To investigate the data-driven evolution of rail corrugation in subways，a spatio-temporally dense measurement method is proposed for rapid detection. First，high-precision sensors are used to measure car-body vibration，collecting triaxial acceleration data. Second，vehicle speed and mileage position are estimated by fusing triaxial vibration acceleration from different car bodies. Then，the vertical vibration acceleration of the car body is decomposed via wavelet packet analysis，and a vibration energy ratio index is defined as the ratio of the energy in the characteristic frequency band excited by rail corrugation wavelength to the total vibration energy. The vibration energy ratio threshold is set to automatically identify rail corrugation and output mileage information. Finally，the corrugation wavelength is derived from the ratio of vehicle speed to characteristic frequency，and the relationship between vibration energy ratio and corrugation amplitude is analyzed. Results show that using a vibration energy ratio threshold of 0.2 yields corrugation mileage distribution consistent with that from noise-based identification，and the calculated corrugation wavelength matches the measured value of 175 mm from a corrugation analyzer. Statistical clustering reveals that the relationship between rail corrugation amplitude and vibration energy ratio is not purely linear. Line-wide rapid detection shows that non-corrugated sections account for 76.56% of the track，while corrugation sections account for 23.44%. Among the corrugated sections，those with wavelengths below 60 mm dominate （77.88%），whereas those above 60 mm account for 22.12%.

In view of the current situation that the detection indices of maglev train boot rail system both domestically and internationally are relatively limited，and there is a lack of detection data within the 100—140 km/h speed range，a comprehensive detection framework for high-speed maglev train boot rail system is proposed. Firstly，in accordance with relevant standards and specifications of pantograph and conductor rail system testing，a detection method is developed，which includes conductor rail contact force，vibration acceleration，electric shoe current，arc combustion and transverse geometric parameters of the conductor rail. Secondly，a real-time side conductor rail monitoring system that combines video surveillance and data statistical analysis is proposed，and a supporting program for extraction，processing and analysis of original detection data is developed. Finally，a medium- and low-speed maglev line is taken as the test object，and the data measured at different speed levels of the maglev train are analyzed. The results show that the dynamic performance of the pantograph differs during the upward and downward runs of the maglev train； The contact force and vibration degree at the expansion joint are larger than those in the middle section，indicating poorer dynamic performance of the conductor rail. Toe vibration mainly comes from vertical vibration. Relevant studies reveal the characteristics and issues of the maglev train boot rail system under different working conditions，providing theoretical support and practical basis for the further optimization of the maglev train boot rail system.

When early failures occur in planetary gearboxes，the weak fault features are difficult to extract and identify due to the interference of background noise in industrial environments and the attenuation of fault impacts in complex transmission paths. To address this issue，a sparse-guided improved empirical wavelet transform （IEWT） is proposed combined with multipoint optimal minimum entropy deconvolution adjusted （MOMEDA） method for weak fault feature extraction. Firstly，a new fault composite index （FCI） is introduced，and the original signal is adaptively decomposed into a set of IEWT components based on the amplitude envelope of the signal spectrum. Secondly，the sensitive components，selected through the sparse-guided method，are used as the sparse representation of the original weak fault signal. Finally，the MOMEDA technique is applied to the sensitive component signals to reduce signal noise and extract the weak fault feature frequencies for identification. The effectiveness of the proposed method is validated through simulations and experiments，successfully extracting and identifying the weak fault features of planetary gearboxes. This demonstrates that the method has good diagnostic performance for noisy，non-stationary，and non-linear fault signals in planetary gearboxes，providing a new approach for the diagnosis and identification of weak faults in engineering practice.

Aiming at the chatter problem in the process of milling screw rotors，a chatter monitoring method based on RelifF algorithm to the least square support vector machine （RF-LSSVM） is proposed. Firstly，the vibration signals in the milling process of the screw rotor are decomposed，and feature extraction and selection are performed using the variational modal （VMD） and the RelifF algorithm. Secondly，the penalty factor，kernel parameter，the number of near neighbor samples of RelifF algorithm and the length of dimension reduction feature of LSSVM are iteratively optimized using the enhanced whale optimization algorithm （E-WOA）. Finally，a flutter identification model is established by inputting the reduced-dimensional flutter eigenvector matrix and outputting the flutter occurrence state. The experimental results show that the proposed VMD-RF-LSSVM model has a higher recognition accuracy than the unoptimized variational modal decomposition-support vector machine （VMD-SVM） model，reaching 99.5% accuracy. The proposed method can effectively monitor the chatter problem in the screw milling process，provides a thought for the optimization of the screw milling processing.

The theoretical framework of Hertzian contact model for soft sphere collisions is well-established； however limited research has been done on the collision between rigid particles and elastoplastic materials. In this study，a novel damping form is introduced by incorporating the elastoplastic half-space contact constitutive relationship，and a viscoelastoplastic collision model between hard spherical particles and polyurethane surfaces is established. The nonlinear dynamic equations for particle and elastoplastic surface collision are derived. Furthermore，by conducting experiments to measure the coefficient of restitution for coal pellets colliding with polyurethane，the Meyer's index of the polyurethane material and the damping coefficient in the dynamic equations are determined. Additionally，the correctness of the damping model proposed in this study is validated by considering different damping forms in the equations. The changes in contact force at different stages are analyzed under various damping coefficients. The variations of displacement，velocity，and contact force during the collision process between particles and elastoplastic polyurethane are investigated. The results reveal that with an increase in the initial collision velocity，the irreversible plastic deformation of polyurethane increases from 1.636×10^-4 m to 5.657×10^-4 m，the coefficient of restitution decreases from 0.583 2 to 0.501 2，the collision time decreases from 6.963×10^-4 s to 4.737×10^-4 s，and the proportion of plastic compression stage decreases from 59.81% to 59.04%.The model established in this paper can be used in scenarios where particles collide with softer planes，such as the separation of metal ores and particle dampers. This paper provides theoretical support for the transportation，collision，impact，and separation of particle systems.

Quasi-zero stiffness （QZS） vibration isolation，by introducing stiffness nonlinearity，effectively addresses the inherent contradiction between load-bearing capacity and isolation bandwidth in conventional linear isolators. As a result，it exhibits superior low-frequency isolation performance. The core challenge in realizing QZS isolation lies in designing mechanical structures whose force-displacement curves simultaneously demonstrate high static stiffness and low dynamic stiffness. Focusing on QZS isolation design methodologies，this paper first outlines the fundamental principles of QZS isolation and categorizes the traditional approaches according to the means of stiffness nonlinearization into four groups： geometric motion nonlinearity，geometric deformation nonlinearity，magnetic nonlinearity，and stress-strain nonlinearity. Subsequently，it introduces emerging design strategies based on nonlinear positive-stiffness structures，including hardening and softening types，and compares them with conventional approaches，with particular attention to their differences in static and dynamic behavior. Finally，the paper summarizes and discusses future directions from the perspectives of negative-stiffness structure design，QZS characteristic tuning，and potential applications，aiming to provide a comprehensive overview of the latest research progress and to offer insights into future development trends of QZS isolation systems.