For enriching the model, a few ideas for discriminating failure drives: Some hard drives have a vibration sensor(s) in them - i wonder if a daemon could sample

For enriching the model, a few ideas for discriminating failure drives: Some hard drives have a vibration sensor(s) in them - i wonder if a daemon could sample throughout the day and dump a summarisation of the data when the daily SMART data dump is being generated. The drives report temperature via SMART, i wonder if anything meaningful can be extracted from the temperature differential between the HDD’s sensor and a chassis temp sensor? I wonder if more discrimination by manufacturing detail (date of mfr, which factory etc) could help? I was going to suggest drive supply voltages but those are hard to compare across chassis - calibration of voltage measuring circuitry in PSUs isn’t too hot for the millivolt differences you’d want to measure - the differences would be so small to be lost in the inaccuracies between boards. Also many PSUs lack a method to query their measurements. Fascinating study and excellent writeup. I have zero ML background but i really enjoyed this.