Network Monitoring Method for Scraper Posture of Mine Scraper Conveyor via Frictional Piezoelectric Effect and Triangular Constrained Filtering

The scraper conveyor is the core equipment to underground coal transportation, its health condition is related to production efficiency and personnel safety. Using wireless sensor network (WSN) technology to monitor scraper posture can determine whether there are faults such as chain jamming or breakage in the scraper conveyor. However, the harsh working conditions and dynamic characteristics of scrapers pose challenges for continuous and accurate monitoring through monitoring nodes with limited energy. Thus, we utilize WSN technology to first design a node friction piezoelectric energy harvester (FPEH) adapted to the motion characteristics of scraper, to ensure the sustainability of node operation and obtain large amount of monitoring data. Then, to reduce the impact of noise on the accuracy of monitoring data under harsh underground conditions, a fusion method of triangle constrained average interpolation and adaptive extended Kalman filtering (TCAI-AEKF) is proposed based on the constraint relationship between adjacent scrapers. The experimental results indicate that when mutually attractive magnets and piezoelectric beam are vertically arranged, the FPEH can achieve optimal power generation performance. Compared with the AEKF method, the proposed TCAI-AEKF method has better filtering performance, can identify and update erroneous values in measured results, and improve the credibility of actual values.