Tag: CBM
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Digital Twin Hype vs Reality: Why Simple FFT Often Wins
A $50K digital twin missed a bearing fault that a $200 FFT setup caught instantly. Here's when physics-based models actually help vs marketing hype.
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FFT Vibration Analysis on Real Bearing Data: Python Setup
Learn FFT vibration analysis on real bearing data with Python. Step-by-step setup using NumPy, SciPy, and Matplotlib for predictive maintenance.
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Threshold Alarms vs ML: 4-Week Python CBM Migration
Compare threshold vs ML-based condition monitoring in Python. 4-week migration guide with FFT, LSTM anomaly detection, and live industrial examples.
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FFT Shows No Peaks: 4 Sensor Setup Mistakes That Kill Spectra
Diagnose why your FFT shows no peaks with these 4 critical sensor setup mistakes. Learn proper sampling rates, gain settings, and mounting techniques.
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CBM Migration Guide: Scheduled to Condition-Based
CBM migration forces you to rethink alerting, data pipelines, and ops workflows โ not just swap sensors. Here's what actually breaks and how to fix it.
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Spectral Kurtosis for Bearing Faults: Outperforming FFT
Spectral kurtosis caught bearing failure 3 weeks before FFT showed symptoms. When it works, when it fails, and what runs in production.
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Attention-Based Sensor Fusion: 15% Better Pump Failure Prediction
Fusing vibration, temperature, and current sensors with Attention boosted pump failure prediction from 79% to 94.2% โ here's the architecture.
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DTW for Multi-Sensor Anomaly Detection in Machinery
DTW beats point-based methods for gradual bearing faults: 12% earlier detection on real degradation data. Includes multi-sensor code.