Research on fault diagnosis of TBM main bearing based on improved BP neural network

Abstract

Main bearing that plays the role of supporting and making the cutter to rotate and tunnel is the core part of TBM. Because of the harsh working conditions and complex changeful construction, the axial and radiai load and environmental factors such as temperature of TBM main bearing are changing to make the fault of main bearing presenting randomness, and then the not easy identified fault may be produced. The traditional neural network model can not dynamic consider the cause of the reasons, parts and types, BP neural network fault diagnosis model based on fault reasons-signs matrix is presented in this papero Firstly, the faults are screened through the fault reasons-symptom matrix, and the neural network structure is designed according to the screening result, then, the fault type is identified by way of model training. According to TBM main bearing fault symptoms data provided by a heavy enterprises practical engineering and MATLAB simulation validation, the feasibility and superiority of this model method are proved.

High-Speed 3D Printing Temperature Control with Optimized PID Parameters Based on Improved ALO Algorithm

“…Li [10] and others developed a Gaussianmutation based ALO (GALO) algorithm and applied it to the 0-1 backpack problem. In order to accurately and efficiently diagnose the health state of rolling bearings, Wang [11] proposed a rolling bearing fault diagnosis model based on Improved Antli-on Optimization (IALO) algorithm to optimize BP neural network. Jie Ji [12] et al in-vestigated the equipment capacity allocation of a chemical company's cogeneration system for cooling, heating and power generation based on the improvement of ant-lion optimization algorithm by increasing the weight coefficients(WALO).…”