Development Of Boiler Fault Diagnosis Software Based On Hierarchical Mixture Expert Network

In connection with the diagnoses problem of 670t/h boiler for Chongqing power plant, the hierarchical mixture expert (HME) network is developed in this paper. The HME module is employed and the new samples are added and the database for fault diagnosis, fault treatment and interpretation subsystem are broadened to further develop the diagnosis system. The diagnosis program is made in Visual C++ .The samples are trained with this network, during which the relationships between the network parameters (such as rule number, study rate, expert network initial error and gate network error, circulation times and power index) and learning error were probed into, and the outcome can provide the basis for network parameter selecting. Then the simulation tests on drum excess water and lack of water were practiced; the results indicate that: if the network parameters are selected correctly,the training samples can be convergent to a relative small and stable value with a small learning error. Some comparisons can be made through sample training under the circumstance of ME and the HME, after adopting HME with the tree-style structure, two layers of the Gate network can enhance the control of the algorithm system. As a result the precision and stability is improved. Also, the experiments show that when the HME, which is a mixture of the fuzzy network and the expert system based on knowledge, is used to diagnose the fault of the boiler, some points can be attained: both symbol reasoning and nerve network reasoning can be combined very well. This system has the quality of a clear hierarchy and a high efficiency for reasoning. In a word it has such a capability of simulating the speculation of human expert to a certain degree that it can diagnose the faults for boiler accurately and timely.