The Case for a Machine Health Foundation Model
Could one model learn how machines behave and fail, then adapt to a new asset with few labels? The case for a machine health foundation model, and the hurdles.
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What Makes Industrial AI Defensible? Data, Deployment and Trust
Data alone protects less than many assume. A framework for what makes industrial AI hard to copy: the right data, deployment, workflow and earned trust.
Cloud, Edge and Device: Where Should Industrial AI Actually Run?
Latency, data volume, outages and security each push industrial AI work to a different tier. What runs best on the device, at the site and in the cloud.
Why the Next Great Infrastructure Company May Start with Hardware
Conventional advice says avoid hardware. In infrastructure, an installed base on real assets often drives the recurring value. What Otis and others show.
The Next Data Moat Is Physical
The last wave of AI trained on public data. Data on how machines behave and fail is scarce, private and slow to gather, which is why it may be the next moat.
Building for Harsh Environments: The Industrial Design Principles Behind AFFERENT
Heat, dust, vibration and electrical noise shape every decision in an industrial device. The design priorities behind NODE, which is still in development.
Resilience Begins Before the Outage
Most outage hours come from major events, but how equipment fares in them is decided weeks earlier. Why grid resilience starts with knowing asset condition.
From Pipelines to Pump Stations: Monitoring Distributed Industrial Assets
Pipelines and water networks spread small, unattended machines over huge areas. What makes them hard to monitor, and how a distributed sensing network works.
Industrial Sensor Placement: Why Location Changes What You Can Detect
How signals travel from a machine fault to a sensor, why mounting acts like a filter, and a practical checklist for placing vibration and temperature sensors.