Gearbox Deterioration Detection under Steady State,Variable Load, and Variable Speed Conditions  

作　　者：SHAO Yimin CHRIS K Mechefske OU Jiafu HU Yumei 

机构地区：[1]State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China [2]Department of Mechanical and Materials Engineering, Queen＇s University, Canada [3]China Chongqing Automobile Research Institute, Chongqing 400039, China

出　　处：《Chinese Journal of Mechanical Engineering》2009年第2期256-264,共9页中国机械工程学报（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant No. 50675232);Key Project of Ministry of Education of China;Chongqing Municipal Natural Science Key Foundation of China (Grant No. 2007BA6021)

摘　　要：Multiple dominant gear meshing frequencies are present in the vibration signals collected from gearboxes and the conventional spiky features that represent initial gear fault conditions are usually difficult to detect. In order to solve this problem, we propose a new gearbox deterioration detection technique based on autoregressive modeling and hypothesis testing in this paper. A stationary autoregressive model was built by using a normal vibration signal from each shaft. The established autoregressive model was then applied to process fault signals from each shaft of a two-stage gearbox. What this paper investigated is a combined technique which unites a time-varying autoregressive model and a two sample Kolmogorov-Smimov goodness-of-fit test, to detect the deterioration of gearing system with simultaneously variable shaft speed and variable load. The time-varying autoregressive model residuals representing both healthy and faulty gear conditions were compared with the original healthy time-synchronous average signals. Compared with the traditional kurtosis statistic, this technique for gearbox deterioration detection has shown significant advantages in highlighting the presence of incipient gear fault in all different speed shafts involved in the meshing motion under variable conditions.Multiple dominant gear meshing frequencies are present in the vibration signals collected from gearboxes and the conventional spiky features that represent initial gear fault conditions are usually difficult to detect. In order to solve this problem, we propose a new gearbox deterioration detection technique based on autoregressive modeling and hypothesis testing in this paper. A stationary autoregressive model was built by using a normal vibration signal from each shaft. The established autoregressive model was then applied to process fault signals from each shaft of a two-stage gearbox. What this paper investigated is a combined technique which unites a time-varying autoregressive model and a two sample Kolmogorov-Smimov goodness-of-fit test, to detect the deterioration of gearing system with simultaneously variable shaft speed and variable load. The time-varying autoregressive model residuals representing both healthy and faulty gear conditions were compared with the original healthy time-synchronous average signals. Compared with the traditional kurtosis statistic, this technique for gearbox deterioration detection has shown significant advantages in highlighting the presence of incipient gear fault in all different speed shafts involved in the meshing motion under variable conditions.

关 键 词：GEARBOX condition detection hypothesis test time-varying autoregressive（AR） modeling Kolmogorov-Smimov goodness-of-fit test 

分 类 号：U463.212[机械工程—车辆工程] U664.33[交通运输工程—载运工具运用工程]