In view of the fact that the mature eddy current sensors on the market have a small measuring range and a certain requirement for the measured conductor area, which cannot meet the requirement of TBM disc cutter Wear monitoring, Ansoft Maxwell simulation was used to optimize the coil geometry parameters and circuit structure of eddy current sensor in this study. A special sensor for measuring disc cutter wear was designed, and the feasibility of the design was verified by measuring a 17-inch disc cutter. The results indicate that the linear range of the eddy current sensor is 35 mm under the condition that the measured blade width is 19 mm, which can meet the requirement of disc cutter wear measurement and provide monitoring data for real-time disc cutter wear monitoring system.

Modern recommender systems have achieved remarkable success in capturing user interests. However, their heavy reliance on correlations within observational data renders them susceptible to confounding biases, such as item popularity and exposure effects. These biases cause the model to learn spurious correlations between user behaviors and item attributes, thereby compromising fairness, novelty, and the ability to generalize to users' true preferences. To address this fundamental challenge, we propose a novel debiasing framework, Causal-Guided Disentanglement for Debiasing Recommendation (CGDR), which causally separates latent preference factors from confounding effects at the representation level. We first construct a Structural Causal Model (SCM) to explicitly delineate the generative process of user-item interactions, postulating that a user's action is determined by both their intrinsic preference and a set of confounding factors. Guided by this causal graph, our framework employs a novel Causal-Guided mechanism that leverages auxiliary supervisory tasks to provide the disentanglement process with a clear causal direction. This decomposes the latent representation into two independent components: one capturing robust, transferable true preferences, and the other explicitly modeling dynamic confounding effects. Upon achieving this disentanglement, we generate debiased recommendations via Counterfactual Reasoning, performing interventions on the confounder representation to simulate a bias-free environment while leveraging the preference representation for prediction. Extensive experiments on multiple real-world datasets demonstrate that our proposed model not only significantly outperforms state-of-the-art baselines in recommendation accuracy, particularly in enhancing long-tail item performance, but also exhibits superior robustness and interpretability.