Nithin Kumar Goud completed Predictive Maintenance: Industrial ML for Fault Detection

Built an end-to-end rolling-bearing fault detector from vibration data: visualized a faulty snapshot, engineered RMS/kurtosis/crest-factor and defect-frequency envelope features, localized the fault to the outer race via BPFO, and trained a tiny depth-capped decision tree on training bearings only. Evaluated on a held-out set with catch-rate and false-alarm metrics, then justified a 0.30 alarm threshold using the candidate’s own cost numbers in a maintenance memo.

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