The vibration and noise of mechanical structures caused by transmission system is one of the key problems that need to be solved in the research of high-speed mechanical equipment. This study investigates the location and optimization of vibration and noise reduction of a high speed packaging machine transmission system based on coupling vibration and noise experiment and simulation analysis. The vibration and noise test device and the rigid-flexible coupling dynamic simulation model of the high-speed packaging machine's transfer mechanism and its transmission system is established. Based on the experimental data，the load identification is carried out and the accuracy and reliability of the model is verified. Based on the model，combined with modal participation factor and acoustic contribution analysis method，the modal frequency and plate area with large acoustic contribution of the high-speed packaging machine transmission system are analyzed，which improves and optimizes the design of the transmission system. The results show that modal contribution analysis and plate contribution analysis can locate the noise problem area quickly and accurately to serve the optimization of the corresponding mechanical structure design. The vibration and noise performance of the optimized high speed packaging machine drive system is improved significantly.

The additional tuning mass damper is a traditional control technique for the chimney，but it usually requires a large additional tuning mass and auxiliary installation space，which brings inconvenience to the construction and installation. This study proposes utilizing the additional tuned mass inerter system （TMIS） to reduce seismic responses of the chimney. The apparent mass effect of the inerter is employed to achieve the goal of lightweight control. Meanwhile，considering that the influence of high-order modes of the high-rise chimney on its seismic responses cannot be ignored，the distributed TMISs arranged along the height of the chimney are proposed to achieve the multimode control effect. Mechanical models of the TMISs based on two different inerter subsystems are established，and the equations of motion for the chimney with corresponding additional distributed TMISs are established. Taking Kanai-Tajimi’s spectrum as the random seismic excitation input and based on the extended fixed-point theory，the simplified assumptions for part of the design parameters of distributed TMISs are proposed. The demand-oriented multimode optimization design method for the chimney with distributed TMISs is presented. The effectiveness of the proposed design method is verified by a design case. The lightweight and multimode control effects of additional distributed TMISs are examined by comparative analyses. The rationality of the simplification based on the extended fixed-point theory is verified through parameter analysis. The results show that the proposed design method can achieve the expected target performance using the two distributed TMISs. Both the two distributed TMISs behave obvious lightweight control effect.

The fault diagnosis method based on deep learning is widely used in the fault diagnosis of key mechanical components represented by bearings. The premise of achieving ideal results is that there are enough fault samples and the training set and test set meet the same distribution requirements. However，the data distribution will change under the actual working conditions，which makes it difficult to apply the diagnostic model under the original working conditions to the new working conditions. For this reason，the domain adaptation transfer learning method is used to solve the problem of different distribution of training sets and test sets，and its key point is to achieve data distribution adaptation，that is，to measure data distribution differences and use the measurement results to guide model training，which can effectively improve learning efficiency and diagnostic accuracy. On this basis，this paper proposes a new domain adaptation method based on adversarial learning. The core of this method is to combine the proposed exponential adjustment strategy with adversarial network to make the network adapt to different data distribution in source domain and target domain more specifically in the process of fault diagnosis. The network consists of a feature extractor，a classifier，a global domain discriminator，and multiple local domain discriminators，and the model is optimized by using the adversarial strategy and adaptive moment estimation algorithm，and adjusted the importance of marginal distribution and conditional distribution by using the exponential adaptive factor set based on the exponential adjustment strategy，so that the model could diagnose faults stably and efficiently. The proposed method is verified in bearing diagnosis cases of cross-speed，cross-load and simultaneous cross-speed load. The results show that the method in this paper is better than other domain adaptation methods in diagnosis effect and has better stability.

Compressed sensing can effectively relieve the burden of data storage and transmission for mechanical condition monitoring. However，this method exists some problems such as low compression efficiency and slow signal reconstruction process in the application of fault diagnosis. In this paper，using the corresponding relationship between autoencoder and compressed sensing，a novel fault feature extraction method of the rolling bearing in the compressed domain based on the deep convolutional measurement network is proposed. For the problem that noise-free fault signal samples are difficult to obtain，a dataset construction method based on the fault mechanism is proposed. The model trained on this dataset is suitable for bearing signals under different working conditions A deep convolutional denoising autoencoder （DCDAE） is constructed，in which the number of layers is determined by the required signal compression rate and the frequency of the hidden layer corresponds to that of the original signal. The fully trained encoding sub-network of DCDAE，named deep convolutional measurement network （DCMN），is used to compress the rolling bearing vibration signal instead of the traditional measurement matrix，and then the fault features are directly extracted in the compressed domain. The effectiveness of the proposed dataset construction method and the compressed domain feature extraction method are analyzed through the simulations. The rolling bearing experimental signals further verify that the deep convolutional measurement network trained by the proposed method has good generalization and can effectively extract fault features for fault diagnosis in the compressed domain with a compression ratio far lower than that of the traditional compressed sensing method.

