The Ideal
Tool for Training
Machine Learning Models
- Machine learning requires large, high quality data sets to realize its potential
- Vibration data can provide unique predictive insights for machine learning models
- enDAQ can quickly provide high-quality differentiating vibration & environmental data
High-Quality Data = More Accurate Models
Key enDAQ features:
- Configurable Sample Rates (1 Hz–20 kHz): Capture high-quality, large datasets with adjustable sample rates for precise model training
- Wi-Fi Remote Control & Rechargeable Batteries: Enable portable, long-term data collection for high-quality time series, ideal for RNNs/LSTMs/deep learning models with real-time access
- Advanced Triggering: Capture critical events for anomaly detection, improving ML model accuracy
- Data Integration: Combines multi-modal data to enhance ML model training
Embedded Sensors
Use Cases: Why Vibration Data is Crucial
Motor Condition Monitoring: In a pump system, vibration analysis can detect bearing wear, allowing maintenance to replace the bearings before a failure occurs, preventing costly pump downtime.
Rotating Equipment Diagnostics: In an industrial fan, unusual vibration patterns can signal blade damage or misalignment, enabling corrective actions before the fan suffers a catastrophic failure.
Quality Control in Production: In CNC machining, vibration sensors monitor spindle health, ensuring any vibrations outside the acceptable range are flagged, preventing defects in precision parts.
Predictive Maintenance: In a wind turbine, vibration data from the gearbox can indicate impending gear damage, allowing for maintenance to be scheduled before a complete gearbox failure happens, avoiding expensive repairs.
Understanding Machine Learning Implementation
enDAQ data loggers are a reliable and easy way to help you quickly collect vast amounts of high-quality data to train your Machine Learning models - expediting your machine-learning efforts and leading to better outcomes.
The following resources were created to help you get started, with helpful insights and codes you can employ today.
Blog Posts:
- Preprocessing Vibration Data for Machine Learning
- Building a Machine Learning Model with a Vibration Sensor
- Building an Anomaly Detection Model in Python
Additional Capabilities
Develop a Shock, Vibration and Environment Profile with ease & confidence for your specific application!
Take a deep dive of our applications pages for specific application resources.
Demo with an Engineer - Book Now