| AGV(Automated Guided Vehicle)is the key equipment for horizontal container transshipment in modern container terminals.Main structures of AGV are motor drive,induction motor,power battery and radar module.Working for a long time in the port open-air environment of,AGV faces many environment challenges.Due to manufacturing defects,wear,deformation and corrosion during operation,performance of the AGV motors and other parts will gradually decline,stability and operating efficiency of the AGV will gradually decrease.In serious cases,shutdown accidents may occur.Health state of induction motors directly determines the reliability of AGV.During operation,AGV needs to start and stop frequently to load different container goods.Under such circumstances,AGV motors are working under the complex speed and load time-varying conditions.Aiming at the motor fault diagnosis problem under variable speed and load,this paper established the relationship between the current,vibration signal and the motor health under different working conditions.Contents of this paper are as follows:(1)To identify the motor health condition during the start-up period,a fault diagnosis framework based on STFT spectrum analysis and pre training deep convolution neural network is proposed to effectively diagnose and evaluate of the health state of induction motor during startup period.(2)To identify the winding fault of induction motors.A feature extraction method based on three-phase current mutual information analysis and Concordia current vector time-domain feature analysis is proposed.(3)To identify the motor health condition under the complex condition.A two-stage intelligent fault diagnosis framework based on 1-D CNN and multiple condition support vector machines is proposed(4)To monitor the heath condition of the AGV motors.An intelligent platform including signal acquisition and transmission,human-machine interface,data processing and fault diagnosis algorithm modules is developed. |
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