Vibration Classification

Accurate State and Fault Assessment For Active Equipment

Continuous Edge Sensing Networks

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.

Vibration-Based Fan State Demo
This demo uses only a low-cost IMU sensor and commodity MCU along with a SensiML ML model to infer a variety of fan states of interest:
  • Fan Off
  • Fan On
  • Fan Guard Tamper
  • Blade Interference
  • Mount Fault
  • Tapping (Base Interference)
  • Blocked Flow
Platforms and Plans
SensiML offers complete Knowledge Pack development services allowing you to focus on your application. Our team can devise a customized project plan using your equipment training data to create a tailored vibration classification library ready to drop into your application.
Platforms and Plans
For project teams with basic machine learning familiarity, desire to learn, or needing to undertake the tasks entirely in-house, SensiML offers its ML Analytics Toolkit suite. A true end-to-end workflow, SensiML Analytics Toolkit supports the complete process from data collection and labeling to Knowledge Pack generation and testing.