关键词: 提升机轴承, 神经网络, 故障诊断, 自编码, 分类

Abstract: In order to diagnose the fault of hoisting bearings for the mine system. This paper proposes a multi-level diagnosis system based on deep learning to dynamically identify the type of fault and the degree of hoisting bearing degradation. Firstly, the sliding window overlapping sampling is used for realizing data enhancement in the proposed system, then the data noise is reduced based on auto-encoder. Through the training of deep neural networks, the failure mode and the degree of failure are identified by classification. Finally, in order to further improve the accuracy, this paper adopts the voting method of integrated learning. The experimental results show that the accuracy of system is higher than SVM and BPNN algorithms. Therefore, the system can identify the type and degree of hoisting bearing fault.

Key words: hoisting bearing, neural network, fault diagnosis, auto-encoder, classification