Research On Fault Diagnosis Of Automobile Seat Gearbox Based On Adaptive Multicomponent Signal Analysis

The horizontal driving machine(HDM)of car seat is the key part to control the horizontal movement of the seat,and the main part of the HDM is a small worm and worm gear reducer,which is driven by a seat horizontal motor.It is traditionally the use of artificial sounds to detect HDM failures.The labor intensity is very large and the accuracy of detection depends entirely on the subjective experience of human.Therefore,it has great limitations.Besides,seat motors are also developing towards small-sized motors.Four stage and six stage seat motors have appeared successively.However,compared with the two stage motors,the vibration and noise are greater,and the noise of HDM is drowned.The method of fault diagnosis based on HDM vibration signal is discussed and studied,and the main research contents and results are as follows:1.According to the structural characteristics of HDM,a vibration test platform for HDM is designed.Vibration signals are collected by using B&K's vibration sensor and NI's data acquisition card,and a system platform for acquisition,storage and analysis of HDM vibration signals is built by using LabVIEW.2.By studying the existing trend extraction methods and introducing the concept of trend in the time domain to the frequency domain,the concept of spectrum trend is proposed.The frequency spectrum of signal is extracted by empirical mode decomposition(EMD)algorithm.The trend is more adaptive than the traditional moving average method.3.EWT and VMD are two new adaptive multicomponent signal analysis algorithms,but all of the two algorithms need to preset the number of mode.For this problem,EWT and VMD algorithm is improved by the spectrum trend.The simulation and experimental data analysis show that the improved algorithm can effectively deal with the HDM vibration signal and accurately diagnose the gear fault of HDM.