Writing
Bearings, vibration spectra, ten billion records a day. What predictive maintenance teaches about data, models and the people who read them.
Four ways AI assistants create IoT technical debt, backed by studies, and three disciplines to contain it: review, no-go zones, refactoring.
Where LLMs fit in Industrial IoT: interpreting ML predictive-maintenance output, RAG over manuals, protocol interoperability. Humans decide.
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.
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.
From Waddington's RAF bombers to a four-stage IIoT pipeline: how modern predictive maintenance works and why it is bought as a service.
The business case for IIoT predictive maintenance: downtime costs, ROI, implementation challenges, and build versus buy.
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].
