This paper presents a complex product design-manufacturing-operations and maintenance integration method based on digital twin technology. This method aims to solve the problem of information silos in the design, manufacturing and operation and maintenance phases of complex products in the context of intelligent manufacturing and to integrate the design, manufacturing and operation and maintenance processes of complex products. To address the integration needs of complex product design, manufacturing, operation and maintenance business integration, a framework for complex product design-manufacturing-operation and maintenance integration based on the digital twin is first proposed, in addition to designing a model and operation mechanism for combining the virtual and real of the digital twin model. Then, the implementation of multistage collaborative design technology, data intelligent sensing technology, and data integration and fusion technology for the digital twin-based design-manufacturing-operations and maintenance integration processes are analyzed and discussed. Finally, a case study involving the fault prediction of key components of the bogie of an EMU demonstrated the integrated mode of operation in the design-manufacture-operation and maintenance process of the EMU. It verified the effectiveness of the proposed framework, process and methodology.

Aiming at the traffic congestion problem of mining vehicles in the intersection area of three-fork roadways in coal mines, this paper proposes a speed guidance strategy based on a vehicle–road cooperative environment to adjust the running state of mining vehicles in the three-fork roadway system. The proposed speed guidance strategy can realize the safe and effective passage of underground mining vehicles to the greatest extent. Taking a three-fork roadway in a coal mine as an actual case, the operation of mining vehicles in the three-fork roadway is optimized and scheduled. Through the joint simulation of PTV VISSIM 4.3 traffic simulation software and PYTHON 3.7, the travel time, number of queuing vehicles, and delay time of mining vehicles passing through the three-fork roadway entrance under the two conditions of no speed and speed guidance in the coal mine are simulated and compared. The results show that after using the proposed speed guidance strategy for scheduling, mining vehicles can quickly pass through the three-fork roadway intersection. The travel time is reduced by 18.4%, the number of queuing vehicles is reduced by 41.5%, and the delay time is reduced by 24.1%, which effectively improves the transportation efficiency of underground mining vehicles.

Aiming at the problem that, under certain extreme conditions, relying on tire force or tire angular velocity to represent the longitudinal velocity of the unmanned vehicle will fail, this paper proposes a longitudinal velocity estimation method that fuses LiDAR and inertial measurement unit (IMU). First, in order to improve the accuracy of LiDAR odometry, IMU information is introduced in the process of eliminating point cloud motion distortion. Then, the statistical characteristic of the system noise is tracked by an adaptive noise estimator, which reduces the model error and suppresses the filtering divergence, thereby improving the robustness and filtering accuracy of the algorithm. Next, in order to further improve the estimation accuracy of longitudinal velocity, time-series analysis is used to predict longitudinal acceleration, which improves the accuracy of the prediction step in the unscented Kalman filter (UKF). Finally, the feasibility of the estimation method is verified by simulation experiments and real-vehicle experiments. In the simulation experiments, medium- and high-velocity conditions are tested. In high-velocity conditions (0–30 m/s), the average error is 1.573 m/s; in the experiment, the average error is 0.113 m/s.