http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Fatigue life prediction of rear axle using time series model
Yimin SHAO,Jieping FANG,Liang Ge,Jiafu OU,Hao JU,Ying MA 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
The rear axle is one of the key parts of the automobile, lots failure of rear axle resulted from fatigue failure of the spiral bevel gears. A new method is proposed to solve the problem of accurately predicting the fatigue life of spiral bevel gears in rear axle. The method uses the recurrence tracing and difference method to improve the Autoregressive Moving Average (ARMA) model prediction accuracy, which uses variables determined from on-line measurements to characterize the state of the deterioration rear axle. The experimental results show the proposed method has relatively high prediction accuracy.
Fault Diagnosis System Based on Smart Bearing
Yimin SHAO,Liang GE,Jieping FANG 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
According to statistics, a lot of the rotating machinery faults are caused by the bearings, so the smart bearing technique is important for ensuring safety of them. For the smart bearing, a representative definition is that sensing devices of the different use are integrated into the traditional bearing in order to realize self-diagnosis. For condition of the variable speed, variable load and heavy load, the diagnostic technology cannot satisfy requirement of the fault feature extraction at present. This paper presents a new smart bearing of the multi-parameters including of two vibration acceleration sensing devices, two speed sensing devices, and two temperature sensing devices. In addition, the heavy noise can be decreased for extraction of the weak fault signals by the embedded integrated mode of bearing and sensor.