Writing
Ten billion records a day teach you retention policies, autoscaling limits and which database not to use.
Why predictive maintenance still needs vibration analysts: scarce matched training data, missing operational context, noisy sensors.
How a PdM pipeline turns bearing vibration into FFT spectra and alerts, plus six infrastructure steps an IT team must prepare.
How a predictive-maintenance team keeps AI diagnostics accurate: field-experienced analysts, three time zones, two-tier triage, custom tooling.
Five big-data mistakes from running a 16 TB, ten-billion-records-a-day platform: retention, quality, uncapped autoscaling, surplus fields, wrong database.
Per-sensor fault signatures for an electric motor: vibration, temperature, current and motion amplification, with thresholds and six implementation steps.
A role-by-role plan for starting production data analytics and predictive maintenance, with the barriers and the expected downtime reduction.
The business case for IIoT predictive maintenance: downtime costs, ROI, implementation challenges, and build versus buy.
Why queries slow down at IIoT scale, and how a PdM platform cut latency from 10 s to 2 s by moving to TimescaleDB.
I'm always interested in thoughtful conversations about technology, engineering organizations, AI and ambitious products.
Get in touch opens LinkedIn in a new tab. Email reaches [email protected].
