Digital twin-driven intelligent fault diagnosis technology for crushers

Abstract In the open-pit coal mine operations involving semi-continuous or continuous mining, the coal crusher station plays a pivotal role. Its operational status directly impacts the continuity and stability of the entire production process. To achieve real-time monitoring for fault diagnosis and maintenance of the crusher station, this study constructs a fault repository for the crushing system, encompassing 13 typical vibration faults and two types of electrical signal faults. Based on the digital twin theory, a high-fidelity virtual model is established, accurately replicating the signal characteristics of the real system. The model is dynamically calibrated using differential evolution algorithms to adjust critical fault parameters in real-time, ensuring consistency between the virtual model and the physical entity. Furthermore, combining multi-head attention mechanisms with one-dimensional convolutional neural networks, the proposed approach extracts features from the virtual model’s output signals and performs fault classification diagnosis. Extensive experimental validations demonstrate that the proposed scheme offers significant advantages in terms of diagnostic accuracy and real-time performance (accuracy exceeding 97%, adjustment time less than 1 s), providing a reliable technical support for the intelligent maintenance of open-pit coal mine crushing systems.