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The key to the evaluation of bearing performance degradation is to extract sensitive characteristic indicators that can reflect the degradation process. In order to solve the problem of insufficient sensitivity of the bearing multi-source signals feature, a fusion entropy ratio feature based on dynamic time warping is proposed. First, the segmented approximate entropy, sample entropy and fuzzy entropy of the bearing performance degradation process are extracted, and then the mean clustering is used to obtain the standard entropy vector of the health state. In order to eliminate the influence of working environment and entropy value on state evaluation, a concept of entropy ratio is defined by the ratio of the entropy vector of each state to the standard entropy vector of the health state. Finally, using dynamic time warping has the advantage of accurately describing the similarity between vectors from a global perspective, and the multi-source heterogeneous fusion entropy ratio distance feature is constructed to describe the degraded state of bearing performance. Experiments show that this feature is more sensitive to the bearing performance degradation than other features, such as fusion entropy ratio feature based on Euclidean distance, single entropy ratio feature based on dynamic time bending.

Herein, we present a case report of sintilimab treatment for a patient with ureteral cancer reoccurring after bladder cancer, and exploration of the mechanism of adverse reactions from the aspects of intestinal flora and immunity. We have reported a case of leukopenia in a patient with recurrent ureteral cancer after bladder cancer who was treated with sintilimab. A 52-year-old Chinese man with a history of hypertension and diabetes presented with lower urinary tract symptoms, including painless hematuria, frequent and urgent urination, and micturition without pain. Computed tomography (CT) and 3-dimensional (3D) reconstruction suggested bladder space occupation, bladder cancer was pathologically confirmed after laser resection of the bladder tumor, which then recurred and was subject to reoperation. After 8 months, B-mode ultrasonography indicated left ureter occupation, and the patient began sintilimab immunotherapy according to the outcome of immunohistochemistry (IHC) and immune checkpoint inhibitor (ICI) evaluation. The patient was treated with sintilimab a total of 6 times. After the first treatment, the patient was in stable condition. The second treatment was discontinued due to renal insufficiency. The patient was then treated with renal and liver protection for 1.5 months, followed by 5 rounds of immunotherapy. After the sixth round of immunotherapy, the patient presented with leukopenia. In order to determine the causes of adverse reactions, we analyzed the changes of intestinal flora of patients before and after immunotherapy, and summarized the immune function indicators of patients during immunotherapy. The leucopenia induced by sintilimab may be related to intestinal flora and immunity.

Metric transfer learning overcomes the problem of no domain distribution alienation in traditional sample transfer, and also improves the classification accuracy. However, some problems exist; for example, the target domain must have labelled samples without considering the manifold connection between data samples, and the algorithm is not robust under sparse samples. A compound fault diagnosis method for rolling bearings based on graph label manifold metric transfer is proposed. First, the pseudo-label of the target domain is acquired through a weighted graph constructed on the source domain with known fault labels. In order to ensure the accuracy of metrics in manifold space, we adopt the method of graph label propagation (LP) to gradually spread the label information. Then, the sample data are mapped to the low-dimensional manifold space by locality preserving projections (LPP), which gives higher weight to the source domain samples close to the target domain to solve the domain offset problem. At the same time, combined with the labelled samples of the target domain in LP, the manifold distance measure is learned to minimize the intra-class distance and maximize the class spacing, eliminate the overlap phenomenon in the domain and realize the distribution alienation between classes in the domain. After each iteration, unsupervised manifold metric transfer learning is realized by increasing the range of LP and gradually reducing the error of graph LP. Experiments show that the new unsupervised transfer learning method has better fault identification ability than the semi-supervised transfer learning method. In the case of sparse training samples, it still maintains a high diagnostic accuracy, and the highest fault recognition accuracy can reach more than 97% with good robustness.

