Plant and shop-floor systems, where the OT/IT boundary is a hard constraint and a bad deploy can stop a production line.
14 published across 7 topics.
Manufacturing and industrial IoT fundamentals58 short answers on one page, for revising rather than studying.PLCs, SCADA, historians and the MES, and how they relate to ERP.
The line survives on cached work orders and locally queued events, so the difficulty is the merge, not the outage. Reconciliation has to be idempotent, ordered by source timestamp rather than arrival, and honest about units that passed a gate nobody could evaluate.
A PLC runs the machine on a deterministic scan cycle and holds the truth about plant state; SCADA gives operators a view and supervisory control; a historian archives the resulting time series; an MES turns a production plan into work orders and a batch record; ERP sits above with the commercial view.
Network segmentation, the unidirectional-flow instinct, and safe data extraction.
Establish reachability before severity, then remove or narrow the paths that lead to the affected asset rather than trying to fix the asset. Patch the systems around it that can be patched, and record a risk acceptance with an owner and an expiry date instead of a permanent silence.
Segment the network into zones, put a broker or historian replica in a DMZ between plant and enterprise, and have the plant side initiate every connection outbound so no firewall rule ever permits traffic into the control zone. Nothing in the cloud reaches a controller.
OPC UA, Modbus and MQTT at the level of what each is for and how each fails.
On any protocol that reports by exception, no new value and an unchanged value look identical, so liveness has to be carried deliberately. Status codes, source timestamps, birth and death messages and a source-side counter each establish it at a different layer of the path.
Modbus is a register read/write protocol with no types and no security; OPC UA is an information model with certificate-based security and subscriptions; MQTT is a broker-based transport that says nothing about payload meaning. They sit at different layers rather than competing.
High-cardinality sensor data, downsampling, retention and edge buffering.
Every derived figure that joins on time is affected, and none of them error. Duration and attribution metrics take the skew directly, so a shift with twelve stops mis-measures availability by nearly four points, and only storing both event time and ingestion time makes the offset visible.
Size the stream from stated assumptions, keep series identity narrow so cardinality stays bounded, choose sampling deliberately and downsample keeping min, max and count rather than means alone, tier retention, and buffer at the edge with a bounded queue and a rate-limited drain.
Failure labelling, feature windows, and why most projects fail on data rather than models.
Start by instrumenting outcomes, because you almost certainly cannot say what happened to any past alert. Delivered recall is model recall multiplied by the rate at which alerts are acted on, so at a fifteen per cent action rate a better model changes nothing that anyone experiences.
They fail on labels rather than models: failures are recorded as repair dates in free-text work orders, the positive class is nearly empty, feature windows leak the outcome, and no baseline was established, so an encouraging first result cannot be trusted or beaten.
Genealogy, batch and serial traceability, recall, and statistical process control.
Intersect the lineage of the three units to find what they share, then forward-trace from every shared factor you cannot yet exclude. Containment starts as the union of those populations, because narrowing later is cheap and widening later is the thing that destroys credibility.
Genealogy is the recorded graph linking consumed lots, equipment and process settings to what was produced, and it makes a recall narrow instead of total. SPC monitors a process against its own variation, so it detects drift before anything breaches a specification limit.
Safety systems, change control on a running line, and availability expectations.
Your first deliverable is a fast, evidenced answer to whether you are in the path, measured at the station rather than on your own dashboards. Restoring production outranks diagnosis, so take a cheap evidence snapshot and then get yourself out of the path.
Downtime has a per-hour cost someone can quote, so changes go in during planned windows under change control, rollback must be instant, and no staging environment reproduces a physical process. Safety functions live in separate assessed systems your software must never join.