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 using vibration data: engineered RMS, kurtosis, crest factor, and envelope-spectrum defect-band energy; trained a depth-capped decision tree on training bearings only; and evaluated it on held-out healthy/faulty bearings with catch-rate and false-alarm metrics. The final memo defended a cost-based alarm threshold and correctly localized the fault to the outer race from BPFO.
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 in narrow bands around all four supplied defect frequencies and correctly identified BPFO as dominant
- Used kurtosis as the root split in a depth-1 decision tree, matching the impulsive nature of bearing faults
- Justified the alarm threshold with explicit miss-vs-false-alarm costs and concrete held-out performance numbers
- Explained the maintenance tradeoff in plain language with a real cost ratio from the student's own results
- Leakage-Free Feature Engineering
- Honest Held-Out Evaluation
The work I submitted19 tasks
11 tasks · 7.3k characters · Python · 8 written answers · 431 words
- Initial Prediction62 characters
- 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 Memo2.0k characters
- Initial Prediction9 words
- Load Plot Signal Code163 words
- Load Plot Signal15 words
- Example Rms Worked72 words
- Time Features10 words
- Freq Features117 words
- Fault Location14 words
- Train Classifier31 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.