The Realization Of Online Monitoring System For Mine Hoist’s Operative State

As one of the key mechanical and electrical equipments, mine hoist is often called the "throat of the mine". The proper operation of mine hoist not only related to the exploitation and transportation of coal, but also seriously affects the safety and life of the mine workers. In recent years, the fault of mine hoist has already caused a lot of serious accidents. Therefore, it has great practical significance to complete the online monitoring and fault diagnosis for mine hoist’s operative state.Based on the analysis of current state of remote monitoring and fault diagnosis technology home and abroad, this thesis confirms the research direction of online monitoring for the mine hoist’s operative stare which is based on Ethernet communication technology. Based on the study of neural network theories, it also puts forward to finishing the design methods for mine hoist’s fault diagnosis by SOM neural network model. By establishing SOM neural network model, the author finished the training of mine hoist’fault sample and the emulation of on-site number, which proves the feasibility and rationality of the application of SOM neural network into fault diagnosis of mine hoist. The main tasks of this thesis are:1. By introducing the background and significance of on-line monitoring and fault diagnosis for Mine Hoist’s Operative State, this thesis summarizes the research status of this subject from the perspectives of remote monitoring technology and neural network fault diagnosis technology. It also puts forward the online monitoring system for mine hoist’s operative state based on Ethernet technology.2. This thesis studies working principles and failure characteristics of mine hoist. By the analysis and summary of various reasons and correlations of mine hoist’s faults, the author lists some monitoring and diagnosis methods of fault features and further establishes the systems’hardware and software platforms.3. The author achieved the installation and debugging of the online monitoring system for Mine Hoist’s Operative State in Cheng zhuang Mine and summarized the stability and reliability of the system in running test. The author made a breakthrough of applying theoretical study to actual production during graduate student period.4. This thesis studies the application of neural network technology into fault diagnosis, introduces SOM neural network model and proved the feasibility of applying it to the fault diagnosis of mine hoist’s shake and brake systems by experiment. It also establishes the SOM neural network model in MATLAB, trains fault feature numbers according to the relationship of fault features offered by Cheng kuang Mine and completes the function of fault diagnosis in test.The innovations of this paper are:it realized the online monitoring system for mine hoist’s operative state which can not only transmit data in real time, but also diagnose fault at the same time; the system connected various sub-stations into a hoist system with the help of Ethernet technology and realized the publishment of Internet’s WEB; realized the fault diagnosis of mine hoist’s shake and brake systems by using SOM neural network technology; the SOM neural network model can effectively solve the problem of mine hoist’s multi-level faults and associated faults. The online monitoring and fault diagnosis system for mine hoist’s operative state which is based on SOM neural network can promote the modern management of mine hoist and improve mine hoist system’s safe operation level, which has a great promotion and application prospects.