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Study on Gearbox Fault Diagnosis Based on Particle Swarm Optimization and System Performance

Author: SunLiMing
Tutor: HuangJinYingï¼›PanHongXia
School: University of North
Course: Pattern Recognition and Intelligent Systems
Keywords: Gearbox Fault Diagnosis ARX ??model Particle Swarm Optimization RBF Neural Network
CLC: TH165.3
Type: Master's thesis
Year: 2010
Downloads: 105
Quote: 4
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Abstract


The subject comes from the National Natural Science Foundation the complex gear early fault diagnosis based on particle swarm optimization and filtering technique \diagnostic research of new technologies \The presence of bearings and gears of the gearbox as the the mechanical equipment most commonly used power transmission member , in the course of long - term operation due to manufacturing errors , the factors impact load , and the work environment as well as fatigue, aging , and other effects will inevitably some failures . Difficult for vibration response signal feature extraction and vibration signal will be subject to the fluctuations of the input shaft speed torque proposed based on the input shaft speed torque signal and output vibration response signals to establish the ARX timing model to characterize the different working through the ARX model gearbox state system characteristics , the analysis of the state of the gear box from the point of view of the system characteristics and fault diagnosis . This paper carried out the experimental research work mainly include : tick points and smooth handling of speed fluctuation signal , zero mean , filtering , resampling , outliers removed , and EMD decomposition processing ; treated input - output data set of the vibration response signals to establish a the gearbox system of ARX model; extraction model since part of the regression coefficients and model residuals as model characteristics ; model the characteristics of the time domain and frequency domain characteristics of the model of the amplitude and frequency response curves sixteen statistical characteristic parameters as model characteristics , constitute the complete feature set of this article gearbox fault diagnosis ; establish the best features of the sub- set of discrete particle swarm optimization algorithm experimental model using MATLAB programming , RBF neural network classification results as the objective function , select the state classification results , so that the fault highest diagnostic accuracy . The above research to form a complete gearbox fault diagnosis method . Measured experimental signal effectively ARX model , feature extraction , and achieve fault diagnosis , pattern recognition, effectively improve the diagnostic accuracy of fault diagnosis , and have important significance in promoting the development of the gearbox fault diagnosis technology in the optimization process .

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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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