The high-speed maglev train adopts active control through the suspension unit to maintain the specified distance above the track. In the presence of flexible track conditions, even if the train is not running, the train-track coupling vibration may occur. The research focuses on analyzing the coupled vibration of the train-track system under elastic track conditions, establishing a simplified and reasonable model of the coupled vibration, and devising a controller with pre-compensation and full-state feedback to ensure closed-loop stability and accurate reference tracking. The research further investigates the impact of various factors on the coupled vibration by modifying key parameters for obtaining root locus plots. Numerical simulation analysis is employed to assess the influence of control parameters on the coupled vibration. Based on the law of the influence of the control parameters, the suppression of the coupled vibration of the system was successfully achieved by adjusting the control parameters in the event of vehicle-track parameter mismatch. Finally, experiments were conducted on the Qingdao high-speed maglev train to verify the effectiveness of the proposed model and full-state feedback control algorithm.

The electromagnet module, as a fundamental component providing levitation force for maglev trains, plays a crucial role in ensuring the stability of train operation. However, vibrations can easily occur due to the mechanical coupling between the two suspension points of the electromagnet module. To reveal the inherent instability of the system and the coupling relationship between the state variables, a state-space equation that considers the mechanical coupling between the two suspension points is established. Furthermore, a differential control algorithm based on geometric feature transformation is proposed to mitigate the structural coupling vibration. Simulation experiments are conducted to compare the dynamic characteristics of the system before and after implementing the improvement algorithm under complex conditions. At the same time, the influence of control parameters on electromagnetic vibration was analyzed, focusing particularly on vibrations resulting from parameter mismatch, offering crucial insights for enhancing system stability. Additionally, suspension tests are carried out on the high-speed double bogie test platform in the Key Laboratory of Hunan Province to further validate the effectiveness of the proposed algorithm. The proposed control framework is both effective and concise, making it easy to implement in engineering applications. This research holds significant practical value in improving the stability of maglev trains.

With the increasing development of industrial technology, fault detection technology has been widely applied in many complex systems. Aiming at the problems of multi-operation conditions and the data length needed for fault detection using Kullback- Leibler Divergence (KLD), a novel fault detection method for complex systems based on optimized KLD under multi-operation conditions is proposed in this paper. In the first place, based on the historical data of the system, the complex operation conditions of the system are divided into several simple mutually exclusive operation conditions, and the division standard is established. In the next place, the optimal length can be quickly determined by autocorrelation analysis and applied to various operation conditions. After the next, in the training stage, the KLD values of all training data are calculated with the benchmark that is the initial optimal data length of each operation condition. And the maximum value is taken as the threshold for fault detection. Afterward, in the test phase, it starts by judging the type of operation condition that the data belongs to, then the corresponding KLD value is calculated, which is compared with the corresponding threshold, so as to determine whether the fault occurs. Eventually, this method is applied to the suspension system of the maglev train and respectively compared with the fault detection methods based on Euclidean distance or Mahalanobis distance. The results show that the proposed method possesses a low false alarm rate and high sensitivity.

In order to meet the technical requirements of non-destructive measurement for the internal stress of ferromagnetic materials represented by cold-rolled steel sheets during the rolling control process, the paper presents a novel method for the nondestructive measurement of ferromagnetic materials based on inverse magnetostrictive principle. By improving the traditional U-shaped sensor, a transmissive quadrapole layout is proposed. The corresponding excitation module and fast signal processing system for dynamic measurement were developed and the test system for detecting innerstress of ferromagnetic material was constructed in the laboratory. The relationship between the magnetic flux with the principal stress was found by experimental investigation and the sensitive correlation of the two was verified under the laboratory measurement conditions without strong electromagnetic interference. The influence of measurement results by sensor parameters such as sensor angle, amplitude of excitation current, variation of air gap were discussed in detail and a method was proposed to decrease the power supply instability caused by the change of the airgap. The experimental results show that the transmission quadrupole layout makes the test system exhibit a good linear response to the internal stress in the specimen. The feasibility of the magnetic detection method of internal stresses in ferromagnetic material was verified through the experiment.

Based on the research background of shallow-ground metal detection under the environment with the interference of steeling structure,a metal detection method based on the transient electromagnetic method is studied. The test platform is built and verified by experiments. Firstly, the loos model of the coil system is established, and the transfer function with the excitation voltage source as the input signal and the induced voltage of the metal conductor as the output signal is derived. The system design of metal detection based on pulse electromagnetic induction is carried out, and a simple and feasible experimental platform is built according to the demand. Aiming at the signal processing problem of metal detection in a complex environment such as steel structure, a detection algorithm based on a hypersphere model and confidence interval is applied. Finally, the experimental verification is carried out. The results show that the designed system is feasible for metal detection, and the applied signal processing algorithm is effective although interfering by steeling structure inthe environment.