Rolling bearing is a key component of mechanical equipment.The instability of its vibration signal and the limitation of single domain features increase the difficulty of bearing fault diagnosis in some extent.On this basis,a bearing fault diagnosis technology based on deep learning and multi-domain decision fusion was proposed.The S transform and recurrence plot transform were used to extend the vibration signal from one-dimensional time domain to two-dimensional time-frequency domain and spatial domain.Then,to adapt the diagnosis model to the lack of fault data,a micro-convolutional neural network with better generalization ability and adaptability was built to learn and extract multi-domain features of the signal,and the network parameters were as low as 6 orders of magnitude,which could be trained and classify fault data efficiently.Finally,D-S evidence theory was introduced to fuse the sin-gle domain diagnosis results.The proposed method achieved an average diagnostic accuracy of 99.84％for nine types of bearing faults in the Case Western Reserve University dataset.
关键词
滚动轴承/微型卷积神经网络/多域融合/故障诊断
Key words
rolling bearing/micro-convolutional neural network/multi-domain fusion/fault diagnosis
维普
万方数据