Products
Orbinova Solutions Ltd — United Kingdom
Precision manufacturing — CNC

Unplanned downtime is endured,not predicted.

Sector
Tier 1/2 machine shops
Assets
20–40 machining centres
Integration
OPC UA — S7Comm — controller APIs
Status
Problem brief
Precision manufacturing — CNC
01Context

A typical precision manufacturer runs twenty to forty machining centres across three shifts — Fanuc, Siemens and Heidenhain controls of mixed ages — cutting aerospace and automotive work to tight delivery windows.

Maintenance is reactive by default: a machine runs until it stops, the cause is diagnosed on the floor, parts and a fitter are found, and production catches up with overtime and expedited freight.

02The problem

The failures that hurt are the ones with long, readable run-ups that nobody is reading. A spindle bearing develops a defect signature — energy at its characteristic frequencies climbing over weeks. A ballscrew loses preload and the axis following-error creeps up. A coolant pump starts cavitating; a toolchanger's hydraulics decay. The first visible symptom is often quality, not a stoppage: chatter marks on a bore, a drift in surface finish — scrap made by a machine that is still 'running fine'.

Then the actual stop comes at the worst time — a spindle seizure on a Saturday night shift kills a Monday shipment. The CMMS records the failure after the fact; it cannot anticipate one. The cost lands as missed OEE, weekend call-outs, expedited freight to recover dates, and a maintenance team that lives on its heels.

The bearing had been announcing itself for three weeks. The machine stopped on Saturday night.
03How Vertex-edge should help
  1. 01Connect to the controllers over OPC UA and Siemens S7Comm — and the native controller APIs where they expose more: spindle load, servo and axis current, following error, alarm history, thermal compensation values.
  2. 02Where the controller is blind to it, add tri-axial accelerometers at the spindle nose and on critical axes — the vibration channels that carry bearing and ballscrew signatures.
  3. 03Train physics-aware models per machine class: bearing defect frequencies as a function of spindle RPM, following-error envelopes per axis and feed, thermal growth against duty cycle — tuned with what the fitters already know about how each machine dies.
  4. 04Stand up a twin of each critical asset that tracks wear signatures against real production cycles, not idealised test runs.
  5. 05Issue predicted failures as ranked, evidence-backed work orders into the existing CMMS: "Spindle on M-14: outer-race defect energy up 3× in three weeks at 8,000 RPM. Recommend bearing inspection at next tool change, not next breakdown."
  6. 06For a bounded set of corrections — coolant flow, warm-up cycles, feed and speed envelopes on affected operations — apply setpoint changes inside engineer-set limits, signed and reversible.
04What success should look like

Success would mean the bearing changed at a planned tool change — not diagnosed at midnight on the floor.

Weeks before the stop
Failure signal
Ranked, with the evidence attached
Work orders
Caught before the scrap run
Quality drift
Signed and reversible
Every intervention

This is a problem brief, not a customer case study. It describes how Vertex-edge is designed to work on this problem — not a deployment that has already happened.