Subhadeep Sen

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Subhadeep Sen

Mechatronics Engineering

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Projects

Certified

Self-directed project

Predictive Maintenance: Industrial ML for Fault Detection

Condition-Monitoring Engineer · AI / ML · September 2026

92/ 100

A ProoV case study · educational project, not employment

Built an end-to-end rolling-bearing fault detector from vibration data: engineered RMS, kurtosis, crest factor, and defect-frequency envelope features; trained a depth-capped decision tree on training bearings only; and validated it on a held-out set with catch-rate and false-alarm metrics. The student also localized the fault to the outer race via BPFO and defended a 0.42 alarm threshold using concrete cost tradeoffs.

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 around the supplied defect frequencies and correctly selected BPFO as dominant
  • Used kurtosis as the root split in a shallow decision tree and stated depth/node count against the embedded budget
  • Reported held-out catch-rate and false-alarm rate rather than evaluating on training data
  • Justified the alarm threshold with explicit euro-cost calculations from the student's own results
  • Leakage-Free Feature Engineering
  • Honest Model Evaluation
The work I submitted18 tasks

10 tasks · 9.1k characters · Python · 8 written answers · 447 words

  • Initial Prediction62 characters
  • Load Plot Signal CodePython · 1.8k characters
  • Example Rms WorkedPython · 697 characters
  • Time Features143 characters
  • Freq FeaturesPython · 2.8k characters
  • Fault Location239 characters
  • Train Classifier363 characters
  • Evaluation Heldout288 characters
  • Threshold Decision335 characters
  • Decision Memo2.5k characters
  • Initial Prediction9 words
  • Load Plot Signal Code163 words
  • Example Rms Worked72 words
  • Time Features10 words
  • Freq Features124 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.

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1Project completed
1Verified certificate
92Average score
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