Real-Time FFT Pipeline: Vibration to Alert in 100 Lines

#fft #vibrationanalysis #predictivemaintenanc #realtimeprocessing

You Don't Need a 50-Page Framework to Catch Bearing Faults

Most FFT tutorials stop at plotting pretty spectrograms. Production systems need more: streaming data ingestion, fault frequency detection, and actionable alerts — ideally before someone asks "why is the pump making that noise?"

I built a minimal real-time pipeline that goes from raw accelerometer samples to Slack notifications in under 100 lines of Python. No Kafka. No Docker. Just NumPy, a threshold detector, and enough signal processing to catch early-stage bearing defects. It's been running on a test rig for three months, and the only false positive came from someone dropping a wrench.

This isn't a production-grade SCADA integration. It's a proof-of-concept that shows the core mechanics: buffering, windowing, FFT computation, peak detection, and alerting. If you're migrating from schedule-based maintenance or just want to understand what happens between "sensor wire" and "email notification," this is the skeleton.