Abstract
The scraper conveyor is prone to chain jam, chain break and other failures due to long time operation under heavy load. To ensure its reliable operation, in this paper, using wireless sensor network (WSN) technology, a method of posture monitoring for scraper conveyor based on adaptive extended Kalman filter (AEKF) algorithm is proposed to judge whether the scraper conveyor is abnormal. Firstly, the posture monitoring model of scraper conveyor is established, and then an AEKF algorithm is designed. Finally, the performance of the proposed algorithm is verified by simulation. The results show that compared with the traditional extended Kalman filter (EKF) algorithm, the proposed AEKF algorithm is more stable in filtering performance under different environmental noises, and RMSE and MAE are kept below 0.11, which further proves that the proposed method is more suitable for the complex environmental noise conditions with dynamic changes in coal mines, and can meet the accuracy requirements for posture monitoring of scraper conveyors.
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