Fault diagnosis of auxiliaries in power plants based on wireless sensor networks with vibration transducer

There are many auxiliaries in a power plant (APP) with high rotating speed, such as pumps, fans, motors and so on. To warrant their safe and reliable operation, their state of vibration has to be monitored. But because of their scattered locations, the traditional way of online monitoring with shielded cable connections is costly and work expensive and the precision, reliability and safety of itinerant measurements are unable to meet the requirements of customers. A novel fault diagnosis of APP based on wireless sensor networks(WSN) with vibration transducer has therefore been proposed to realize vibration data acquisition, online-detection and data analyzing in this paper, which meets the requirements of auxiliaries with less expenditure and warrants safe operation in the long run. The multi-sink topological structure of WSN can improve the transmission efficiency of the multi-hop network to meet the vibration test requirements of low-latency, high frequency sampling and high data throughput. With rough set (RS) we can reduct the parameters of the samples, optimize attribute index and simplify the training samples and the neural network structure, which makes the training learning of the radial basis function neural network (RBFNN) faster. Therefore, this method improves the efficiency and precision of auxiliaries fault diagnosis through the wireless monitoring of auxiliaries' vibration.