Provably lower MTTR.
Mean time to repair gets shorter, and the difference shows in OEE. Numbers you can defend in a quarterly review without hand-waving about "improving processes."
Edmund is a debugging platform that reads your PLCs, HMIs, schematics, manuals, alarms, and maintenance journals. Unifies all of your data sources into one platform with a reliable maintenance workflow.
Found the servo control block FB 1343 Faulhaber MCBL. The drive is enabled when it is online, not cancelled, and a motion command (Home / Jog / Positioning) requests it.
142//Go Hall Zero 143IF #GoHallZero AND NOT #Cancel AND NOT #Stop THEN 144 IF NOT #status.enabled THEN 145 #state.enable.start := true; 146 END_IF; 147 #state.goHallZero.start := true; 148 #status.done := false; 149END_IF;
-94B1 is the product-detect photocell (SICK WL12-2P430) on conveyor 3. It's wired to input %I4.3 on DI module -A4, through terminal X1:11–12 in cabinet +SK3.
Pulled IMM01 power from the MES historian for Jan 12–18, 2026 and bucketed it by machine state:
The machine drew power while idle for 74 h — more than double its 31 h of actual production. Trimming idle time is the fastest saving here.
From the SICK datasheet (rev 04/2024):
"Recommended cleaning interval for the WL12 in dust contamination class M is every 240 operating hours; failure to clean reliably degrades switching frequency below the 1 000 Hz nominal value."
Your maintenance log shows the lens was last cleaned 312 h ago — past the 240 h interval, which lines up with the repeated photocell dropouts.
Yes — the Station 52 photocell replacement (Tomáš Marek, Apr 23) sits at the top of the Line 4 journal, indexed alongside every recent fix:
Open any entry for the full symptom, cause, and fix — so the next technician inherits it instead of rediscovering it.
Assembly line 4 is fully indexed and every source is active:
A plant lead can confirm coverage at a glance — and catch a source that's gone stale before someone hits the gap mid-fault.
An hour of unplanned downtime on a tier-2 automotive line.
We bet your last downtime took more hours than you would have liked.
Edmund's first users are technicians on the floor. Its first KPI movers are the people running the plant.
Mean time to repair gets shorter, and the difference shows in OEE. Numbers you can defend in a quarterly review without hand-waving about "improving processes."
An ordinary technician picks up where a PLC specialist used to be needed.
Every fault has a documented root cause. The board review stops being "we're working on it" and starts being "this is the third Tuesday relay sticking; here's the procurement plan."
When a line stops, the problem usually isn't the repair itself — it's diagnosing the right problem. Your data already has the answer, scattered across PLC projects, schematics, alarms, journals, and people's heads, but none of it connects. Edmund significantly shortens the diagnostic step.
Across our deployments, the part swap is rarely the bottleneck. The hour you lose is upstream, figuring out which part, why it failed, and whether it'll come back next quarter.
PLC projects, EPLAN drawings, alarm history, maintenance journals, and what's only in the head of the senior technician — separate systems, separate handoffs, no map between them.
A traceable answer beats a confident guess. Edmund hands the technician the cause and the next step, with citations, so the only thing left to do is the wrench work.
Here, Copilot means Siemens Industrial Copilot, Siemens' own factory-floor AI, not the Microsoft 365 office assistant. Edmund reads the Siemens stack too, alongside the other controllers and the records around them.
Siemens Industrial Copilot is a real diagnostic assistant, and a good one, on Siemens equipment. It reads the TIA project, translates Siemens fault codes, and works through the Siemens manuals. But your plant isn't all Siemens, and the answer at 2 a.m. isn't all in the Siemens manuals. Edmund reads the Siemens line and the Rockwell cell next to it, plus the schematic, the journals, the MES, and it cites where every answer came from.
The conveyor 3 fault is a photocell signal fault on the SIMATIC controller. I've translated the HMI message and pulled the troubleshooting steps from the machine manual.
Documented checks: clean the sensor lens, verify the alignment, inspect the sensor wiring. The spare-part list has the replacement sensor if you need it.
That's -94B1, the SICK WL12-2P430 photo-eye on conveyor 3 — input %I4.3, part of the run interlock in FB_Conv3_Control, line 48.
It's the third photocell stop on this conveyor in 30 days. The lens was last cleaned 312 h ago; the maintenance plan calls for every 240 h in this dust class. Clean the lens and the reflector first. If the fault returns, check terminals X1:11–12 in cabinet +SK3.
