Under extreme working conditions such as high-speed driving on roads with a large road surface unevenness coefficient, turning on a road with a low road surface adhesion coefficient, and emergency acceleration and braking, a vehicle’s stability deteriorates sharply and reduces ride comfort. There is extensive existing research on vehicle active suspension control, trajectory tracking, and control methods. However, most of these studies focus on conventional operating conditions, while vehicle stability analysis under extreme operating conditions is much less studied. In order to improve the stability of the whole vehicle under extreme operating conditions, this paper investigates the stability of a vehicle under extreme operating conditions based on linear quadratic regulator (LQR) control. First, a seven degrees of freedom (7-DOF) dynamics model of the whole vehicle is established based on the use of electromagnetic active suspension, and then an LQR controller of the electromagnetic active suspension is designed. A joint simulation platform incorporating MATLAB and CarSim was built, and the CarSim model is verified by real vehicle tests. Finally, the stability of the vehicle under four different ultimate operating conditions was analyzed. The simulation results show that the root mean square (RMS) values of body droop acceleration and pitch angle acceleration are improved by 57.48% and 28.81%, respectively, under high-speed driving conditions on Class C roads. Under the double-shift condition with a low adhesion coefficient, the RMS values of body droop acceleration, pitch acceleration, and roll angle acceleration are improved by 58.25%, 55.41%, and 31.39%, respectively. These results indicate that electromagnetic active suspension can significantly improve vehicle stability and reduce driving risk under extreme working conditions when combined with an LQR controller.

Traditional multipoint hybrid maglev systems cannot realize the zero-power horizontal levitation state under eccentric load, which means the steady-state current is zero, and the air gap of each magnet is equal. This article proposes a new maglev system with two rotary magnets to solve this problem. The two magnets can move on an annular guideway to counteract the moment caused by the eccentric load. At first, we established the linear parameter varying model of this maglev system. Then, the control strategy is designed, which consists of a zero-power horizontal levitation controller and rotation controller. The levitation control is realized by introducing feedback of integral current and deviation of air gap in the proportional-derivative control system. The rotation controller employs a centroid observer and centroid Jacobian. It outputs the rotating angle at the demand of the zero-power and horizontal levitation. Experimental results demonstrate that the proposed maglev system has a better zero power and horizontal levitation performance than without rotary magnets. The direction of the air gap variation is opposite to that of the loading direction. The variation size of air gap length depends on the load weight, whereas rotation angle is related to the load weight and loading position.

The method of acoustic radiation signal detection not only enables contactless measurement but also provides comprehensive state information during equipment operation. This paper proposes an enhanced feature extraction network (EFEN) for fault diagnosis of rolling bearings based on acoustic signal feature learning. The EFEN network comprises four main components: the data preprocessing module, the information feature selection module (IFSM), the channel attention mechanism module (CAMM), and the convolutional neural network module (CNNM). Firstly, the one-dimensional acoustic signal is transformed into a two-dimensional grayscale image. Then, IFSM utilizes three different-sized convolution filters to process input image data and fuse and assign weights to feature information that can attenuate noise while highlighting effective fault information. Next, a channel attention mechanism module is introduced to assign weights to each channel. Finally, the convolutional neural network (CNN) fault diagnosis module is employed for accurate classification of rolling bearing faults. Experimental results demonstrate that the EFEN network achieves high accuracy in fault diagnosis and effectively detects rolling bearing faults based on acoustic signals. The proposed method achieves an accuracy of 98.52%, surpassing other methods in terms of performance. In comparative analysis of antinoise experiments, the average accuracy remains remarkably high at 96.62%, accompanied by a significantly reduced average iteration time of only 0.25 s. Furthermore, comparative analysis confirms that the proposed algorithm exhibits excellent accuracy and resistance against noise.

This paper proposes an electromagnetic actuator that concurrently realizes two working functions of vibration suppression and energy regeneration. The actuator consists of four permanent magnetic rings, three soft iron rings, three coils, and three springs. The design of the electromagnetic actuator is based on finite element method (FEM) analysis, and the prototype is based on this analysis. Based on the prototype, the characteristics of the electromagnetic actuator, which has an output force–current coefficient of 39.49 N/A, are explored. A control algorithm with a position controller and an acceleration controller are applied to the actuator. When an impulse excitation is input to the electromagnetic actuator, the acceleration of the controlled object decreases from 114.26 m/s² to 3.14 m/s² here. Moreover, when the sinusoidal excitation with a 3 mm amplitude and 5 Hz frequency is input to the electromagnetic actuator, the vibration amplitude of the controlled object is 0.045 mm, suppressed within 1.46% when compared with the input signal. The peak value of the regenerated electromotive force is 1.97 V here, and the actuator efficiency for regenerating energy is 11.59%. The experimental results with multiple frequencies and amplitudes also show that the amplitude of the controlled object can be suppressed within 5.5%, and that the ratio of the electromotive force (EMF) to the input amplitude is 0.13. The results indicate that this electromagnetic actuator can suppress vibrations effectively and regenerate energy from vibrations.

A control valve is one of the most widely used machines in hydraulic systems. However, it often works in harsh environments and failure occurs from time to