Adi Lithin Reddy completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector using vibration snapshots, time-domain statistics, envelope-spectrum defect-band analysis, and a compact decision tree trained only on the training bearings. Evaluated it on a held-out bearing set with reported catch-rate and false-alarm rate, then defended a 0.53 alarm threshold in a maintenance memo that named the outer-race fault from the dominant BPFO frequency.

• Leakage-Free Feature Engineering
• Honest Model Evaluation
• Cost-Based Decision Making
• Physics-Grounded Fault Localization