Category: CBM Portfolio Project
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Real-Time CBM Dashboard: Pipeline with Alerts and Viz
Deploy a CBM dashboard with Flask + TimescaleDB + Slack alerts. Full pipeline from sensor stream to RUL predictions in under 200ms.
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RUL Prediction: Classical ML vs LSTM Training Guide
Random Forest beat LSTM by 12% MAE in offline testsโbut LSTMs won in streaming inference. Here's the benchmark data and when to use each.
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Feature Engineering for Predictive Maintenance: Health Indicators
Extract FFT peaks, RMS trends, and thermal gradients from vibration data for RUL predictionโfeatures that beat raw signals by 40% accuracy.
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CBM from Scratch: Sensor Data Collection and Preprocessing
Fix the 3 timestamp sync errors that ruin CBM predictions. Trigger-based logging pipeline design with sub-second precision on edge devices.