Shield machine is a complex large-scale tunneling equipment with multiple systems and driving sources. In order to improve the accuracy and efficiency of fault diagnosis for shield machine, a method based on the combination of reverse feature elimination (RFE) and extreme learning machine (ELM) is proposed. For the characteristics of shield machine operation data with many dimensions and large quantity, the RFE method is introduced to reduce the dimension of data, eliminate the redundant dimension and remove the correlation between features. Considering the neural network has the slow speed and low efficiency of fault diagnosis, the ELM neural network classifier model is built based on the extremely learning mechanism for fault diagnosis of shield machine. The simulation results based on the field construction data show that this method improves the accuracy and efficiency of fault diagnosis of shield machine significantly and has good engineering application value.
Research
Fault diagnosis of shield machine based on RFE and ELM
https://doi.org/10.21203/rs.3.rs-20661/v1
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shield machine
fault diagnosis
extremely learning machine
reverse feature elimination
Table 1 fault classification of shield machine
|
Main fault type |
Type of secondary fault |
|
Cutter head system hob wear fault |
NO.1,5,3,7 cutter head motor torque is too high NO.2,6,4,8 cutter head motor torque is too high |
|
Propulsion system shield shell stuck |
NO.1,3, jack speed is too high NO.2,4, jack speed is too high |
|
Grouting system grouting pipe blocking failure |
grouting A liquid injection flow is too low grouting B liquid injection flow is too low |
Table 2 Comparison of fault diagnosis results of four different algorithms for shield machine
