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[P] Anomaly detection and/or predictive maintenance for automatic weather stations, what are some best models or techniques? (alien.top)

submitted 1 year ago by Ok-Way9889@alien.top to c/machinelearning@academy.garden

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Anomaly Detection and/or Predictive maintenance for automatic weather stations, what are some best models or techniques?

This is regarding collected meteorological data from automatic weather station sensors.

Looking for predictive maintenance and anomaly detection resources related to my project.

I am a graduating Computer Engineering student by next semester and currently planning to do a anomaly detection and predictive maintenance project of an automatic weather station or its components (I have a relative who has one and also one that works at a local government weather service).

Wikipedia: "An automatic weather station (AWS) is an automated version of the traditional weather station, either to save human labor or to enable measurements from remote areas.[1] An AWS will typically consist of a weather-proof enclosure containing the data logger, rechargeable battery, telemetry (optional) and the meteorological sensors with an attached solar panel or wind turbine and mounted upon a mast."

This one observes weather data at a high resolution (every 10 mins) but it is prone to errors and inconsistencies as compared to those manned stations.

Does anyone know reliable researches, datasets or resources I could utilize? Probably those related to a meteorological equipment or those that capture things such as temperature, wind speed, humidity, etc.? I can't seem to find studies where they perform prediction of equipment/sensor health or predictive maintenance on a weather station, or at least the components or sensors used for capturing weather data.

Also, is this a feasible project?

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[–] eamonnkeogh@alien.top 1 points 1 year ago

In paper [a] we look at (among other things) weather data, temperature, wind speed, humidity (fig 15, 16 ,18).

The DAMP algorithms is:

Parameter-lite (a single parameter)
Blindingly fast
Can be used with or without training data.
Can be used domain agnostic, or exploit domain knowledge.

And, in bake-offs, has been show to be SOTA...

[a] https://www.cs.ucr.edu/%7Eeamonn/DAMP_long_version.pdf