The System Of Metallurgic Dedusting Fan Status Monitoring And Faulty Diagnosis

Equipment monitoring and faulty diagnosis have always been the research hotspots in industrial field. On the one hand, there are rising requirements of the safety, stability and reliability for equipments. On the other hand, the network-based remote monitoring and fault diagnosis model has been getting more and more attention at home and abroad, along with the widely successful application of network technology as well as the signal analyzing and processing technology. Our research was launched just under this background. It was combined with the monitoring and fault diagnosis study on dedusting fan of the cast house at ironmaking plant. In this paper, we mainly studied the realization of the technology of fan monitoring and fault diagnosis, analyzed the key techniques and made a relatively deep discussion about the problems to be resolved.The frame structure of the fan remote monitoring and diagnosis system was designed. The system was mainly composed of the on-line monitoring subsystem and the intelligent diagnosis subsystem, which were able to analyze and design the data acquisition system, on-line monitoring system, signal analysis and fault diagnosis system and so on.To develop the data acquisition system based on a single chip microcomputer. After the design of the data acquisition scheme and the establishment of the simulation system using Proteus ISIS, we wrote the corresponding compiler of the KeilC platform in order to simulate the system. Lastly, we developed the AT89S52-based data acquisition system which could collect the state values of each sensor according to the instruction and then transmit them to server through the CAN bus. The server was to process and store data, therefore, users could remotely monitor the real-time data through network.Research on faulty diagnosis by the method of data fusion disposal. First of all, it was essential to select an appropriate neural network model in accordance with the requirements of the system and the characteristics of the source, and to study a data fusion algorithm with ART-2 clustering analysis as the core. As the implementation of classifying mechanism and similar data fusion technology with the application of ART-2 network data fusion, some learning method was adopted into the off-line learning towards the established neural network system on the basis of the present multi-source information and the systematic fusion knowledge. Through the off-line learning, the connection weight and connecting structure topology of the network were determined. The lastly obtained network was used in the faulty diagnosis system of the metallurgical fan. The system utilized the BP network to realize the fusion of different kinds of data and to accomplish the real-time monitoring and fault diagnosis of the running state for the fan system equipment. The algorithm was proved to be effective and feasible.