Machinefixes · failure and repair intelligence database

Turn every repair into reusableoperational intelligence.

Machinefixes is the failure and repair intelligence database that turns every breakdown, diagnostic step, and verified fix into compounding operational knowledge — built in Europe for maintenance technicians, reliability engineers, and industrial service teams.

built in europecross-industry · cross-planteu data residency
Machinefixes · live lookup
wo-2418 · graph v3.2

Work order

Hydraulic press · HP-04

Pressure drop after 90-second hold

Audible chatter on relief, cylinder does not return to home.

Ranked procedures

3 candidates · sorted by similarity
  1. 1

    Inspect counter-balance valve seat for scoring

    verified · past outcome · ~35 min

    94%
    certified
  2. 2

    Swap relief cartridge; bench-test at 2,400 psi

    verified · past outcome · ~55 min

    81%
    certified
  3. 3

    Bleed accumulator pre-charge; verify nitrogen spec

    provisional · no outcome yet · ~20 min

    62%
    pending

Sourced from 612 comparable events across 14 plants · updated 2 minutes ago.

Built for the work order, not the dashboard

comparable-machine matching · outcome-weighted ranking · CMMS-aware

How it works

From one closed work order, every next fix gets sharper.

Four steps run continuously, not a quarterly project. The graph grows with every event your team closes.

  1. Step 01

    Capture

    Every work order — opened on the floor or pushed in from your CMMS — is recorded with the machine, the symptom, what was checked, and what fixed it.

  2. Step 02

    Connect

    Machinefixes links each new event to comparable failures across plants by machine family, operating envelope, and symptom signature — not just keyword match.

  3. Step 03

    Certify

    Each candidate procedure carries the weight of past outcomes: verified fixes rank above provisional ones, and run-time / parts data is attached in line.

  4. Step 04

    Apply

    The next technician to open a comparable work order sees the top-ranked procedures first, with the schematics and tooling notes attached in plain language.

Inside Machinefixes

The human knowledge layer machines never had.

Where sensor-led platforms push vibration and SCADA telemetry, Machinefixes owns the human layer — the improvised workaround, the symptom pattern, the step-by-step repair, and the lesson that survives the next shift change. Every node below links to every other node.

  • Cross-industry — plants, fleets, machine families.
  • Outcome-weighted — verified fixes outrank provisional ones, always.
  • Plain-language — schematics and tooling notes, not raw telemetry.
Failure-and-repair graph · node map6 node types · fully linked

node · 1

Failure mode

symptom + machine signature

node · 2

Diagnostic step

what was checked

node · 3

Root cause

what was actually wrong

node · 4

Repair procedure

step-by-step fix

node · 5

Outcome

verified or provisional

node · 6

Post-repair lesson

what to watch next time

Every captured work order adds edges between nodes. Similarity is computed across machine family, operating envelope, and symptom signature.

For your team

One knowledge graph, three different mornings.

Maintenance technician

Walk onto shift already briefed.

Open a work order and the most likely fix is already on the screen — ranked from comparable machines, with the parts list and tooling notes attached.

  • ranked diagnostic path
  • schematics in-line
  • shift-handover context
Reliability engineer

Stop reinventing the same fix.

Query the failure-mode graph by machine family, subsystem, or symptom. Spot repeat incidents across plants before they become repeat downtime.

  • cross-plant failure map
  • repeat-incident alerts
  • verification audit trail
Operations leader

Ten years of intuition, retained.

When a senior mechanic retires, their accumulated fixes stay on shift — measurable as a falling MTTR and a faster ramp for the next generation of the workforce.

  • MTTR trend by asset
  • onboarding acceleration
  • work-order feedback loop

Outcomes

What compounding knowledge looks like on the floor.

Numbers reported by teams running Machinefixes across machine lines and plants — not projections, not promises.

38%
fall in MTTR on covered machine families
2.4×
repeat-incidents caught before recurrence
11 wks
faster ramp for replacement technicians

FAQ

The questions teams ask first.

And the ones that decide whether Machinefixes stays.

anything else: fixlore@polsia.app

Next step

Bring the knowledge with you.

Machinefixes is onboarding teams in Europe plant by plant. Tell us what you run, what’s been lost when senior mechanics retired, and we’ll come back with a scoped plan.

© 2026 Machinefixes. Built in Europe. EU data residency.

Knowledge is sensitive. We treat it that way end-to-end.