Calculated Channels

Overview

Calculated Channels in Sift process and transform time-series sensor data in real-time, enabling precise anomaly detection, root cause analysis, and predictive maintenance. Engineers can extract actionable insights directly from raw sensor data by applying advanced mathematical operations. Calculated Channels can be express using the Common Expression Language (CEL).

Sift supports two types of calculations (transformations) tailored to work with all supported data types:

• Stateless transformations: Stateless transformations process each data point independently, making them ideal for real-time computations like unit conversions or threshold checks.

  

• Stateful transformations: Stateful transformations retain context over time, enabling engineers to analyze historical trends, detect gradual system degradation, identify performance shifts, and uncover evolving failure patterns.

  

Unlike Rules, which enforce logic and trigger automated actions, Calculated Channels refine raw sensor data for deeper analysis. This distinction allows engineers to identify trends, compare system behaviors, and make data-driven decisions before failures occur.

Sift enables engineers to implement Calculated Channels seamlessly through the UI and API. This flexibility allows for real-time data transformations, ensuring structured, data-driven decision-making at scale. By integrating Calculated Channels into automated workflows, teams can refine raw sensor data, detect emerging trends, and optimize predictive maintenance strategies.