Coriolis mass flowmeter (CMF) is a kind of flow measurement instrument, which can directly measure the high-precision transient mass flow parameters. Also, the vibration characteristic of the U-shaped measuring tube inside is one of the important factors that determine the measuring accuracy. The pulsating flow through the measuring tube will lead to the motion component except for the main vibration frequency, which will affect the phase difference calculation and reduce the measurement accuracy of the mass flowmeter. This article presents a method to improve the accuracy of U-tube CMF based on variable step-size least-mean-square (LMS) filter and Hilbert transform with interval shifting under pulsating flow. Experimental work was conducted on a dynamic experimental platform of pulsating flow. Experimental results show that the stability and accuracy of the proposed algorithm are better than that of the traditional CMF phase difference calibration method. The mean time difference error is $9.0525~\mu \text{s}$ , and the mean time difference relative error is 5.806%. The calibration effect is more than 88.0934% better than other traditional algorithms. It is verified that it has a good error calibration effect for pulsating flow at various frequencies.

The strapdown inertial navigation system (SINS) can be installed on a shearer and used for monitoring its position. However, under the complex environment of the mechanized mining face, the strong vibration of the shearer may cause large calculation error. First, the dynamic model of a double-drum shearer is built with a force analysis, and the spectrum characteristics of linear vibration and angular vibration for the fuselage are then obtained. Second, the coning error and sculling error compensation models of SINS for the shearer are derived based on vibration characteristics. Meanwhile, according to the factor of the uncompensated model, multi-sample compensation model, and different coal and rock traits and different vibration frequencies of the fuselage, the shearer SINS error compensation property under multiple parameters is researched and analyzed in simulation. Finally, simulations indicate that the SINS error compensation model with the three-sample algorithm and four-sample algorithm can improve the calculating accuracy of the shearer SINS. The coning and sculling errors can be compensated effectively by the shearer error compensation model under many vibration conditions, such as different coal and rock traits and different frequencies of the fuselage.

The accurate measurement of position and orientation for shearers is a key technology in realizing an automated, fully-mechanized, coal mining face. Since Global Positioning System (GPS) signal cannot arrive at the coal mine underground, wireless sensor network positioning system cannot operate stably in the coal mine; thus a strap-down inertial navigation system (SINS) is used to measure the position and orientation of the shearer. Aiming at the problem of the SINS accumulative error, this paper proposes a positioning error correction method based on the motion constraint-aided SINS zero velocity updated (ZUPT) model. First of all, a stationary state detection model of the shearer is built with median filter based on the acceleration and angular rate measured by the SINS. Secondly, the motion of the shearer is analyzed using coal mining technology, then the motion constraint model of the shearer is established. In addition, the alternate action between the motion constraint model and the ZUPT model is analyzed at the process of movement and cessation of the shearer, respectively; hence, the motion constraint-aided SINS ZUPT model is built. Finally, by means of the experimental platform of the SINS for the shearer, the experimental results show that the maximum position error with the positioning model proposed in this paper is 1.6 m in 180 s, and increases by 92.0% and 88.1% compared with the single motion constraint model and single ZUPT model, respectively. It can then restrain the accumulative error of the SINS effectively.

The accurate stable measurement of position for shearer is a key technology in realizing an automated fully-mechanized coal mining face. Using as a base of SINS/UWB integrated positioning system, the signal interference and node damage of UWB often cause invalid integrated positioning system, because of the bad working environment of shearer in the coal mine. Consequently, positioning accuracy of SINS/UWB integrated system decreases quickly, due to the drift error of SINS. Aiming at two invalid styles include partial anchor node failure and all anchor nodes failure of UWB, this paper proposes a stable SINS/UWB integrated positioning system of shearer using the multi-model intelligent switching method based on tightly- coupled integrated model and decision tree fault-tolerant model. First of all, the tightly-coupled integrated model and decision tree fault-tolerant model are built using measured distance and the final position of UWB respectively. Secondly, the multi-model intelligent switching algorithm can be established based on the shearer Markoff movement state and residual threshold judgment of Kalman Filter. Finally, the experimental results show that the multi-model intelligent switching fault-tolerant positioning algorithm can effectively overcome increased positioning error of tightly-coupled model and decision tree model based on these two invalid styles. The maximum position error with the positioning model proposed in this paper is 0.93 m in 327 s, and the positioning accuracy with multi-model intelligent switching model is much greater than that with tightly-coupled integration and the decision tree fault tolerant models.

While drilling, the drilling tool can reach the target layer more quickly and efficiently with the accurate borehole trajectory information. It can reduce the drilling cost. However, in the process of traditional well inclination measurement with drilling, due to the interference form the rotary vibration of drill string, the inclinometer needs to stop drilling to complete the measurement of drill tool attitude. And the sensor is installed far away from the drill, so the measurement error and the risk of well control are increased. In order to solve the problem that the interference of near the bit inclinometer which caused by complex shock vibration and drill string rotation, this paper proposes a method of dynamic measuring inclination angle that based on unscented kalman filter and cross-correlation extraction algorithm (UKF+CC). It is a composite filtering method that combine UKF, cross-correlation extraction algorithm, low pass filtering with sliding slide filtering. In this method, firstly, a limiter filter is used to filter the step excitation signal caused by impact noise. Then UKF filter is used to filter out the most colored noise caused by vibration acceleration, at the same time, low-pass filtering is used to deal with the magnetic interference noise in the output signal of tri-axis fluxgate, and using sliding filter to smooth the radial acceleration signal in the tri-axis acceleration sensor. Finally, the radial or tangential direction magnetic signal of the tri-axis fluxgate is used as the reference signal, and use reference signal to extract accurately the gravity acceleration component of the near bit position by cross-correlation detection method, then can finish the accurate calculation of dynamic well inclination angle. The results are verified by the simulation test and the simulation experiment of the rotating vibration test platform. When the drilling tool rotation speed within 60-300r/min, the maximum measurement error is +0.090, the overall measurement accuracy of inclination angle under different drilling tool rotation speed is better than 10.10and the measured results are not affected by the rotation speed and vibration of drilling tool, it realizes the dynamic and accurate measurement of the inclination angle in the process of drilling.