Jianzhong Yang

Rolling bearings are crucial components in CNC machine tool spindles, and their health condition directly affects machining precision and operational reliability. To address the significant challenges of bearing fault diagnosis in industrial environments, this paper proposes an adaptive shapelet-based deep learning model for bearing fault diagnosis. The proposed model integrates three key components: (1) an adaptive multi-scale shapelet extraction module for discriminative pattern learning, (2) a gated parallel CNN with depthwise separable convolutions for multi-scale spatial feature extraction, (3) an enhanced bidirectional long short-term memory network with residual connections for temporal dependency modeling. A composite loss function combining cross-entropy, supervised contrastive learning, and multi-scale consistency regularization is employed for training. To simulate real-world industrial noise conditions, Gaussian, uniform, and impulse noise were injected into the signals. Experiments conducted on the CWRU and IMS datasets demonstrate that, compared with state-of-the-art methods, the proposed approach achieves stronger noise robustness, higher fault classification accuracy, and more stable performance under severe noise contamination.

To improve the disturbance-rejection performance of the permanent magnet synchronous motor (PMSM) cascade speed control system under voltage saturation constraints (VSCs), a reference current-corrected disturbance observer (RCCDOB) method is proposed. This method first integrates the linear current loop dynamics (CLD) into the disturbance observer to minimize the impact of current tracking errors on speed loop disturbance compensation. Then, an anti-windup auxiliary system is designed to correct the reference current in order to convert the VSC into a current control input constraint, addressing the saturation nonlinearity in the CLD. Furthermore, based on this auxiliary system, an RCCDOB speed control structure is developed to enhance the robustness of the system under VSC and disturbances. The stability analysis and design procedure of the RCCDOB control system are also presented. The comparative experiment with other competitive methods in disturbance rejection verifies the effectiveness of the proposed method.

The smoothing of linear toolpaths plays is critical in improving machining quality and efficiency in five-axis CNC machining. Existing corner-smoothing methods often overlook the impact of spline curvature fluctuations, which may lead to acceleration variations, hindering surface quality improvements. The paper presents a five-axis toolpath corner-smoothing method based on the space of master–slave movement (SMM), aiming to minimize curvature fluctuations in five-axis machining and improve surface quality. The concept of movement space in master–slave cooperative motion is introduced, where the tool tip position and tool orientation are decoupled into a main motion trajectory and two master–slave movement space trajectories. By deriving the curvature monotony conditions of a dual Bézier spline, a G2-continuous tool tip corner-smoothing curve with minimal curvature fluctuations is constructed in real-time. Subsequently, using the SMM and the asymmetric dual Bézier spline, a high-order continuous synchronization relationship between the tool tip position and tool orientation is established. Simulation tests and machining experiments show that with our smoothing algorithm, maximum acceleration values for each axis were reduced by 21.05%, while jerk was lowered by 22.31%. These results indicate that trajectory smoothing significantly reduces mechanical vibrations and improves surface quality.

Traditional integer-order admittance control is widely used in industrial scenarios requiring force control, but integer-order models often struggle to accurately depict fractional-order-controlled objects, leading to precision bottlenecks in the field of precision machining. For robotic precision polishing scenarios, to enhance the stability of the control process, we propose a more physically accurate five-parameter fractional-order admittance control model. To reduce contact impact, we introduce a method combining the rear fastest tracking differential with fractional-order admittance control. The optimal parameter identification for the fractional-order system is completed through Pareto optimality and a time–frequency domain fusion analysis of the control system. We completed the optimal parameter identification in a simulation, which is applied to the robotic precision polishing scenario. This method significantly enhanced the force control precision, reducing the error margin from 15% to 5%.

Parameter identification is a critical factor influencing the dynamic performance of permanent magnet synchronous motor (PMSM)-driven servo systems. However, mechanical parameter identification encounters the following challenges: multiple parameters that are significantly mutually coupled and difficulties in direct measurement, especially with friction parameters. Currently, most scholars have navigated these challenges through partial parameter identification, either by ignoring Coulomb friction, neglecting the dynamic processes of Coulomb friction, or identifying only the parameters associated with the friction model. In this study, a scheme using a parallel-cascaded extended sliding-mode observer (PCESMO) is proposed to address these challenges. The PCESMO relies solely on phase currents and rotor position to identify parameters, including the moment of inertia, viscous damping, load torque, and parameters of a simplified LuGre model that considers the dynamic process of Coulomb friction during self-commission. Additionally, the PCESMO has low computational complexity and is easy to implement. The effectiveness of the PCESMO was validated through both simulations and experiments. Compared to other competitive methods, PCESMO-identified parameters result in optimal control effects.

To overcome the issue of poor absolute positioning accuracy owing to the low stiffness of serial robot arms, this study proposed a new dual-arm robot (DAR) through the optimisation of the configuration of traditional robots. The DAR adopts a novel kinematic structure comprising a pair of 3-degree of freedom (DOF) robots: the workpiece and tool arms. A kinematic modelling method based on a closed-loop virtual kinematic chain was proposed to solve the problem of synchronous control of both arms via one controller. The workspace of the DAR was calculated, and the singularity was analysed. Furthermore, the stiffness was evaluated through finite element analysis. The results demonstrate that the DAR exhibits significantly higher stiffness, thus enabling considerably better positioning accuracy. To verify the superiority of the proposed DAR, a machining platform was developed based on the DAR for grinding and polishing an aero-engine blade. Compared to a traditional serial 6-DOF robot, the proposed DAR significantly improved the contour accuracy, with a surface roughness Ra 0.3 v.s. 0.4 $\mu $ m. Furthermore, the profile deviation of the blade body and edge were $+$ 0.05 mm, -0.07 mm v.s. $+$ 0.075 mm, -0.07 mm, and $+$ 0.05 mm, -0.03 mm v.s. $+$ 0.05 mm, -0.05 mm, respectively. Note to Practitioners —This study aimed to enhance the machining accuracy of industrial robotic machining. A novel dual-arm robot with superior stiffness was designed and compared with a conventional serial 6-DOF robot. A prototyping DAR was developed as the foundation for a grinding platform’s subsequent design and construction. The platform was utilised in aero-engine blade grinding and polishing. The contour accuracy of the processed blade drastically improved compared with that of the traditional serial 6-DOF robot system. Experiments verified the proposed robot’s feasibility and superiority in improving the robotic machining accuracy. Furthermore, the proposed DAR exhibited good potential for application in other robotic machining scenarios, such as assembly, carving, etc.

An identification method is presented for the arctan two-parameter model, which describes the deadtime effect voltage error of PMSM drives' three-phase voltage source inverter (VSI) during self-commission. In this method, the motor maintains a standstill during the whole identification process. First, a disturbance observer is used to improve the tracking performance of the total voltage error. Then, a technique is used to extract the deadtime effect voltage error from the total voltage error without a voltage sensor during the standstill condition. Finally, the core algorithm (utilizing the bisection method) is developed to solve the coupling problem in the two-parameter identification process, such that the parameters in the model converge simultaneously. Furthermore, a sufficient condition for the convergence of the algorithm is given, and a mathematical proof is presented. Experimental results show that the deadtime effect voltage error estimated by our method is closer to the measured voltage error than other state-of-the-art methods proposed in the literature.