Research On Key Techniques Of Radio Resource Allocation In Relay-enhanced Cellular Networks

Trackless equipment are the major operating equipment in the mine, they are special vehicles which can adapt to bad working environment in the assignment of task. However, the intellectualization and automation level of trackless equipment, which is researched and produced by domestic corporations, is still relatively low. Especially the intelligent condition monitoring and fault diagnosis ability is very weak in the process of equipment operation. For a long time, the maintenance mode of trackless equipment is based on the principle of mainly periodical inspection and breakdown maintenance, and supplemented by the experience of the technical personnel for equipment maintenance and troubleshooting. Therefore, it has very import significance for the trackless equipment to research and to establish a reliable and effective equipment condition monitoring and fault diagnosis system. In this thesis, embarking from the practical engineering, designed and implemented a system for underground trackless equipment status inspection and fault diagnosis, according to the company mainly trackless equipment.Firstly, it introduces the main structure of trackless equipment and components, and analyzes the main types of trackless equipment common failure and mechanism in this article. A brief overview of the working condition of the selection of parameters combining with structural characteristics and failure characteristics of trackless equipment. Then putting forward the general software architecture and design scheme of the system.Secondly in view of the working condition of equipment parameter detection and feature extraction problem, proposing a distributed data acquisition system based on CAN bus. It’s aimed to choose the acquisition sensor module according to the different equipment need. Considering the characteristic parameter of different working conditions, use different feature extraction methods. The way to extract fault features of the instantaneous parameters, such as temperature and pressure, mainly based on threshold criterion. Because it can provide condition assessment and alarm information in time. Due to the engine as the core component and fault characteristic is more complex, most of the fault are reflected by the vibration signal. Therefore proposed a method to extract the engine fault vibration signal characteristics based on EMD approximate entropy algorithms, which can effectively reduce the noise interference, and extraction of fault feature more accurately.Thirdly, according to the technical requirements and characteristics of trackless equipment. The method based on information fusion technology was adopted to realize fault diagnosis, in order to the operation condition of the comprehensive analysis of all parameters. To extract the characteristic parameter of all conditions by signal processing method, then the information fusion method was used to construct feature vectors. To implement fault diagnosis and decision making by using the fault characteristic vectors as learning and testing samples for LSSVM algorithms. This method can improve the fault diagnosis accuracy and reliability of the fault diagnosis system.Lastly, the underground trackless equipment condition monitoring and fault diagnosis system was designed and implemented according to the system performance and requirements. The System includes the condition monitoring and alarm module, data processing and fault diagnosis module, CANOpen protocol test module, area alarm system setting and debugging module, operating conditions and the setting of the basic information of the channel, the work interface instrument display mode Settings module. Elaborating the system software structure and the development process in the configuration system, the parameter detection system, the state monitoring and fault diagnosis system four aspects.