Virtual-real mapping for the scraper conveyor S-shaped bending in a fully mechanized mining face production system based on digital twin

The human-centered paradigm of intelligent manufacturing is reshaping traditional production models. The fully mechanized coal mining face production system (FMCMFPS) represents a typical application of complex manufacturing systems (MS) in the coal mining industry, with an increased emphasis on the safety of personnel within the system. Among its key components, the real-time monitoring of the scraper conveyor S-shaped bending is critical to ensuring safe and efficient coal mining operations. This monitoring also aligns with the principles of Industry 5.0, which emphasize human-centric, safe and intelligent manufacturing practices. However, the underground environment is harsh, with moisture, dust and high vibrations. These conditions, along with the structure of the scraper conveyor, complicate the accurate capture of data. Issues such as data misalignment, missing information and frame retraction errors lead to inconsistencies between sensor data and actual conditions. To tackle these challenges, the paper presents a virtual-real mapping method for the scraper conveyor S-shaped bending using digital twin technology. A framework and mechanistic model for the S-shaped bending are established, and techniques like the Interquartile Range (IQR), sliding window and filtering algorithms are used to handle anomalous data. The proposed approach is validated through a prototype system at the Ulanmulun Mine in Inner Mongolia.