Chaman completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector from vibration data: visualized the signal, engineered RMS/kurtosis/crest-factor and envelope-spectrum features, localized the fault to the outer race via BPFO, and trained a compact depth-capped decision tree on training bearings only. Evaluated on held-out bearings with reported catch-rate and false-alarm rate, then justified a 0.42 alarm threshold using the project’s cost trade-off and a plain-language maintenance memo.

• Leakage-Free Feature Engineering
• Honest Held-Out Evaluation
• Physics-Based Fault Localization
• Cost-Based Operational Decision-Making