Abdullah Rahim Hussain completed Predictive Maintenance: Industrial ML for Fault Detection

Built a working rolling-bearing fault detector end to end: engineered RMS, kurtosis, crest factor, and defect-frequency envelope features; localized the fault to the outer race via BPFO; trained a compact depth-2 decision tree on training bearings only; and evaluated it on held-out bearings with reported catch-rate and false-alarm rate. The candidate also wrote a maintenance memo that justified a 0.60 alarm threshold using their own cost numbers and explained the decision in plain language.

• Leakage-Free Feature Engineering
• Physics-Grounded Fault Localization
• Honest Held-Out Evaluation
• Cost-Based Thresholding