To detect the demagnetization fault (DF) of a dual-sided permanent magnet synchronous linear motor, a new method based on deep feature fusion of external stray flux signal (ESFS) is proposed. First, finite element models under ideal materials and assembly conditions are established to extract ESFS to reflect DF information. Second, Markov transition field and recurrence plot transform 1-D signals into 2-D images, to realize DF feature visual enhancement. Low-rank representation networks can merge the advantages of both methods by image fusion. Then, an accurate diagnosis framework, as efficient channel attention-MobileNetV3, is proposed to conduct deep feature extraction and realize diagnosis in both qualitative fault type classification and fault degree evaluation aspects. The classification accuracy reaches 98.50%, and the evaluation indexR2reaches 0.96, superior to other frameworks. Finally, a tunnel magnetoresistance sensor is applied to realize ESFS noninvasive online measurement, and an experimental platform is built to certify the superiority.

This article presents a linear motor mover position detection method based on an image sample database to achieve high-precision measurement of the position of a long-stroke linear motor mover. First, the aperiodic fringe image is constructed with a chirp signal as the target shooting source, and an image sample database is established by capturing the target image with a certain acquisition frequency using a line-scan camera. Second, a normalized correlation coefficient (NCC) matching method is proposed to search for the position of the current image in the database. A coarse positioning method based on velocity estimation is also employed to improve the efficiency of position matching in the sample database. Third, to overcome the position measurement error caused by incomplete matching between the current image and the image in the database, a subpixel measurement algorithm based on local upsampling NCC is proposed to improve the measurement accuracy. Finally, the actual displacement of the actuator is obtained by combining the calibration coefficients. To verify the feasibility of the proposed method, an experimental platform is built using a line-scan camera, a linear motor, an adjustable light source, and a target source image. Comparison tests, speed adaptation experiments, and light adaptation experiments are set up. The experimental results show that the average time of the proposed method is approximately 1 ms, and the average absolute measurement error is approximately 0.005 mm under various working conditions, which shows that the proposed method has real-time performance and strong environmental adaptability.

To improve the tracking accuracy and antidisturbance performance of permanent magnet linear synchronous motor (PMLSM), in this article, a fast recursive terminal sliding-mode control (FRTSMC) with mover velocity and disturbance observer (MVDO) is proposed. The FRTSMC is designed by a recursive structure of fast terminal sliding mode and integral sliding mode. The FRTSMC is initialized with an appropriate value to reduce the sliding-mode approach time and guarantee that the tracking error converges to zero in a finite time. To further improve the antidisturbance property and suppress the chattering phenomenon, an MVDO is integrated into the FRTSMC to simultaneously estimate the mover velocity and the lumped disturbance. Lyapunov theory is employed to analyze the closed-loop stability of the composited controller. The experimental results verify the effectiveness of the proposed method in reducing convergence error and enhancing robustness compared with the conventional fast nonsingular TSMC, adaptive recursive TSMC, and nonlinear disturbance observer based FRTSMC for PMLSM.

To improve the bandwidth and thrust performance of permanent magnet synchronous linear motor (PMSLM), from the perspective of motor structure design, this article proposes a high-accuracy and fast-calculation inductance model of centralized winding based on analytic kernel-embedded elastic-net regression (AKER) to establish a comprehensive analytical model of PMSLM performance. A stochastic fractal search (SFS) algorithm is introduced to optimize the comprehensive performances of PMSLM. First, Neumann's formula of integral is used to establish the equivalent inductance analytical model (EIAM) for the three-phase winding of PMSLM, and the analytic kernel is constructed based on the EIAM. In combination with the finite element simulation samples of inductance, the analytical kernel is embedded into the elastic-net regression to establish the inductance model of high efficiency, namely AKER, and compared with the finite element mode (FEM), EIAM and other advanced methods, the superiority of AKER is verified from the accuracy and time cost. Then, based on the proposed AKER inductance model, the current and voltage equations of PMSLM are decoupled in the two-phase rotating coordinate system to establish the comprehensive analysis model of the PMSLM performance. Based on the comprehensive model, the optimization function of PMSLM is constructed, and the SFS algorithm is introduced to solve the function iteratively. Finally, the effectiveness of the proposed modeling and optimization method is verified by FEM-based control simulation and prototype experiment, respectively.

Traditional design optimization methods for permanent magnet linear synchronous motor (PMLSM) always pursuit the optimal result without considering the influence of uncertainties (manufacturing errors, use wear, etc.), which will result in big fluctuations for the stability of motor thrust performance. This article proposes a new filled function algorithm (NFFA) to improve the stability of PMLSM. It searches for the structure parameters that not only lay in the smooth area of thrust ripple distribution but also satisfy the thrust ripple constraints under the effect of uncertainties. First, an analytical model of thrust ripple is established for the next-step optimization. Second, a sigma level stability criterion is proposed, which will be used to judge the robustness of optimal thrust ripple under uncertainties, and a new global robust algorithm NFFA is studied to realize the stability of thrust ripple by managing the sigma level stability criterion as the convergence criterion of traditional filled function optimization algorithm (TFFA). Third, the NFFA is used to optimize the thrust ripple of PMLSM based on the analytical model. The optimization results are proven to be stable and superior compared with other methods, such as TFFA, Taguchi robust optimization method, and design for six sigma method. Finally, the experiments confirm the feasibility and validity of the proposed method.

