Poornima Singh

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Poornima Singh

Robotics and Automation · DY Patil College Of Engineering, Akurdi, Pune

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Projects

Certified

Self-directed project

Predictive Maintenance: Industrial ML for Fault Detection

Condition-Monitoring Engineer · AI / ML · August 2026

68/ 100

A ProoV case study · educational project, not employment

Built a compact rolling-bearing fault detector end to end: engineered time-domain features, trained a shallow decision tree on training bearings only, and evaluated it on a held-out set with catch-rate and false-alarm rate. The candidate also wrote a maintenance memo that justified an alarm threshold using explicit cost trade-offs and translated the result into plain language for non-experts.

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
  • Trained a depth-capped decision tree on training bearings only and stated a compact 3-node, depth-1 model against the embedded budget.
  • Reported held-out catch-rate and false-alarm rate rather than relying on training performance.
  • Made a threshold choice using explicit cost figures and tied the memo to maintenance operations.
  • Leakage-Aware Model Training
  • Held-Out Evaluation
  • Cost-Based Operational Decision-Making
The work I submitted18 tasks

10 tasks · 6.4k characters · Python · 8 written answers · 438 words

  • Initial Prediction50 characters
  • Load Plot Signal CodePython · 1.8k characters
  • Load Plot Signal205 characters
  • Example Rms WorkedPython · 697 characters
  • Time Features143 characters
  • Freq FeaturesPython · 1.1k characters
  • Train Classifier363 characters
  • Evaluation Heldout288 characters
  • Threshold Decision334 characters
  • Decision Memo1.4k characters
  • Initial Prediction6 words
  • Load Plot Signal Code163 words
  • Load Plot Signal15 words
  • Example Rms Worked72 words
  • Time Features10 words
  • Freq Features117 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.

Verified certificateTamper-proof · issued by ProoV
1Project completed
1Verified certificate
68Average score
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