综采工作面刮板输送机的直线度误差受刮板输送机的轨迹检测误差和液压支架的推移误差的影响，给刮板输送机的状态监测带来了新的挑战。综采工作面刮板输送机直线度实时监测与控制及其获取精确、可靠的位置状态信息对煤矿智能化开采至关重要。为实现综采工作面刮板输送机自动化、智能化、无人化调直并有效地监测刮板输送机的状态，提出了一种基于卡尔曼滤波的刮板输送机位置状态估计方法，结合综采工作面采煤工艺，利用刮板输送机检测轨迹建立刮板输送机调直方法模型，针对传统方法无法实时反映刮板输送机运动状态的难题，以数字孪生技术作为物理世界与数字世界的桥梁，实时精准地反映刮板输送机的位置状态信息，研究综采工作面“三机”的工作特点，利用卡尔曼滤波算法实现对综采工作面刮板输送机直线度的有效监测，并通过改变检测误差与推移误差的正态分布来检验该方法的准确性。试验结果表明，所提出的监测方法能够有效地减小检测误差和推移误差对综采工作面刮板输送机直线度的影响，且在检测误差与推移误差较大时依旧能发挥出色的效果，监测精准率均能提高30%以上，能使综采工作面刮板输送机的直线度误差稳定在一定的范围内，提高刮板输送机直线度的监测精度。

The straightness error of the scraper conveyor in the fully-mechanized mining face is affected by the track detection error of the scraper conveyor and the movement error of the hydraulic support，which brings new challenges to the condition monitoring of the scraper conveyor. The real-time monitoring and control of the straightness of the scraper conveyor in fully-mechanized coal mining face and the acquisition of accurate and reliable position status information are essential for the intelligent mining of coal mines. In order to realize the automation，intelligence，unmanned straightening of the scraper conveyor in fully-mechanized mining face and effectively monitor the status of the scraper conveyor，this paper proposes a position state estimation method of the scraper conveyor based on Kalman filtering. Combined with the coal mining technology of the fully-mechanized coal mining face，a model of the straightening method of the scraper conveyor was established by using the detection track of the scraper conveyor. In view of the problem that the traditional method cannot reflect the movement state of the scraper conveyor in real time，the digital twin technology was used as the physical world and digital technology. The bridge of the world accurately reflects the position status information of the scraper conveyor in real time，studies the working characteristics of the “three machines” in the fully-mechanized mining face，and uses Kalman filter algorithm to effectively monitor the straightness of the scraper conveyor in the fully-mechanized mining face，and test the accuracy of the method by changing the normal distribution of detection error and shift error. The experimental results show that the monitoring method proposed in this paper can effectively reduce the influence of the detection error and the displacement error on the straightness of the scraper conveyor of the fully-mechanized mining face，and can still play an excellent effect when the detection error and the displacement error are large. The accuracy of monitoring can be increased by more than 30%. The method in this paper can make the straightness error of the scraper conveyor in the fully-mechanized mining face stable within a certain range，and improve the monitoring accuracy of the straightness of the scraper conveyor.