All classifications of fan events are based on monitoring only vibration/motion as detected by the QuickAI on-board inertial measurement unit (6-axis accelerometer and gyro sensor IC). In such way, the demo simulates a common desired use case in industrial predictive maintenance applications for an over-the-top sensor system that can detect equipment usage, fault states, and degrading performance with minimal or no incursion into existing electromechanical systems used for control and operation.
The dataset consists of captured sessions of raw IMU sensor values (STMicro LSM6DSL on the QuickAI eval board) sampled at 104Hz. Each session contains labeled segments of a given fan state/classification type from amongst the following class map of events for recognition:
|0||Unknown||Board is undergoing some unrecognized movement|
|1||Fault||Fan unit has an obstruction in the blades|
|2||Off||Fan unit is turned off|
|3||On||Fan unit is turned on and in ‘normal’ operation state|
|4||Shock||Something is tapping the fan unit from the top side|
Depending on various factors like table surface, AC frequency, and other unknowns in your work space the demo may not work well under your specific conditions. This is a perfect example of how-to retrain the demo with more variance data so that it can build a more advanced algorithm that works under all conditions.
Below is a typical labeled input file (this one is Fault_01.csv) showing raw accelerometer and gyro data as collected during a capture session: