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In this paper, the method of system identification and predictive control is used to control the stress of steam turbine rotor. Firstly, the three-dimensional model of turbine rotor is established by using finite element simulation software, and then the results are identified by using ANSYS finite element analysis software. The transfer function is obtained. Firstly, the temperature model is identified with an accuracy of 99.73%, and then the stress model is identified with an accuracy of 98.2% $. Then the transfer function is discretized, and the data is input into the discrete transfer function to verify the accuracy of system identification. After the accurate verification of the transfer function, the transfer function is transformed into the dynamic matrix control model, and then the stress feedback controller is designed to realize the model predictive control (MPC) of the rotor stress. At the same time, the output response of the system before and after the addition of the controller is compared. Finally, the rationality of the control model is verified. Future work will concentrate on more complex working conditions.

Rolling bearing is a common rotating machine in industry. Once it is damaged, the industrial machinery associated with it will be affected, and if it is serious, it may threaten life safety. Thus, an effective fault diagnosis method can reduce the occurrence of accidents. In the light of the principle of Manhattan distance and symmetrized dot pattern (SDP), the Manhattan distance is improved and a new variable is obtained. It is used as characteristic parameter to diagnose fault type. Firstly, sample data of rolling bearing under various working conditions is extracted, and it’s split into 10 pieces of data. Then, through the SDP principle, the equal part of the data is became a picture, and the symmetrical image in polar coordinate system is obtained. After binarization of the SDP image, the local area of the binarized SDP image is selected, and the mean value matrix of the local matrix is computed, and salt and pepper denoising is carried out for each local matrix and mean value matrix, and the maximum characteristic value of average value array after salt and pepper denoising is computed. Finally, local images are corresponded with their average images one by one, and the Manhattan distance between them is calculated. According to the feature method, the improved new value is obtained by linear transformation, which is called the improved Manhattan distance, and then the average value is obtained. Through the experimental data to verify whether the method can effectively distinguish the fault type.

This design is according to the requirements of the wide rider, and the Multi-function bike computer has the function of velocity measurement, display, timing, lighting, buzzer warning, which could satisfy the requirements of cycling enthusiasts. This design is based on STC89C52 SCM (Single Chip Microcomputer) smallest system and uses hall sensor receives the bicycle wheel rotation numbers date, and then transmit the received data into the SCM. After the SCM’s calculation and processing, it gets the bicycle’s instantaneous speed, mileage, riding time through the LED display the date. The part of software programming use C language, which compiles and downloads by the Keil and STC-ISP. This design uses Proteus to make the circuit diagram drawing and system simulation. The overall program adopted modular approach, and each module has a special function. The idea which makes the program more clear, is advantageous to the optimization of code logic and modification.