To achieve high-precision position control for the active magnetic bearing high-speed flywheel rotor system (AMB-HFRS), a novel control strategy based on inverse system method and extended two-degree-of-freedom (2-DOF) proportional–integral–derivative (PID) controller is proposed in this study. First, the inverse system method is employed to decouple the AMB flywheel rotor system with strong non-linear and coupling, into four independent subsystems. Subsequently, extended 2-DOF PID controllers are used to regulate the decoupled subsystems, to obtain good performances of disturbance rejection and tracking simultaneously. In the extended 2-DOF PID controller, a differential signal is produced by a velocity observer to improve the ability of against noise. Finally, the characteristics of stability, tracking, disturbance rejection and robustness of the control strategy proposed are analysed in theory, and its ability and effectiveness to control the radial position of the AMB-HFRS are further studied by simulations and experiments. It is shown that the control strategy proposed can keep the AMB-HFRS suspending stably from static state to the speed of 24,000 rpm, has the advantages of good tracking, high disturbance rejection, strong robustness and good ability of against noise.

To eliminate the influence of gyroscopic effect on system stability and to improve the control performances for the active magnetic bearing (AMB) high-speed flywheel rotor system, a novel control strategy based on the modal separation and inverse system method is proposed. The translational motions and rotational motions of the AMB flywheel rotor system are firstly decoupled by the modal separation, then the rotational motions are decoupled by the inverse system method, so that the AMB flywheel rotor system, which is a multivariable and strong coupled system, is decoupled into four subsystems. Finally, the subsystems are regulated by four two-degrees-of-freedom (2-DOF) internal model controllers, respectively. Furthermore, the stability, tracking and robustness of the novel control strategy are analysed theoretically, and its effectiveness on vibration control of the AMB high-speed flywheel rotor system is further investigated by simulations and experiments. The results show that the novel control strategy can achieve stable suspension and effectively suppress the vibration of the AMB high-speed flywheel rotor system from static state to 24,000 rpm, with the advantages of high stability, strong robustness, and good performances of tracking and disturbance rejection simultaneously.

The crosstalk voltage is the result of the combined effect of the displacement current generated by the gate-drain capacitance and the induced voltage introduced by the common source inductance. When considering common-source inductance, the relationship between the driving circuit impedance and the spikes of crosstalk voltage is no longer a single correlation. This paper starts with the crosstalk phenomenon in the double pulse test (DPT) circuit, introduces the causes of the crosstalk phenomenon in detail, and constructs the equivalent circuit of the drive loop to obtain the mathematical expression of the crosstalk voltage calculation. Finally, through mathematical analysis, the influence of gate-source capacitance and gate resistance on the spikes of crosstalk voltage is obtained, which provides guidance for the design of driving parameters.

This paper presents a twelve-pole heteropolar radial hybrid magnetic bearing (HRHMB) structure. Firstly, the structure and equivalent magnetic circuit (EMC) are designed. And the radial electromagnetic force characteristics are calculated by the EMC model. At the same time, the rationality of EMC model is verified by the finite-element method (FEM) of Magnet software. Then, the 2-D model of the twelve-pole HRHMB is established in Magnet software. The flux density variations of twelve-pole HRHMB and eight-pole HRHMB under different currents are compared by using the FEM. Finally, a method of Magnet-Simulink cosimulation is proposed to analyze the suspension characteristics of the twelve-pole HRHMB and compared with the eight-pole HRHMB. Thus, the effective combination of theoretical analysis, FEM analysis, and Magnet-Simulink cosimulation analysis is realized in the design of HRHMB. The results of Magnet-Simulink cosimulation show that the twelve-pole HRHMB has the advantages of low power consumption, small coupling, large construction dynamic stiffness, and better suspension characteristics than the eight-pole HRHMB.

Effective and accurate diagnostic methods are necessary to ensure the stable and efficient operation of proton exchange membrane fuel cells (PEMFC). In practice, the voltage drop is commonly used as the detection indicator of the flooding fault. However, the output power changes also affect the voltage of the PEMFC. To better diagnose the flooding fault under load-varying conditions, a data-driven method for PEMFC using deep learning technologies is proposed, which can automatically extract fault features for the raw data to diagnose the flooding fault. First, the indicators for the fault diagnosis model are selected to meet the actual situation according to the water transport mechanism and auxiliary systems of the general fuel cell stack. And the collected data are transformed into a 2-D graph to visually represent the characteristics of the time-series data. Then, the convolutional neural network is adopted to develop the fault diagnosis model. In addition, the batch normalization method is used to alleviate feature distribution differences and enhance the model generalization. Finally, the trained model is applied to detect the flooding fault. A real PEMFC experiment dataset is adopted to verify the diagnostic performance of the method. Experiment results show that the proposed model can effectively identify the flooding fault of the fuel cell accurately under load-varying conditions, and achieves over 99% accuracy.

