SmartSignal Virtual Signal Repair Maximizes Machine Learning in the Presence of Unhealthy Sensor Data

Machine learning anomaly detection models rely on continuous access to high-quality sensor data to accurately detect and predict equipment faults. Maintaining these models over time requires periodic adjustments using recent training data. But, when one or more sensors produce unhealthy or missing readings, the affected data is typically excluded from the training set, reducing the volume of usable data and often triggering costly model rebuilds. The risk? If a model does not completely represent the normal operating conditions of the equipment, false alerts are likely to result.

Learn how Virtual Signal Repair (VSR) in SmartSignal® Predictive Analytics software addresses this challenge by imputing realistic substitute values for compromised sensor readings. This preserves the full breadth of available training data without sacrificing model accuracy.

Learn how VSR: