Selbstgesteuertes Projekt
Predictive Maintenance: Industrial ML for Fault Detection
Condition-Monitoring Engineer · KI / ML · August 2026
Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis
Built a compact rolling-bearing fault detector end to end: engineered time-domain features, trained a shallow decision tree on training bearings only, and evaluated it on a held-out set with catch-rate and false-alarm rate. The candidate also wrote a maintenance memo that justified an alarm threshold using explicit cost trade-offs and translated the result into plain language for non-experts.
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
- Trained a depth-capped decision tree on training bearings only and stated a compact 3-node, depth-1 model against the embedded budget.
- Reported held-out catch-rate and false-alarm rate rather than relying on training performance.
- Made a threshold choice using explicit cost figures and tied the memo to maintenance operations.
- Leakage-Aware Model Training
- Held-Out Evaluation
- Cost-Based Operational Decision-Making
Meine eingereichte Arbeit18 Aufgaben
10 Aufgaben · 6.4k Zeichen · Python · 8 Textantworten · 438 Wörter
- Initial Prediction50 Zeichen
- Load Plot Signal CodePython · 1.8k Zeichen
- Load Plot Signal205 Zeichen
- Example Rms WorkedPython · 697 Zeichen
- Time Features143 Zeichen
- Freq FeaturesPython · 1.1k Zeichen
- Train Classifier363 Zeichen
- Evaluation Heldout288 Zeichen
- Threshold Decision334 Zeichen
- Decision Memo1.4k Zeichen
- Initial Prediction6 Wörter
- Load Plot Signal Code163 Wörter
- Load Plot Signal15 Wörter
- Example Rms Worked72 Wörter
- Time Features10 Wörter
- Freq Features117 Wörter
- Train Classifier31 Wörter
- Evaluation Heldout24 Wörter
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