On-node Signal Processing to Reduce the Power Consumption of Wireless Sensor Nodes for Vibration Monitoring Application in Rolling Element Bearing Monitoring

In recent years, the use of wireless sensor networks for vibration monitoring is emphasized, because of its capability to continuously monitor at hard-to-reach locations of complex machines. Low power consumption is one of the main requirements for the sensor nodes in continuous and long-term vibration monitoring. However, the power consumption of state-of-the-art wireless sensor nodes is significantly increased by wireless radio in continuously transmitting the raw vibration data to the base station. One of the ways to reduce the power consumption is to reduce the duty-cycle of wireless transmission. Accurately processing the vibration data on the sensor node and transmitting only the critical information, such as natural frequency, defective frequency and amplitude of the vibration, will not only reduce the amount of data transmitted but also the duty cycle of the wireless communication. It eventually leads to reduction of total power consumed by the sensor nodes. In this paper the capability of a sensor node to accurately process the real-time vibration data is analyzed and the corresponding power consumption is measured. In particular, impact-based analysis of real-time vibration data is performed by breaking complex signal-processing tasks into manageable segments on the sensor nodes to minimize algorithmic complexity while still meeting real-time deadlines of the algorithm. As a result, it is found that the accuracy of the on-node signal processing is comparable with conventional off-node monitoring methods, whilst reducing total