Selbstgesteuertes Projekt
Predictive Maintenance: Industrial ML for Fault Detection
Condition-Monitoring Engineer · KI / ML · August 2026
Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis
Built an end-to-end rolling-bearing fault detector using leakage-free training on bearing vibration features, a compact depth-capped decision tree, and held-out evaluation with catch-rate and false-alarm rate. The candidate also produced a maintenance decision memo that justified a 0.60 alarm threshold using explicit cost trade-offs and translated the result into plain language for operations staff.
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
- Correctly identified kurtosis as the time-domain feature that jumps most on the faulty bearing
- Trained a depth-capped decision tree on training bearings only and stated depth and node count against the embedded budget
- Reported held-out catch-rate and false-alarm rate rather than evaluating on training data
- Wrote a maintenance memo that used the student's own threshold, catch-rate, false-alarm rate, and cost figures
- Leakage-Free Feature Engineering
- Honest Held-Out Evaluation
Meine eingereichte Arbeit19 Aufgaben
11 Aufgaben · 6.4k Zeichen · Python · 8 Textantworten · 436 Wörter
- Initial Prediction62 Zeichen
- Example Load Plot HealthyPython · 860 Zeichen
- Load Plot Signal CodePython · 1.8k Zeichen
- Load Plot Signal205 Zeichen
- Example Rms WorkedPython · 697 Zeichen
- Time Features143 Zeichen
- Freq FeaturesPython · 715 Zeichen
- Train Classifier363 Zeichen
- Evaluation Heldout288 Zeichen
- Threshold Decision332 Zeichen
- Decision Memo941 Zeichen
- Initial Prediction9 Wörter
- Example Load Plot Healthy81 Wörter
- Load Plot Signal Code163 Wörter
- Load Plot Signal15 Wörter
- Example Rms Worked72 Wörter
- Time Features10 Wörter
- Freq Features55 Wörter
- Train Classifier31 Wörter
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