  The current battery-powered fault detection system for vibration monitoring
has a rather limited lifetime. This is because the high-frequency sampling
(typically tens of kilo-Hertz) required for vibration monitoring results in
high energy consumption in both the analog-to-digital (ADC) converter and
wireless transmissions. This paper proposes a new fault detection architecture
that can significantly reduce the energy consumption of the ADC and wireless
transmission. Our inspiration for the new architecture is based on the
observation that the many tens of thousand of data samples collected for fault
detection are ultimately transformed into a small number of features. If we can
generate these features directly without high frequency sampling, then we can
avoid the the energy cost for ADC and wireless transmissions. We propose to use
piezoelectric energy harvesters (which can be designed to have different
frequency responses) and integrators to obtain these features in an
energy-efficient manner. By using a publicly available data set for ball
bearing fault detection (which was originally sampled at 51.2kHz) and
piezoelectric energy harvester models, we can produce features, which when
sampled at 0.33Hz, give a fault detection accuracy of 89% while reducing the
sampling requirement by 4 orders-of-magnitude.
