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 bearing-fault detector end to end: plotted a faulty vibration snapshot, used time-domain features including kurtosis, trained a shallow decision tree on training bearings only, and evaluated it on a held-out set with catch-rate and false-alarm rate. Also produced a maintenance memo defending a 0.50 alarm threshold with explicit cost trade-offs and a plain-language explanation 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
- Used a depth-capped decision tree and explicitly stated depth 1 and 3 nodes against the embedded budget
- Reported held-out catch rate and false-alarm rate rather than evaluating on training data
- Wrote a maintenance memo that ties the threshold choice to concrete catch-rate and false-alarm numbers
- Leakage-Free Model Training
- Honest Held-Out Evaluation
- Cost-Based Operational Decision-Making
Meine eingereichte Arbeit18 Aufgaben
10 Aufgaben · 6.4k Zeichen · Python · 8 Textantworten · 428 Wörter
- Initial Prediction62 Zeichen
- Load Plot Signal CodePython · 1.9k Zeichen
- Load Plot Signal205 Zeichen
- Example Rms WorkedPython · 698 Zeichen
- Time Features143 Zeichen
- Freq FeaturesPython · 1.1k Zeichen
- Train Classifier363 Zeichen
- Evaluation Heldout288 Zeichen
- Threshold Decision334 Zeichen
- Decision Memo1.4k Zeichen
- Initial Prediction9 Wörter
- Load Plot Signal Code163 Wörter
- Load Plot Signal15 Wörter
- Example Rms Worked72 Wörter
- Time Features10 Wörter
- Freq Features104 Wörter
- Train Classifier31 Wörter
- Evaluation Heldout24 Wörter
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