Challenge:Developing and maintaining a “smart” solution without the need for a dedicated data science team on site to incorporate machine learning. Automated pattern recognition and analytics functionalities.
Obstacles to overcome:No two assets are identical, which requires a generalized machine learning software still able to capture individual anomalies to a high enough degree of precision and accuracy.
Solution:Purpose-built application for use in asset-intensive use cases.
- Drag-and-drop dashboards for real-time visualization of metrics like temperature, output current, torque trim, position, velocity, etc.
- Machine learning capabilities, including automatic model training, parameter tuning, automatic anomaly detection, and individual asset-level models for maximum detection accuracy.
- Customizable rules to trigger real-time SMS and email alerts about anomalies 24 hours a day.
- Key performance indicator (KPI) monitoring
- Inherent, industry-accepted security standards for device and system connectivity.
Machine Learning Application for Predictive Maintenance
Build your own IIoT solution following these three steps:
- Connectivity: Choose the suitable Hardware or Lean.Solution Starter Kit
- Edge Data Processing: Add the Edge Client for Continuous Data Streaming
- Asset Monitor: Enable your IIoT Solution with the Data Processing Backend
Contact us if you need assistance to put your solution together.