No abstract

No abstract

No abstract

The load spectrum is a crucial factor for assessing the fatigue reliability of in-service rolling element bearings in transmission systems. For a bearing in a high-speed train gearbox, a measurement technique based on strain detection of bearing outer ring was used to instrument the bearing and determine the time histories of the distributed load in the bearing under different gear meshing conditions. Accordingly, the load spectrum of the total radial load carried by the bearing was compiled. The mean value and class interval of the obtained load spectrum were found to vary non-monotonously with the speed and torque of gear meshing, which was considered to be caused by the vibration of the shaft and the bearing cage. As the realistic service load input of bearing life assessment, the measured load spectrum under different gear meshing conditions can be used to predict gearbox bearing life realistically based on the damage-equivalent principle and actual operating conditions.

No abstract

No abstract

No abstract

Intelligent bearing fault diagnosis has received much research attention in the field of rotary machinery systems where miscellaneous deep learning methods are generally applied. Among these methods, convolution neural network is particularly powerful because of its ability to learn fruitful features from the original data. However, normal convolutions cannot fully utilize the information along the data flow while the features are being abstracted in deeper layers. To address this problem, a new supervised learning model is proposed for small sample size bearing fault diagnosis with consideration of imbalanced data. This model, which is developed based on a convolution neural network, has a high generalization ability, and its performance is verified by conducting two experiments that use data collected from a self-made bearing test rig. The proposed model demonstrates a favorable performance and is more effective and robust than other deep learning methods.