Aiming at the shortcomings of single-sensor sensing information characterization ability, which is easily interfered with by external environmental factors, a method of intelligent perception is proposed in this paper. This method integrates multi-source and multi-level information, including spindle temperature field, spindle thermal deformation, operating parameters, and motor current. Firstly, the internal and external thermal-error-related signals of the spindle system are collected by sensors, and the feature parameters are extracted; then, the radial basis function (RBF) neural network is utilized to realize the preliminary integration of the feature parameters because of the advantages of the RBF neural network, which offers strong multi-dimensional solid nonlinear mapping ability and generalization ability. Thermal-error decision values are then generated by a weighted fusion of different pieces of evidence by considering uncertain information from multiple sources. The spindle thermal-error sensing experiment was based on the spindle system of the VMC850 (Yunnan Machine Tool Group Co., LTD, Yunnan, China) vertical machining center of the Yunnan Machine Tool Factory. Experiments were designed for thermal-error sensing of the spindle under constant speed (2000 r/min and 4000 r/min), standard variable speed, and stepped variable speed conditions. The experiment’s results show that the prediction accuracy of the intelligent-sensing model with multi-source information fusion can reach 98.1%, 99.3%, 98.6%, and 98.8% under the above working conditions, respectively. The intelligent-perception model proposed in this paper has higher accuracy and lower residual error than the traditional BP neural network perception and wavelet neural network models. The research in this paper provides a theoretical basis for the operation, maintenance management, and performance optimization of machine tool spindle systems.

Because the linear motor feeding system always runs in complex working conditions for a long time, its performance and state transition have great randomness. Therefore, abnormal detection is particularly significant for predictive maintenance to promptly discover the running state degradation trend. Aiming at the problem that the abnormal samples of linear motor feed system are few and the samples have time-series features, a method of abnormal operation state detection of a linear motor feed system based on normal sample training was proposed, named GANomaly-LSTM. The method constructs an encoding-decoding-reconstructed encoding network model. Firstly, the time-series features of vibration, current and composite data samples are extracted by the long short-term memory (LSTM) network; Secondly, the three-layer fully connected layer is employed to extract potential feature vectors; Finally, anomaly detection of the system is completed by comparing the potential feature vectors of the two encodings. An experimental platform of the X-Y two-axis linkage linear motor feeding system is built to verify the rationality of the proposed method. Compared with other classical methods such as GANomaly and GAN-AE, the average AUROC index of this method is improved by 17.5% and 9.3%, the average accuracy is enhanced by 11.6% and 15.5%, and the detection time is shortened by 223 ms and 284 ms, respectively. GANomaly-LSTM has successfully proved its superiority for abnormal detection for running state of linear motor feeding systems.

To address the challenges of missing degradation samples and the low accuracy of traditional degradation assessment methods in linear motor feeding systems, and considering the advantages of Digital Twin technology in complex equipment status evaluation and performance prediction, this study introduces Digital Twin into the performance degradation assessment of the linear motor feeding system. To resolve the issue of missing degradation samples, a digital integrated model consisting of the mechanical subsystem, electrical subsystem, and control subsystem is established, based on an analysis of the multi-field coupling mechanisms (mechanical, electrical, and magnetic) of the linear motor feeding system. Normal operating conditions and demagnetization degradation conditions are designed, and the motor output characteristics under multiple conditions are simulated and analyzed. A current simulation dataset for both normal and demagnetization degradation conditions is constructed.In response to the challenges posed by the temporal dependence of operational data and the difficulty of identifying degradation states, and leveraging the superior data transformation capabilities of Gramian Angular Field (GAF), which effectively preserves temporal information, this study proposes a performance degradation assessment model combining GAF encoding with the AlexNet convolutional neural network. The model first converts one-dimensional time-series data into two-dimensional images using GAF encoding, and then utilizes the image recognition capabilities of AlexNet to assess the degradation state of the linear motor feeding system. This approach enables the evaluation of irreversible demagnetization degradation levels, achieving an accuracy of 98.3%.Furthermore, compared to the traditional methods of PNN, GRNN, and LS-SVM, the proposed method shows improvements of 10.3%, 4.9%, and 15.2% in the AUC index, respectively. The average accuracy is improved by 10.5%, 5.3%, and 14.7%, significantly enhancing the evaluation accuracy. This method effectively resolves the issue of insufficient real degradation samples and provides a powerful tool for predictive maintenance and performance monitoring in industrial environments.

(1) Background: Air rudders are used to control the flight attitude of aircraft, and their surface quality directly affects flight accuracy and safety. (2) Method: Traditional positioning methods can only obtain defect location information at the image level but cannot determine the defect’s physical surface position on the air rudder, which lacks guidance for subsequent defect repair. We propose a defect physical surface positioning method based on a camera mapping model. (3) Results: Repeated positioning experiments were conducted on three typical surface defects of the air rudder, with a maximum absolute error of 0.53 mm and a maximum uncertainty of 0.26 mm. Through hardware systems and software development, the real-time positioning function for surface defects on the air rudder was realized, with the maximum axial positioning error for real-time defect positioning being 0.38 mm. (4) Conclusions: The proposed defect positioning method meets the required accuracy, providing a basis for surface defect repair in the air rudder manufacturing process. It also offers a new approach for surface defect positioning in similar products, with engineering application value.