Dhruv Desai completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector from vibration data: plotted a faulty snapshot, engineered RMS/kurtosis/crest-factor and envelope-spectrum defect-band features, trained a compact depth-capped decision tree on training bearings only, and evaluated it on held-out bearings with catch-rate and false-alarm metrics. The final memo justified a 0.40 alarm threshold using the candidate’s own cost numbers and correctly localized the fault to the outer race via BPFO.

• Leakage-Free Feature Engineering
• Honest Held-Out Evaluation
• Cost-Based Thresholding
• Physically Interpretable Modeling