Abstract: In tunnel construction with tunnel boring machines (TBMs), accurate knowledge of disc-cutter failure states is crucial to ensure efficient operation and prevent delays and cost overruns. This study investigates the influence of disc-cutter partial wear on tunneling parameters and proposes a novel method for discriminating partial-wear ratio based on a stacking ensemble model. The time-domain features of torque and thrust, including the average value and standard deviation, are analyzed through a series of scaled-down experimental tests on partial wear. Torque and thrust values will increase when a disc cutter is trapped and partially worn. The impact of partial-wear ratio on tunneling parameters appears to be more significant than partial-wear depth. A total of 40 features are selected from the time domain, frequency domain, and time-frequency domain to describe the torque and thrust. The relationships between these features and the partial-wear ratio are analyzed using the Pearson coefficient and Copula entropy. The results reveal that, except for the form factor in the time-domain features, the remaining features exhibit certain linear or non-linear correlations with the partial-wear ratio. Lastly, the proposed model successfully achieves the discrimination of the partial-wear ratio and outperforms other commonly used models in terms of overall classification accuracy and differentiation capability in different categories. This research provides effective support for monitoring and health management of disc-cutter failure states.

Key words: Tunnel boring machine (TBM); Disc cutter; Partial wear; Tunneling parameters; Multi-domain features; Ensemble learning

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