WHITEPAPER

Sensor Health Monitoring — 6 Data Issues and How to Safeguard Against Them

GE Vernova
Sensors embedded in critical equipment continuously collect data on temperature, pressure, vibration, and more. However, the integrity of this data can be compromised by six “data pathologies,” including readings with a bad quality indication, missing values, erratic values, extreme values, flatline values, and time series data pathologies. When unhealthy data is streaming to predictive analytics software, operators are unable to rely on it to prevent equipment failure.

To safeguard against these risks, GE Vernova’s SmartSignal predictive analytics software includes Sensor Health Monitoring (SHM). SHM proactively and continuously monitors data streams for the six data pathologies, using advanced detection mechanisms to identify anomalies before they impact operations.

In this white paper, learn how reliable sensor data confirmed by SHM can help:
  • Prevent unplanned downtime: By catching data anomalies early, SHM helps avoid false alerts and ensures real issues are detected in time.
  • Optimize maintenance schedules: Reliable data allows for precise planning, reducing unnecessary maintenance, and extending equipment life.
  • Enhance operational efficiency: With accurate insights, plants can fine-tune operations, improving energy output and reducing waste.
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WHITEPAPER

Sensor Health Monitoring — 6 Data Issues and How to Safeguard Against Them

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