A nonlinear two-degree-of-freedom system is used to construct an aircraft landing gear model. The stochastic excitation of the uneven runway to the system is described by time-domain noise，and the road roughness coefficient is used to describe the roughness of runway. Based on the probability density function and the statistics of system response，the influence of uneven runway on aircraft landing gear system is investigated. The reliability of the landing gear model and the passenger comfort under different road roughness coefficients are analyzed by establishing the relationship between the safety zones，comfort zones and the system response. The results show that the larger the road roughness coefficient is，the more fluctuation of the system state variable will be. The reliability and comfort of the system are negatively correlated with the road roughness coefficient. In addition，when the road roughness coefficient is small，the mean first-passage time and comfort of the system are more significantly affected by random disturbance. The present paper provides a theoretical basis for aircraft riding comfort and landing gear development and design.

In order to satisfy both cushioning and fast-extension performances，the carrier aircraft nose landing gear often adopts the dual-chamber buffer design. Based on a certain type nose landing gear，this paper establishes the dynamic model of the cushioning performance analysis and compares the simulation calculation results with the test results to ensure the validity and correctness of the theoretical dynamic model. The parameter sensitivity analysis of cushioning performance is carried out for the initial filling pressure and volume ratio of the high- and low-pressure chambers of the buffer. Results show that the effects of the initial filling pressure and high- and low-pressure chambers volume ratios on the cushioning performance is different from their impacts on the fast-extension performance. Therefore，the design of the nose landing gear buffer of the carrier aircraft needs to be continuously optimized for taking the cushioning and fast-extension performances into account synchronously.

Considering the low accuracy problem of complex dynamic load identification under the effect of real measurement noise，an L1 norm regularized load identification method based on redundant extended cosine transform dictionary is proposed. According to the convolutional relationship between the system response and the external load，the discrete system control equation for load identification is established. According to the main characteristics of the vibration response signal，appropriate discrete cosine basis functions are selected and extended，and the extended cosine dictionary and the Db10 wavelet dictionary are used to cascade a redundant extended dictionary to represent the complex load sparsely. By using the L1 norm regularization method to solve the sparse representation vector under the proposed redundant extended cosine transform dictionary，the optimal regularization parameter is obtained by improved L curve criterion，and the identification of beat load and repetitive impact load at different noise levels is realized. The experimental verification results show that the constructed redundant extended cosine transform dictionary has much better performance in sparse representation of beat load and repetitive impact load，and the load identification method based on the redundant extended cosine transform dictionary has great advantages to obtain accurate inversion results and good robustness.

Multi-axial stationary non-Gaussian random vibration control tests can simultaneously control the time-frequency characteristics of the specified response signals. A fast method for generating stationary non-Gaussian random vibration signals with specified power spectral density，skewness and kurtosis is proposed. The target power spectral density is designed as a filter by frequency sampling method. The non-Gaussian random signal is obtained by nonlinear transformation method and then it passes through the designed filter to obtain the desired non-Gaussian random signals. This method is computationally efficient and overcomes the shortcoming of the traditional nonlinear transformation methods. This proposed method is applied to the three-axis stationary non-Gaussian random vibration test，and the closed-loop equalization step of the three-axis non-Gaussian random vibration control is given，which can decouple the power spectrum auto spectrum，coherence coefficient，phase difference，skewness and kurtosis of the signal at the same time. A three-axis stationary non-Gaussian random vibration control test is carried out. The control results of power spectral density，skewness and kurtosis in three directions are satisfactory which meet the requirements of engineering application.

To support the rapid and high-efficient elimination of the vibration fault of complex systems，this paper presents a quantitative method of vibration failure mode，which can realize the key verification of the vibration failure mode based on quantifiable value. The technical characteristics of the vibration fault tree analysis is discussed，which points out that fault tree analysis is not suitable for the vibration fault of complex systems. The quantification method of vibration failure mode is proposed，and dimensions of fault mode，like the probability of failure mode and the verifiability，are used to quantify. The vibration fault of the core machine of an aviation engine is introduced，and the specific application of the method of vibration failure mode is given. It proves that the quantization method of vibration fault mode has the availability，high efficiency，and the important value of the engineering application.

In order to meet the requirements of high transmission efficiency and low noise in the planetary gear transmission system，the bending-torsion-shaft coupling power of helical planetary gear transmission system was established by employing the lumped parameter method，with considerations for eccentricity error and installation error. Using the fourth-order Runge-Kutta method to solve the dynamic equation，the dynamic characteristics of the planetary gear，such as the dynamic meshing force，dynamic transmission error，and dynamic load coefficient，were obtained. Based on this，the planetary gear modification research is carried out，and the tooth profile modification is established. Upon establishing the tooth surface equation and the dynamic equation that takes tooth surface modification into account，an analysis was conducted on the dynamic characteristics corresponding to varying degrees of modification. The research results show that with the increase of the modification amount，the dynamic meshing force，dynamic transmission error and dynamic load coefficient of the planetary gear all decrease to varying degrees，and then increase after reaching the lowest value. Performance testing of the entire machine revealed a reduction in vibration and noise of the planetary reducer，as well as an improvement in transmission performance providing theoretical support for the design of vibration reduction，noise reduction and transmission efficiency improvement of the planetary gear transmission system.