Pranitha GUNIGANTI completed Predictive Maintenance: Industrial ML for Fault Detection

Built a working rolling-bearing fault detector end to end: engineered time-domain and envelope-spectrum features, identified the dominant BPFO outer-race signature, trained a compact depth-capped decision tree on training bearings only, and evaluated it on held-out bearings with a full confusion matrix. The decision memo defended a cost-based alarm threshold using the candidate’s own catch-rate, false-alarm rate, and maintenance-cost numbers.

• Leakage-Free Feature Engineering
• Honest Held-Out Evaluation
• Cost-Based Operational Decision-Making
• Interpreting Defect Frequencies