Selbstgesteuertes Projekt
Predictive Maintenance: Industrial ML for Fault Detection
Condition-Monitoring Engineer · KI / ML · August 2026
Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis
Built a working rolling-bearing fault detector end to end: engineered time-domain and envelope-spectrum features, trained a compact depth-2 decision tree on training bearings only, and evaluated it on a held-out set with an 83% catch-rate and 25% false-alarm rate. The memo defended a 0.50 alarm threshold using the candidate’s own numbers and correctly localized the fault to the outer race via dominant BPFO energy.
Bewertet nach
- Feature engineering (signal to honest numbers)25%
- Held-out evaluation (honest measurement)25%
- Cost-based threshold decision + memo25%
- Compact model + embedded-budget justification15%
- Plain-language explanation10%
Bestanden · Bestehensgrenze 60/100
Was herausstach6
- Used the provided envelope_spectrum helper and correctly found BPFO as the dominant defect band
- Trained a depth-capped decision tree on training bearings only and stated depth 2 with 3 nodes against the embedded budget
- Reported a held-out confusion summary with both catch-rate (0.83) and false-alarm rate (0.25)
- Wrote a maintenance memo that ties the threshold choice to concrete operational costs and identifies the outer-race fault
- Leakage-Free Feature Engineering
- Honest Held-Out Evaluation
Meine eingereichte Arbeit14 Aufgaben
7 Aufgaben · 8 Zeilen · Python · 7 Textantworten · 344 Wörter
- Initial Prediction62 Zeichen
- Time Features143 Zeichen
- Freq FeaturesPython · 1.2k Zeichen
- Train Classifier363 Zeichen
- Evaluation Heldout288 Zeichen
- Decision Memo1.6k Zeichen
- Jupyterlite Code2 Zeilen
- Initial Prediction9 Wörter
- Time Features10 Wörter
- Freq Features101 Wörter
- Train Classifier31 Wörter
- Evaluation Heldout24 Wörter
- Decision Memo168 Wörter
- Jupyterlite Code1 Wort
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