| Autor: |
Xiaowei Xu, Jizhou Lai, Pin Lv, Junqing Lu, Shiyu Bai, Huafeng Hu |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 9, Pp 151198-151208 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2021.3125501 |
| Popis: |
The high-precision positioning of the shearer is the key technology to realize the automation of longwall mining. Since mine is a Global Position System (GPS)-denied environment, highly autonomous Inertial Navigation System (INS)/odometer integrated navigation has been widely used. At present, the shearer positioning method based on INS/odometer has been challenging to meet the requirements of long-time and high-precision mining. Aiming at the high-precision navigation in the complex mining environment, this paper constructs a comprehensive rail kinematics model of the shearer that does not rely on external sensors. By analyzing the kinematic characteristics of the shearer and the scraper conveyor during the longwall mining process, a method of information fusion and navigation system fault diagnosis based on the assistance of the shearer rail kinematics model was proposed. According to the working principle of the shearer rails and hydraulic supports, the characteristics of the trajectory deviation caused by the sensor fault of the hydraulic support are analyzed. Combined with the engineering requirements of shearer mining, the model fault identification was carried out by the fading probability ratio detection algorithm. The simulation results show that the proposed algorithm effectively improves the positioning of shearer accuracy in multiple cutting cycles. At the same time, it avoids the influence of the rail deviation caused by the rail kinematics model fault on the positioning of the shearer. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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