AI maintenance-log classification
Convert short maintenance notes into traceable categories while keeping observations, suspected causes and confirmed findings separate.
Synthetic evaluation input
id,note M-01,Vibration reported; bearing fault suspected; inspection not completed M-02,Seal leak confirmed by visual inspection M-03,No fault found during inspection Allowed finding_status values: suspected; confirmed; not_found
Original Inzonex training example, version 1.0. No customer document or real equipment data. No model has been scored on this page.
Expected fields
| Field | Reference answer |
|---|---|
| M-01 | "suspected" |
| M-02 | "confirmed" |
| M-03 | "not_found" |
| confirmed_records | 1 |
Check a structured response
The comparison runs in your browser against this answer key. It checks exact fields and values, not the accuracy of an explanation or a model's general ability.
Recommended workflow
Use an explicit label set and preserve the original record ID. Escalate ambiguous records rather than forcing every note into a failure diagnosis.
Maintenance language often mixes symptoms with hypotheses. A technician writing that a bearing fault is suspected has not confirmed a bearing fault. The label should preserve that distinction, even when a confident diagnosis would make a cleaner dashboard.
Define the allowed categories before testing. Otherwise two systems can produce different labels that look reasonable but cannot be compared. Include duplicates, incomplete notes and notes that explicitly rule a condition out in the evaluation set.
The synthetic packet uses three records with controlled labels. It measures adherence to that contract, not predictive-maintenance accuracy. A production evaluation needs locally reviewed notes and an assessment of which mistakes create the most operational harm.
Failure checks
- M-01 must not become a confirmed bearing failure.
- Keep all three source record IDs.
- Count confirmed findings, not all reported symptoms.
What this exercise does not prove
These labels are an example taxonomy. Passing this packet does not establish diagnostic accuracy or failure prediction.
For an actual evaluation, keep a separate held-out set, record tool/model version and settings, and log raw outputs, corrections, elapsed time and actual charges. Do not compare tools tested on different inputs as though they ran the same benchmark.
What is checked in AI maintenance-log classification?
Convert short maintenance notes into traceable categories while keeping observations, suspected causes and confirmed findings separate.
What does AI maintenance-log classification not prove?
These labels are an example taxonomy. Passing this packet does not establish diagnostic accuracy or failure prediction.
Related tasks
Match this workflow to your data requirements
Methodology and reuse
These packets and answer keys are original Inzonex educational material, licensed CC BY 4.0. Attribute Inzonex and link to this task page when reusing the packet. The licence does not cover third-party material linked from this site.
NIST AI 600-1: background on generative AI evaluation and risk. This exercise is not NIST-certified.