To improve the accuracy and robustness of linear motor mover position detection from the target image, this article constructs an aperiodic sinusoidal stripe image and introduces the chirp transform algorithm (CTA) to achieve a fast, high-precision measurement. First, three kinds of sinusoidal stripe images are generated by controlling the stripe period of the sinusoidal stripe image. Second, the gray-level co-occurrence matrix is used to optimize the aperiodic sinusoidal stripe image with strong robustness. Third, a line-scanning camera is used to scan the image information to acquire the one-dimensional (1-D) signal, thereby improving the real-time performance. Finally, the displacement between the 1-D signals is calculated by the CTA, and the moving distance of the mover can be acquired in accordance with the calibration coefficient of the measurement system. Simulation and experimental results show the effectiveness and feasibility of the mover position measurement method.

This paper investigates a new method for the demagnetization fault recognition and classification in double-sided permanent magnet synchronous linear motors that are used in linear motion applications. This method is based on time-time-transform (TT) coupled with extreme learning machine (ELM), which are especially suitable for the industrial occasions such as motor batch demagnetization inspection before delivery and periodic maintenance. First, a finite element analysis model with demagnetization faults is built to extract three lines (up-line, center-line, and down-line) magnetic flux density signals. Second, TT is first applied to conduct magnetic signals waveform transformation, and digital picture processing technology is innovatively used to extract the pixel rate of its diagonal elements contour surfaces as the fault feature. Then, machine learning algorithm called ELM is utilized as a classifier to obtain the unique fault labels that can represent the demagnetization occurred positions, sides, and severity types in detail. The validity and superiority of ELM is verified through comparison with back propagation neural network, and probabilistic neural network. Finally, prototype motor experimental platform is designed to confirm the correctness and effectiveness of this proposed method.

In this article, dual-sided permanent magnet linear motors (DPMLM) have been wildly applied in linear motion occasions such as high-precision laser cutting machines. This article researches a new way for accurate demagnetization fault detection of DPMLM, and this proposed method based on signal enveloping and time-domain energy analysis can be suitably used in some typical industrial occasions such as motor batch demagnetization inspection before delivery and periodic maintenance. First, three magnetic signals in motor air-gap region are selected as demagnetization fault index, and finite element analysis (FEA) is used to obtain these signals. Second, complex continuous wavelet transform (CCWT) is introduced to preprocess the fault signal and extract signal envelop for next-step fault feature extraction. Comparison experiments show that CCWT is better than frequently used extreme value and Hilbert-Huang transform methods. Then, Teager-Kaiser energy operator (TKEO) is applied to detect time-domain energy of fault signal envelop as fault feature. In addition to this, Hanning window is used to optimize TKEO to enhance fault feature and help with the accurate detection of demagnetization fault. Finally, motor prototype is manufactured for actual experiment and the results under different noise environments can certify the effectiveness and robustness of this proposed method.

A control method that combines a vector resonant controller and an active disturbance rejection control controller is proposed in this article for suppressing the current harmonics of permanent magnet synchronous linear motors. First, the resonant controller is improved by changing its transfer function so that it can suppress current harmonics better. Then, an active disturbance rejection control (ADRC) is designed to suppress the parameter disturbance of motors, which will adversely affect the improved resonant controller. The parameter disturbance is estimated by an extended state observer, and linear feedback control is used for disturbance compensation. The ADRC and the improved resonant controller work together to not only suppress harmonics but also improve resistance against system disturbance. Finally, a linear motor control platform is built, and experimental results are presented to verify the significance and correctness of the proposed approach.

The extended joint transformation correlation (EJTC) algorithm is proposed in this paper to meet the high-precision and anti-interference requirements of linear motor mover position measurement. An image measurement system for linear motor mover position is established. Stripe image movement sequences are captured using a high-speed camera. First, the image is preprocessed using a bilateral filtering method. Second, the joint power spectral function of adjacent stripe images is obtained on the basis of the joint transform correlation method. The zero-order diffraction peaks are eliminated, and interference from cross-correlated peak side lobes is avoided. Third, subpixel positioning is obtained using the oversampled discrete Fourier transform. Finally, the actual displacement of the stripe images can be obtained by combining the system calibration coefficients. An experimental measurement platform for a linear motor mover is established to verify the validity of the EJTC algorithm. The EJTC shows high measurement accuracy and strong anti-interference ability.

In this article, a rapid and precise position measurement method based on joint spatial phase is studied for linear motors. First, a low-cost mover position measurement system is designed on the basis of motion characters of linear motors. The target image is an aperiodic fence stripe. The image sequences with the movement of the mover are recorded quickly by linear charge coupled device (CCD) camera. Second, spatial domain algorithm is introduced for the rough registration, and according to the registration results, the overlapping regions are extracted to form two new images, and then the improved phase difference method is used to get the accurate displacement of the two new images. The actual mover displacement is finally calculated by the calibration coefficient of the system. Simulation and experimental results show that this method proposed takes approximately 2 ms on average, and the measurement accuracy reaches 0.02 mm.

To improve the accuracy of the mover position detection with longer measurement range, a linear motor displacement measurement method is proposed based on digital image measurement and temporal sinusoidal fringe pattern phase retrieval. A simple computer-generated sinusoidal fringe pattern is designed as the target image, and sequences of fringe signals are recorded by a line scan camera that moves with the linear motor. Fourier phase analysis is employed to extract the initial phase of each signal curve, and then temporal phase unwrapping is applied to reconstruct the mover displacement evolution according to the system magnification parameter and fringe width. This method removes the phase ambiguity problem of traditional temporal phase analysis, and can be used in complex motion of permanent magnet synchronous linear motors. To suppress the spectrum leakage of fast Fourier transform (FFT), all-phase FFT with double Hanning windows is used to improve the detection accuracy of initial phase and fringe width. Simulation and experimental results demonstrate that the proposed method can estimate the mover position with longer measurement range precisely and robustly under different working conditions.