Thenujan Arulnesan 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, trained a compact depth-2 decision tree on training bearings only, and evaluated it on a held-out set with an 83% catch-rate and 25% false-alarm rate. The memo defended a 0.50 alarm threshold using the candidate’s own numbers and correctly localized the fault to the outer race via dominant BPFO energy.

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