QuartzEdge & FuseFixer
QuartzEdge QuartzEdge
FuseFixer FuseFixer
QuartzEdge QuartzEdge
I’ve been training a neural net to spot faults in power lines before they happen—thought you might be intrigued. Have you ever tried feeding a circuit’s sensor data into an ML model to predict outages?
FuseFixer FuseFixer
Nice work! I’ve played with a few of those predictive models before, mostly to flag transformer sagging or breaker trips. The trick is getting enough clean, labeled data—power line sensors can be a nightmare with all the noise and weather interference. What kind of features are you pulling out? I’d love to hear if you’re using vibration, temperature, or maybe even the little humming of the current. The more you can correlate with actual failures, the better the net can learn. If you run into any false positives, let me know—I’m pretty good at figuring out why a model keeps chasing ghosts.
QuartzEdge QuartzEdge
Sounds like you’re on the right track. I’m pulling a mix of high‑frequency vibration spectra, temperature spikes, and the low‑amplitude “humming” at the fundamental line frequency—those 60‑Hz hums tend to change when insulation starts to break down. I also throw in some derived statistics: RMS, kurtosis, skewness of the vibration waveform, and a few moving‑average filters to catch those slow‑drifting temperature trends. Then I map those to actual outage logs to train a small random forest. The trick is normalizing the data per feeder so the model doesn’t just learn the big, obvious outliers. If you hit any false positives, let me know and we’ll dig into the feature importance matrix—often it’s a subtle sensor drift I hadn’t accounted for.
FuseFixer FuseFixer
That’s a solid mix—vibration spectra give you the micro‑fractures, the hum flags dielectric changes, and the stats pull out the non‑linear bits. I’d just double‑check that the moving‑average windows aren’t smoothing away the very slow drifts you’re trying to catch; sometimes a 10‑minute window will swallow a 30‑minute anomaly. Also, if you see a spike in the importance of the kurtosis after you normalize, that could mean the sensor’s own calibration curve is slipping. Keep an eye on the sensor’s baseline; a tiny offset can masquerade as a fault. Good luck, and hit me up if the model starts calling every rainstorm an outage.
QuartzEdge QuartzEdge
You’re spot on—window size is critical, and that kurtosis spike is a classic calibration hiccup. I’ll run a quick baseline drift test on the oldest sensors to see if they’re the culprit. Any chance you’ve logged the exact sensor firmware version? That might explain the subtle offsets. Keep me posted if the rainstorm test turns into a false alarm; I’ll tweak the threshold after all.