As an important part of the power source, motorized spindle is considered as the components in the high-end CNC machine tools, which is required as a special attention to avoid expensive production shutdown due to appearance of massive failures. Therefore, it is necessary to detect the incipient fault of the motorized spindle by establishing an appropriate model. This paper presents a scheme for detecting the stator-winding incipient shorted-turn fault of motor system in the motorized spindle. As the cornerstone of the model-based fault detection scheme, the electromagnetic coupling motor model in motorized spindle with the stator-winding incipient shorted-turn fault under dq coordinate is given. In the presence of electromagnetic disturbance, two aspects can make the stator-winding shorted-turn fault of motor in motorized spindle be detected earlier. Firstly, the modified interval observer based on the established mathematical model of the motor in motorized spindle has more design freedom than the Luenberger interval observer, which can make the residual interval generated in the optimization process have better performance and wider range of applications. Secondly, the l1/H∞ performance is introduced in the process of residual interval generation, which can make it have better electromagnetic disturbance robustness and incipient fault sensitivity. The effectiveness of the proposed scheme is illustrated by numerical simulation, and is further validated by lab experiments on a 9:8kW motorized spindle test rig.

Cutting force identification is critical to improving industrial robot performance and reducing machining vibration. However, most indirect identification methods of cutting force are not applicable since the modal parameters of the robotic milling system vary with the robot pose. This paper presents a novel pose-dependent method to identify the cutting force using the acceleration signal generated by robotic milling. First, the modal parameters at different machining points are employed as a training dataset to develop the Gaussian Process Regression (GPR) model. Next, the modal parameters predicted by the GPR model are employed to optimize the cutting force estimation based on the minimum variance unbiased estimate method. Then, the Kalman filter method is employed to update the covariance matrix of the cutting force identification error and the state estimation error. Lastly, the effectiveness of the proposed method is verified with robotic milling experiments, and the results show that the identification error and time are acceptable under the condition of variable robot pose.

This paper solves the leader-following consensus problem for a class of second-order multi-agent systems with input quantized by a newly proposed adaptive dynamic quantizer. The novel dynamic quantizer is an adaptive quantizer that combines the logarithmic quantizer and the uniform quantizer by introducing dynamic gain parameters to achieve quantizer adaptive adjustment. It has advantages of logarithmic, uniform, and adaptive dynamic quantizers in ensuring reducible communication expenses and acceptable quantizer errors for better system performance. On this basis, we transform the guide way climbing frame (GWCF) under ideal conditions into a second-order multi-agent system and solve the motion synchronization problem of GWCF. Finally, we illustrate our approach by numerical examples.