Search

The overall structure of WSN (wireless sensor networks) node platform was introduced. This platform was suitable for collecting and transmitting data of vibration signals. The initialization, register visit and data read mode of CC2420 were analyzed in detail, which made it possible to realize the wireless transceiver function of node platform. By applying WSN to collect data of mechanical equipment and using neural network to analyze the data, the usability of WSN in mechanical equipment conditions monitoring is proved.

Wireless sensor network is a thriving information collecting and processing technology, which is widely used in military field, industry and environmental monitoring, etc. In a wireless sensor network which is made up of tens of thousands battery-powered sensor nodes, data fusion technology can be used to reduce communication traffic in order to save energy. With respect to large mechanical equipment, traditional wired sensors are commonly used for fault detection and diagnosis. There will be no wiring problem if wireless sensor networks are used, which is favorable to detect potential problems in mechanical equipment without affecting normal production of enterprises. In this paper, the application of wireless sensor networks in machinery fault diagnosis is studied, a data fusion model for machinery fault diagnosis in wireless sensor networks and PCA neural data fusion algorithm are proposed, and the effectiveness of the method is demonstrated in an experiment.