Are you looking for an out-of-the-box solution for Predictive Maintenance and Data Analytics? I'm thrilled to announce my latest video , where I focus on predictive maintenance for induction motors.
In this video , I introduce the WAGO Library Analytics (from WAGO), a powerful tool that simplifies the integration of machine learning models into PLC programs for predictive maintenance and condition monitoring.
By attaching simple sensors to your motor, such as vibration or temperature sensors, you can detect:
- Anomalies
- Drifts
- Forecast motor behavior
These metrics helps in preventing costly downtime and ensuring smooth operation. Whether you're an engineer, a maintenance manager, or just passionate about automation and IIoT, join me as I demonstrate live data from my motor and explore how WAGO Library Analytics can transform your approach to maintenance.
Say goodbye to unexpected failures and hello to optimized performance!
Timeline:
0:00 - Introduction
0:26 - What is Predictive Maintenance?
0:46 - What is Anomaly?
0:58 - What is Drift?
1:16 - How do you find Predictive Maintenance metrics?
1:25 - Introduction to WAGO Library Analytics
1:47 - Hardware setup
3:07 - Use-case 1: Detecting Anomaly score of the industrial motor's vibration data
7:39 - Use-case 2: Detecting drift of the industrial motor's vibration data
9:35 - Use-case 3: Prediction of future data points
10:55 - Grafana and Node-RED Integration
13:15 - Introduction to Portainer
13:30 - Outroduction
