model parameters. ARMA model used in the establishment of FPE criterion for model order , then use the least squares method to estimate parameters calculation . Simulation of particle filter noise in the signal state estimation algorithm application , with its collection of laboratory gearbox normal conditions and fault conditions of the vibration acceleration signal processing for noise reduction , noise reduction comparative analysis of the data before and after the eigenvalues , noise reduction characteristics of the data values ??are better than the former . Studied based on particle filter optimization neural network algorithm, the algorithm is established based on the particle filter optimization neural network model . From the noise reduction gearbox vibration signal characteristic parameter extraction , the extracted characteristic parameters with a particle filter optimization neural network fault identification, and achieved satisfactory results. This also proves that the effect of particle filter signal noise reduction is desirable.

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CLC: > Industrial Technology > Machinery and Instrument Industry > Machinery Manufacturing Technology > Flexible manufacturing systems and flexible manufacturing cell > Fault diagnosis and maintenance
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