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 vibration features, a compact decision tree, and a held-out evaluation. The work includes leakage-aware training on the training bearings only, a cost-based alarm threshold decision, and a maintenance memo that explains the outer-race fault call in plain language using the model’s own numbers.
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
- Computed RMS, kurtosis, and crest factor with correct step-by-step logic and showed a strong separation between healthy and faulty snapshots.
- Trained a depth-1 decision tree on training bearings only and explicitly stated the tree depth and node count against the embedded budget.
- Provided a concrete maintenance memo with actual numbers for catch rate, false-alarm rate, and euro-denominated miss vs inspection costs.
- Leakage-Free Feature Engineering
- Honest Held-Out Evaluation
- Cost-Based Operational Decision-Making
Meine eingereichte Arbeit18 Aufgaben
10 Aufgaben · 8.9k Zeichen · Python · 8 Textantworten · 569 Wörter
- Initial Prediction62 Zeichen
- Load Plot Signal CodePython · 1.8k Zeichen
- Load Plot Signal205 Zeichen
- Example Rms WorkedPython · 698 Zeichen
- Time FeaturesPython · 1.6k Zeichen
- Freq FeaturesPython · 2.1k Zeichen
- Train Classifier363 Zeichen
- Evaluation Heldout288 Zeichen
- Threshold Decision332 Zeichen
- Decision Memo1.4k Zeichen
- Initial Prediction9 Wörter
- Load Plot Signal Code163 Wörter
- Load Plot Signal15 Wörter
- Example Rms Worked72 Wörter
- Time Features158 Wörter
- Freq Features97 Wörter
- Train Classifier31 Wörter
- Evaluation Heldout24 Wörter
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