Rotor unbalance (RUB) and Sensor Runout (SR) are two main vibration sources for active magnetic bearing (AMB) system, and they not only produce considerable harmonic vibration but also endanger the stability of AMB system. Therefore, a nonlinear adaptive algorithm, whose asymptotic stability is guaranteed by Lyapunov's theory, is proposed in this paper to suppress the harmonic vibration. Nevertheless, RUB and the synchronous component of SR have the same frequency with the rotational frequency, and it is highly necessary to distinguish them from each other for the compensation of displacement stiffness force. Hence, the scheme of changing rotor speed is adopted for the permanent magnet biased magnetic bearing system. As a consequence, the harmonic current caused by RUB and SR is effectively attenuated and the displacement stiffness force is correctly compensated. The results of the numerical simulations and experiments demonstrate that the harmonic vibration is dramatically suppressed through the proposed method.

Permanent Magnet Synchronous Motor (PMSM) is widely used in nearly all industrial applications, as key components, the performance degradation of bearings in PMSM-driven-systems poses threats to operation safety. However, because of environmental conditions, space and weight limits, additional vibration sensors are unacceptable in some engineering scenarios, therefore, this study proposes a fault diagnosis method for bearings coupled with PMSM based on stator current signal. First, the current signals are calibrated based on current phases. Then, to improve the robustness of neural network, the training samples are extended based on data enhancement, considering the influencing factors of current signal. After that, calibrated residual signals are calculated to enhance the subtle fault information in current signals, and the calibrated residual signals are transformed to symmetrized dot pattern (SDP) images with color information. Finally, these images are employed for fault pattern recognition based on a pre-trained two-dimensional convolution neural network( 2D-CNN). The proposed method was validated based on stator current signals acquired from a test rig, and the accuracy of fault diagnosis was 96.67%. The advantages of the proposed method were also demonstrated based on comparative case studies.

Intelligent on-site fault diagnosis and professional vibration analysis are essential for the safety and stability of rotating machinery operation. This paper represents a fault diagnosis scheme based on two-stage compressed sensing for triaxial vibration data, which realizes fault diagnosis for rotating machinery based on compressed data and data reconstruction for professional vibration analysis. In the 1st stage, the triaxial vibration signals are compressed using a pre-designed hybrid measurement matrix; these compressed data can be used both for time-frequency transform and for vibration data reconstruction. In the 2nd stage, the frequency spectra of the triaxial vibration signals are fused and further compressed using another pre-designed joint measurement matrix, which inhibits the high-frequency noises simultaneously. Finally, the fused spectra are employed as feature vectors in sparse-representation-based classification, where the proposed batch matching pursuit (BMP) algorithm is utilized to calculate the sparse vectors. The two-stage compression scheme and the BMP algorithm minimize the computational cost of on-site fault diagnosis, which is suitable for edge computing platforms. Meanwhile, the compressed vibration data can be reconstructed, which provides evidence for professional vibration analysis. The method proposed in this study is validated by two practical case studies, in which the accuracies are 99.73% and 96.70%, respectively.

The harmonic current in active magnetic bearing (AMB) system usually results from rotor unbalance and sensor runout. It not only causes considerable harmonic vibration, but also brings about the power saturation and the instability of AMB system especially for the high-speed rotor. Therefore, a novel parallel finite dimension repetitive controller (PFDRC) is proposed to restrain the harmonic current in this paper. Compared with the conventional repetitive controller (RC), PFDRC only introduces the limited imaginary poles, so that the problem that the infinite imaginary poles of RC endanger the stability and instantaneous characteristic of the closed-loop system is solved. In order to realize the harmonic current suppression, the power amplifier system is taken as the controlled object, and the proposed PFDRC is parallel with the original equivalent main controller. Then the root reclus method is adopted to design the parameters and to analyze the closed-loop system stability. Based on the phase-frequency characteristic of the system function, the compensators are introduced to improve the system stability margin. Meanwhile, the low-pass property of power amplifier system is sufficiently taken into consideration in the parameters design. Finally, the simulations and experiments are implemented to validate the effectiveness of the proposed PFDRC, and the results demonstrate that the harmonic current is dramatically restrained by the proposed method.

As a critical component of aircraft flight control systems, aileron actuator’s fault directly affects the performance of flight control system and the overall flight safety of the aircraft. Therefore, effective fault diagnosis of aileron actuators becomes particularly important. However, with the development of redundancy design, the structure of aileron actuators is more and more complex, and the fault modes are more and more diverse, which increases the difficulty of fault diagnosis significantly. To address this challenge, this study proposes a fault diagnosis method based on parallel-symmetrical dot pattern (SDP) and polar sparse representation. First, the current residual signals of force motors are obtained using a bi-step observer. Then, the residual signals are transformed into parallel-SDP images, where each of the four petals of the parallel-SDP image is generated from the corresponding channel. The parallel-SDP image presents global fault information and captures subtle changes of residual signals, which increases the sensitivity of fault diagnosis. Subsequently, rapid single-channel fault localization is achieved by barycenter analysis of the parallel-SDP images. Finally, based on the result of fault localization, the proposed polar sparse representation algorithm is utilized for fault diagnosis of mechanical and control faults separately. Based on the dataset obtained from physical-parameter-simulation, the proposed method was validated, and the accuracy of fault diagnosis was 99.29%, which is better than conventional fault diagnosis, meanwhile, the computational resources consumption of the proposed method is much less than deep-learning-based method, which is suitable for embedded computing system in aircraft.

The superheterodyne receiver is a typical device widely used in electronics and information systems. Thus effective performance assessment and prediction for superheterodyne receiver are necessary for its preventative maintenance. A scheme of performance assessment and prediction based on Mahalanobis distance and time sequence analysis is proposed in this paper. First, a state observer based on radial basis function (RBF) neural network is designed to monitor the superheterodyne receiver and generate the estimated output. The residual error can be calculated by the actual and estimated output. Second, time-domain features of the residual error are then extracted; after that, the Mahalanobis distance measurement is utilized to obtain the health confidence value which represents the performance assessment result of the most recent state. Furthermore, an Elman neural network based time sequence analysis approach is adopted to forecast the future performance of the superheterodyne receiver system. The results of simulation experiments demonstrate the robustness and effectiveness of the proposed performance assessment and prediction method.