Traditional predictive maintenance relies on skilled maintenance technicians equipped with portable diagnostic sensors to perform spot checks on vital equipment. However, this method provides limited coverage for high-value or critical machines.
With the emergence of low-cost embedded processors, sensors, and wireless connectivity, there is now a transformative increase in monitoring capability. Smart edge AI sensors with predictive models built using SensiML tools provide a cost-effective means for continuous monitoring. Such systems can be implemented at much greater scale with networks of sensor endpoints covering more machinery for better assurance on uptime, yield, and productivity.