Anmol Raj completed Predictive Maintenance: Industrial ML for Fault Detection

Built a working rolling-bearing fault detector end to end: visualized the vibration snapshot, engineered RMS/kurtosis/crest-factor style features plus envelope-spectrum energy at the supplied defect frequencies, and used the dominant BPFO peak to localize an outer-race fault. Trained a compact depth-capped decision tree on training bearings only, then evaluated it on held-out bearings and justified the alarm threshold with concrete catch-rate, false-alarm, and cost numbers in a maintenance memo.

• Leakage-Free Feature Engineering
• Honest Held-Out Evaluation
• Physics-Based Fault Localization
• Cost-Aware Decision Making