Toseef Haider

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Toseef Haider

Mechanical Engineering · NUST Pakistan

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Certified

Self-directed project

Predictive Maintenance: Industrial ML for Fault Detection

Condition-Monitoring Engineer · AI / ML · July 2026

84/ 100

A ProoV case study · educational project, not employment

Built an end-to-end rolling-bearing fault detector using vibration snapshots, physics-based time and frequency features, and a compact decision tree sized for embedded use. Evaluated on held-out bearings with a full confusion matrix, then wrote a maintenance memo that justified a 0.40 alarm threshold using the candidate’s own catch-rate, false-alarm rate, and cost numbers while correctly localizing 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 RMS, kurtosis, and crest factor with step-by-step logic and showed the expected spike in kurtosis/crest factor on the faulty signal.
  • Used envelope-spectrum energy at the supplied defect frequencies and correctly found BPFO as the dominant band, mapping it to the outer race.
  • Trained a depth-capped decision tree on training bearings only and stated the tree depth and node count against an embedded budget.
  • Reported a complete held-out confusion breakdown with catch-rate and false-alarm rate, then defended a threshold using explicit cost figures.
  • Leakage-Free Feature Engineering
  • Honest Held-Out Evaluation
The work I submitted18 tasks

10 tasks · 11.5k characters · Python · 8 written answers · 714 words

  • Initial Prediction62 characters
  • Load Plot Signal CodePython · 1.8k characters
  • Example Rms WorkedPython · 697 characters
  • Time FeaturesPython · 1.9k characters
  • Freq FeaturesPython · 1.1k characters
  • Fault Location239 characters
  • Train Classifier363 characters
  • Evaluation HeldoutPython · 3.8k characters
  • Threshold Decision334 characters
  • Decision Memo1.2k characters
  • Initial Prediction9 words
  • Load Plot Signal Code163 words
  • Example Rms Worked72 words
  • Time Features173 words
  • Freq Features110 words
  • Fault Location14 words
  • Train Classifier31 words
  • Evaluation Heldout142 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.

Verified certificateTamper-proof · issued by ProoV
1Project completed
1Verified certificate
84Average score
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