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In order to effectively capture the fault features contained in the vibration signals of the rotating machinery and complete the fault diagnosis task efficiently, a fault diagnosis model combining two-dimensional image features and lightweight neural network is designed. Firstly, the collected one-dimensional vibration signals are decomposed by modified ensemble empirical mode decomposition (MEEMD) to obtain the intrinsic mode function (IMF) components, and the corresponding IMF components are selected for sum reconstruction to enhance the amplitude fluctuation of vibration signals. Then, Markov transition field (MTF) could be used to more effectively characterize the fault features in the reconstructed signals. Secondly, the 2D feature map generated by MTF is input into residual depth separable convolutional neural network (ResDSCNN) for feature extraction and fault diagnosis. The planetary gearbox fault data set is used to verify the performance of the model, and the results show that the diagnosis accuracy of the model for all kinds of gear faults can reach more than 98%.

In order to solve the difficulty in fault feature extraction of the planetary gearbox vibration signal,a comprehensive fault diagnosis method based on empirical mode decomposition（ EMD）,singularity value decomposition（ SVD） and probabilistic neural networks（ PNN） is proposed. Firstly,with the EMD method,de-noised vibration signals are decomposed into a finite number of intrinsic mode functions （IMF）. Secondly,some IMF components are selected according to the criterion of correlation coefficient and variance contribution ratio,and singular value sequences regarded as eigenvectors are obtained with the method of SVD. Lastly,the eigenvectors serve as import of PNN so that faults of the planetary gearbox are recognized. Experiments are conducted on the planetary gearbox fault diagnosis test-bed and comparison is carried out with eigenvectors made of energy entropy,which fully validates the effectiveness of the proposed methodology.

Aiming at the major difficulties of OMA method based on vibration signals such as low noise-robust ability,high subjective experience and poor accuracy in identifying parameters and more complex recognition method,a new method of OMA on the basis of QICA is studied.Through the LMS modal experimental data algorithm analysis,the QICA modal parameter identification technology operates simply,and can effectively eliminate noise interference.Finally,regards taking of gearbox modal frequency of the five kinds of working condition recognized by the QICA as characteristic parameter of fault diagnosis,the changes of any kind of model frequency on gearbox fault working condition are compared and analyzed,and the goal of the fault identification is achieved.

A study on the issue that the forward kinematics and workspace of a manipulator with 8--DOF is carried out.Firstly,according to the structural characteristics of the manipulator,the link coordinate system of manipulator are established by using D-H method,and its forward kinematics is obtained.Secondly,the simulation model of manipulator is set up by using the MATLAB Robotics Toolbox,and the correctness of the kinematics forward solution is testified.Finally,according to the position vector of the end actuator,the Monte Carlo method is applied to analyze the manipulator workspace,and the workspace range of the manipulator end actuator is acquired,and it is consistent with the design parameters of the manipulator.

Abstract Background China has witnessed a remarkable increase in sexually transmitted infections (STIs) and HIV. The study is to assess the prevalence of HIV, HBV, HCV and syphilis and related risk factors among drug users in mandatory detoxification center Qingyuan, Guangdong, China. Method A cross-sectional study on drug use behaviors, sex behaviors, and presence of antibodies to HIV, HCV, Treponema pallidum, and surface antigen of HBV (HBsAg) was conducted among drug users recruited from 3 detoxification centers in Qingyuan, Guangdong, China. Risk factors for each of four infections were analyzed with logistic regression model. Results A total of 740 subjects were recruited, the median age was 31 years old (range 24-38). The seroprevalence rates of HIV, HBsAg, HCV and syphilis were 4.6%, 19.3%, 71.6% and 12.6%, respectively. Risk factors for HIV were intravenous drug use and co-infection with syphilis. Having a regular sexual partner who was a drug user was considered to be a risk factor for HBV. Intravenous drug use was a risk factor for HCV. However, the consistent use of condoms with commercial sex partners was protective for HCV infection. Compared to drug users living in urban area, those living in rural areas were more likely to be infected with syphilis, and there was an association between commercial sex and syphilis. Conclusion The prevalence of HIV, HBV, HCV and syphilis were high among drug users in detoxification centers in Qingyuan, thus, risk reduction programs for the drug user population is urgently required.