DC Motor Anomaly Detection System based on Arduino Solution
This Arduino-powered solution implements an energy monitoring-based anomaly detection system using a current sensor and machine learning models running on edge devices. By capturing the electricity flowing in and out of a machine, it can collect large amounts of data on energy usage patterns over time. This data is then used to train a machine learning model capable of identifying anomalies in energy consumption behaviors and alerting operators to potential issues. The solution offers a cost-effective and scalable method for maintaining equipment health and maximizing energy efficiency, while also reducing downtime and maintenance costs.
CATEGORY
Embedded; Energy; Hardware; Industrial; Interconnect Passives & Electromechanical; Machine learning; Programmable logic; Sensors; Software
PARTS IN DESIGN / BOM BY STORE
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The displayed part lists is a small subset of the complete BOM.
| MFGR | PART# | BLK NM |
|---|
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| MFGR | PART# | BLK NM |
|---|---|---|
| Arduino | AFX00002 | Embedded Board |
| MFGR | PART# | BLK NM |
|---|---|---|
| Arduino | AFX00002 | Embedded Board |
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