Harsh Gaikwad completed Predictive Maintenance: Industrial ML for Fault Detection

Built a compact rolling-bearing fault detector end to end: engineered time-domain and envelope-spectrum features, trained a depth-capped decision tree on training bearings only, and validated it on held-out healthy and faulty bearings. The final memo defended a practical alarm threshold using the candidate’s own catch-rate and false-alarm numbers and translated the dominant defect-frequency result into a maintenance action.

• Leakage-Free Feature Engineering
• Honest Held-Out Evaluation
• Physics-Grounded Fault Interpretation
• Cost-Based Operational Decision-Making