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Industrial process

As explained in Outlier Detection in Python

An industrial process is a production or operational procedure involving machinery, materials, and monitored process conditions.

An industrial process is a stable, monitored production or operational procedure involving machinery, materials, and process conditions. Manufacturing assembly lines use monitoring to protect output quality, equipment reliability, and workplace safety. section 1.1.5

Industrial-process monitoring treats deviations from normal behavior as operationally important:

  • Unexpected friction, voltage, temperature, or other measurements can indicate equipment wear or developing faults. section 1.1.5
  • Severe anomalies may trigger alerts or slow or stop the process; less severe findings can support scheduled maintenance. section 1.1.5
  • An anomaly indicates deviation from the norm, not necessarily an imminent failure, especially when failure examples are scarce. section 1.1.5
Figure 1.1 Temperature readings from sensor data presented as a time series. The x-axis tracks time in seconds and the y-axis tracks the temperature. There is an unusually high level of fluctuation at 200 seconds and an usually low level at 800 seconds.

Use the sensor time series to inspect unusually high fluctuation near 200 seconds and unusually low readings near 800 seconds. section 1.1.5

How Outlier Detection in Python covers it

How deep it goes
Explains operational consequences and monitoring limitations, but stops short of specifying detection algorithms or maintenance procedures.
Examples
Uses manufacturing assembly lines, friction, voltage, equipment wear, and temperature readings with unusual fluctuations as examples.
What it assumes
Assumes monitored process measurements and notes that historical failure data may be sparse or incomplete.
What you can do after
Diagnose/debug with it by identifying process deviations, escalating severe anomalies, and scheduling maintenance before equipment failure risks increase.
How it connects
Connects industrial processes with outlier detection, predictive maintenance, sensor data, time series, and equipment failure.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Outlier Detection in Python. The section links point to the parts of the book it draws on.