Mahsa Mahdi abadi completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector from vibration data: engineered RMS, kurtosis, crest factor, and envelope-spectrum defect-frequency features; localized the fault to the outer race via BPFO; trained a compact depth-capped decision tree on training bearings only; and defended a 0.40 alarm threshold with held-out catch-rate, false-alarm rate, and cost numbers.

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