In this paper, a new dynamic model was proposed for identifying the rock hardness during the process of roadway tunnelling, thereby regulating the speed of the driving motor and the torque of the cutting head. The presented identification model establishes a multi-information feature database containing vibration signals in the y-axis, acoustic emission signals, cutting current signals, and temperature signals. Subsequently, we obtain the membership functions (MFs) of the given multiple signals with the amount of feature samples according to the principle of minimum fuzzy entropy. Furthermore, a rock hardness identification model was established based on multi-sensor information fusion and Dempster-Shafer (D-S) evidence theory. To prove the accuracy of the proposed model, an identification experiment was carried out through the cutting of a poured mixed rock specimen with five grades of hardness. As a result, the proposed identification model recognizes the rock hardness accurately for fifteen sampling points, which indicates the significance of the method with regard to the dynamic identification of rock hardness during the process of roadway tunnelling, and further provides data support for adjusting the speed of the cutting head adaptively, thereby achieving high efficiency tunnelling.
The paper presents Gupta's relational decomposition technique expanded on linguistic level. It allows to reduce the hardware cost of the fuzzy system or the computing time of the final result, especially when referring to First Aggregation Then Inference (FATI) relational systems or First Inference Then Aggregation (FITA) rule systems. The inference result of the hierarchical system using decomposition technique is more fuzzy than of the classical system. The paper describes a linguistic decomposition technique based on partitioning the knowledge base of the fuzzy inference system. It allows to decrease or even totally remove a redundant fuzziness of the inference result.
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