This study is concerned with the unified analytical solution of rotor strength for the segmented permanent magnet (PM) rotor retained by the carbon-fibre sleeve and non-magnetic alloy sleeve in high-speed surface-mounted permanent magnet synchronous machine. Considering the influence of different densities and coefficients of thermal expansion of PMs and pole fillers on rotor stress, the analytical solution for rotor strength was proposed based on displacement method and stress potential method in polar coordinate. The proposed analytical solution was validated by the finite-element method (FEM) and experiment, respectively, and the influences of rotational speed, retaining sleeve thickness and interference fit between the retaining sleeve and PMs on rotor strength were further investigated based on the analytical solution proposed. It is shown that the results calculated by the proposed analytical solution and the FEM are in good agreement with each other, and the analytical solution can accurately predict the stress distributions of the segmented PM rotor retained by the carbon-fibre sleeve and non-magnetic alloy sleeve. The difference in density and coefficient of thermal expansion between PM and pole filler, rotational speed and operating temperature have great effects on rotor strength.

How to improve the reliability has been a challenging task for transient protection, which has been affected seriously by the fault initial angle (FIA) and transient resistance (TR). Based on support vector machine (SVM), a novel smart transient protection method is presented in this study, which protects a bus and two transmission lines connected to the bus. The method is based on the measurements from the two close-in ends of the two lines. Hilbert–Huang transform is used to extracting instantaneous amplitude from the measured currents. The fault area is determined from the two instantaneous amplitude-integral (IA) of the transient fault currents, the difference of the two IAs, FIA, and the TR. SVM is utilised to determine the fault area. Two IAIs, difference of IAs, FIA and TR are treated as the input to SVMs. Faults with different FIA and TR are treated as a different class of fault, respectively. Correspondingly, a different criterion of discrimination is automatically employed by SVMs for a different fault class. The influence of FIA and TR is eliminated significantly, and the reliability of protection is enhanced substantially. The performance of the method is tested using ATP/EMTP with satisfactory results.

In this paper proposes a new digital monitoring method, which is aimed at the problems of low accuracy and poor stability of current arc welding inverter power parameters. The sensor based on the principle of zero flux modulation can effectively overcome the measurement error and realize the electrical isolation between the main output and the control circuit, which improves the collecting and measuring accuracy of the electrical signal. Meanwhile, the Ethernet communication technology is applied to the data transmission with high transmission rate and reliability, which is the core of the monitoring system of the arc welding inverter power. The applied results show that the monitoring system can digitally collect and display the electrical parameters during the experiment of the dual-pulse metal inert-gas welding(MIG). Moreover, the measurement accuracy is high and has good stability performance.

In this paper, the model of equivalent torsion spring is adopted to divide the simply supported beam laminated by the stator elastomer and piezoelectric plate of a TRUM into two segments, and the vibration equation is established. Based on the equation, the overall vibration model and its forced response analysis of the TRUM stator with cracks are derived according to the boundary conditions of simply supported beam and the consistency conditions of deflection, shear, and torque and the angle coordination at the crack. Then a simulation example of this kind of TRUM is carried out on the MATLAB software platform, it is found that the existence of cracks would change the shape of traveling wave on the TRUM and produce the influence of inconsistent peak points of lateral vibration and its longitudinal motion velocity of the stator. This provides a better way to detect whether there are cracks in a TRUM according to the waveform variation of stator vibration, and then to monitor the healthy state of the motor operation in the future.

For the stability problem of the active magnetic levitation ball, the application of the PID controller on the system is determined. Firstly, the linearized mathematical model of the active magnetic levitation ball system is established. Then the PID parameters are adjusted by the trial method. The influence of three PID parameters on the system is introduced, and a PID controller is designed for a set of actual systems. The performance is simulated and experimentally studied. The research results show that the active electromagnetic suspension ball system based on PID controller has high anti-interference performance and stability.