Commercial elevator reliability is a key factor in the flow of people and products through a building. Improperly maintained elevators impact public safety, productivity, energy consumption, and quality of life. To help businesses run their elevators consistently, Ivan Arakistain wanted to a build a solution that could help detect problems in elevator operation to help reduce maintenance cost.
The solution he arrived on was detecting audio anomalies (grinding, squealing, etc) using a machine learning model, and reporting those anomalies to a cloud dashboard using a Notecard.
More specifically, Ivan uses a XIAO BLE Sense to capture audio input, which he then processes with Edge Impulse Studio—all before sending the results to his cloud dashboard using the Notecard and Notehub. Ivan can then use the dashboard to spot if anything abnormal is going on inside the elevators that contain his hardware.
If all this sounds interesting, check out Ivan’s full writeup on Hackster, as it has a step-by-guide on the hardware and software he used.
