The current vibration detection system has the problems of large delay, poor controllability of sensor network and low detection precision. In order to solve the problems, a new vibration detection system based on Process field bus (Profibus) technology was proposed. Firstly, the complex calculations such as Kalman filtering and Fast Fourier Transform (FFT) were implemented at each node of detection device. The network load was reduced by about 95% compared with the traditional scheme of transmitting large amount of original vibration data, the network transmission time and the computing time of workstation were shortened, the real-time performance and computing power of system were improved. Secondly, the Profibus protocol was used to realize the management and data transmission of the vibration detection device network, which ensured the stability and controllability of the sensor network. In addition, the high-precision vibration sensor was used on the vibration detection node device, and the vibration data was filtered on the detection node. The detection precision was up to 0.0039 mg. What's more, the function customization and secondary development of detection device were convenient by the independent design and development of the Profibus protocol slave station. The RT-Thread embedded system kernel was used to achieve resource allocation and task scheduling on the detection node device, and the real-time performance and reliability were improved. The experimental results show that, the proposed system can process the original vibration data of the vibration field quickly, and transmit the processed data to the workstation computer with high real-time performance. Meanwhile, the Profibus network composed of vibration detection devices can display the status information of node in real-time and remind users promptly when there is a network fault. The network has good controllability.

The recent Risk Minimization based Ontology Mapping (RiMOM), based on multi-strategy, in spite of certain improvements, has the shortcomings in complexity and redundancy, and limitations in similarity selection of structure characteristics. Therefore, a comprehensive method of computing ontology similarity based on association rules was proposed. First, the "tree" model of association rules was constructed to compute the structure similarity. Then the similarity of instances, properties, and names were also computed. Finally, a comprehensive method was used to achieve the optimal calculation of the ontology similarity. The experimental results show that this method has higher recall and precision ratio and less time complexity than RiMOM.