Probabilistic Life Prediction of Tunnel Boring Machine under Wearing Conditions with Incomplete Information

Abstract

This paper developed a data analysis approach to estimate the probabilistic life of an earth pressure balance (EPB) tunnel boring machine (TBM) under wearing conditions with incomplete information. The marginal reliability function of each system component of TBM is derived based on data collected from the site. The structure of the failure framework was determined based on the evaluation of influencing factors, including the wearing of the cutter head panel and screw conveyor. The joint distribution model was built by utilizing the best-fit copula function and the remaining reliable mining distance can be predicted from this model. Real data of the remaining thickness of the wearing resistance structure of the cutter head panel and screw conveyor from an earth pressure balance (EPB) TBM were captured. A realistic metro tunneling project in China was utilized to examine the applicability and effectiveness of the developed approach. The results indicate that: (1) With the selection of normal distribution and Gumbel copula as the best-fit marginal distribution function and copula function, the reliable mining distance was predicted as 4.0834 km when the reliability equaled 0.2. (2) The copula function was necessary to be considered to assess the joint distribution of the reliability function, as the predicted mining distance reduces significantly to 3.9970 km if assumed independent. (3) It enables the user to identify the weak component in the machinery and significantly improve the reliable mining distance to 4.5075 km by increasing the initial thickness of the screw conveyor by 0.5 mm. This approach can be implemented to minimize the risk of unintended TBM breakdown and improve the tunneling efficiency by reducing unnecessary cutter head intervention during the mining process.