Self-directed project
Predictive Maintenance: Industrial ML for Fault Detection
Condition-Monitoring Engineer · AI / ML · August 2026
A ProoV case study · educational project, not employment
Built an end-to-end rolling-bearing fault detector from vibration data: visualized the faulty waveform, engineered RMS/kurtosis/crest-style signal features plus envelope-spectrum energy at the supplied defect frequencies, and localized the fault to the outer race via BPFO dominance. Trained a compact depth-capped decision tree on training bearings only, then evaluated it on held-out bearings with reported catch-rate and false-alarm rate and defended a cost-based alarm threshold using the candidate’s own numbers.
Graded against
- 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%
Passed · pass mark 60/100
What stood out6
- Computed envelope-spectrum energy at all four supplied defect frequencies and correctly found BPFO as dominant.
- Used kurtosis as the strongest time-domain indicator for the impulsive faulty bearing.
- Reported a compact depth-1 decision tree with 3 nodes and stated it was trained on training bearings only.
- Included concrete held-out metrics and a cost-based threshold decision rather than optimizing for raw accuracy.
- Leakage-Free Feature Engineering
- Honest Held-Out Evaluation
The work I submitted18 tasks
10 tasks · 6.0k characters · Python · 8 written answers · 433 words
- Load Plot Signal CodePython · 1.9k characters
- Load Plot Signal205 characters
- Example Rms WorkedPython · 697 characters
- Time Features143 characters
- Freq FeaturesPython · 1.1k characters
- Fault Location239 characters
- Train Classifier363 characters
- Evaluation Heldout288 characters
- Threshold Decision334 characters
- Decision Memo769 characters
- Load Plot Signal Code163 words
- Load Plot Signal15 words
- Example Rms Worked72 words
- Time Features10 words
- Freq Features104 words
- Fault Location14 words
- Train Classifier31 words
- Evaluation Heldout24 words
Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.