On Siemens equipment, both will answer. The difference shows when the fault crosses into the rest of the plant.
None of this is a knock on Siemens' copilot — it troubleshoots well from Siemens fault codes and manuals, and Senseye brings predictive maintenance across sensor data from any vendor. Two things it isn't built for, as of mid-2026: reading a competitor's PLC project to diagnose its logic, and tracing your existing EPLAN schematic or free-text journals to find a fault. That's the gap Edmund fills, across the whole plant, Siemens included.
Tick what matches your line. If none do, your operation probably runs fine without us. We'd rather tell you that on the call than walk you through a sales funnel.
Edmund reads and connects every data source, from PLC programs to handwritten journals, into a single source of truth.
Edmund parses TIA Portal, Allen-Bradley, and Omron projects (plus anything that compiles to IEC 61131-3) into the same searchable model. Returns blocks, line numbers, call sites, never a paraphrase. A technician can navigate code they didn't write.
142//Go Hall Zero 143IF #GoHallZero AND NOT #Cancel AND NOT #Stop THEN 144 IF NOT #status.enabled THEN 145 #state.enable.start := true; 146 END_IF; 147 #state.goHallZero.start := true; 148 #status.done := false; 149END_IF; 150//Position abnullen 151IF #SetPositionNull AND #status.online THEN 152 #state.setPositionNull.start := true; 153END_IF;
A tag like -94B1 resolves to the cabinet, the row, and the clamp position a technician can put a hand on. Closes the "where is it physically?" loop in seconds, not days.
Pulls trends, historical measurements, and alarm logs in real time. Confirms or refutes a guess from PLC or EPLAN, surfaces patterns across shifts and lines, and returns a diagnostic conclusion with a recommended next step, not a CSV to read.
Mechanical drawings, electrical schematics, manuals, datasheets, measurement protocols. Returns the exact citation, multilingual and visual when needed. The agent that turns up in every debugging workflow, checking the HMI, reviewing schematics, looking up a part spec, handing context to the next shift.
"Recommended cleaning interval for the WL12 in dust contamination class M is every 240 operating hours; failure to clean reliably degrades switching frequency below the 1 000 Hz nominal value."
Captures what technicians know but rarely write down: the symptom, the root cause, the parameter change, the photo. Edmund parses handwritten notes and shift logs, surfaces repeat interventions, and turns one expert's hour-long debug into a repeatable solution anyone can follow.
Every project keeps a live count of indexed documents, journals, PLC programs, and connected sources, each with its status and last-sync time. A plant lead can confirm coverage at a glance and catch a source that’s gone stale before someone hits the gap mid-fault.
Maintenance knowledge sat in three places, hundreds of PDFs, handwritten logs, and the heads of technicians who'd spent decades on the floor. None of it was searchable. Edmund pulled all of it into one query box.
A junior technician answering a 2 a.m. fault alarm now reaches the same institutional knowledge a senior engineer could recall in seconds. Adoption was immediate, the interface was simple enough that technicians started using it on day one without prompting.
The standout feature was voice input. Instead of typing notes after a long shift, technicians dictate directly on the floor and Edmund writes the entry into the digital maintenance log automatically.
Model Obaly's Opava plant alone had accumulated over 300,000 pages of technical documentation: manuals, PLC programs, maintenance logs, spare-parts databases, spread across shared drives, physical binders, and the heads of long-serving engineers.
Nobody could search it. When a machine stopped, the maintenance team had to know the right person, not the right system. As experienced engineers retired, the institutional memory was quietly leaving the building.
Edmund indexed the production machines at Opava, covering printing, laminating, die-cutting, and waste lines.
Moravia Cans makes aluminum aerosol cans with patented embossing technology and high-speed production. With 480+ employees and 60% growth over three years, their Bojkovice factory runs shaping and printing lines where uptime is everything.
Manual collections, maintenance records, and live ERP databases sat in disconnected systems. Edmund brings them together so technicians get a complete picture from a single question. No switching between tools or chasing colleagues for context.
Edmund connects directly to Moravia Cans' Microsoft SQL databases, including QAD and PowerBI. Maintenance teams check spare-parts availability, review repair history, or pull production metrics on the spot, without database expertise or IT tickets.
Edmund works with the data you already have. No new systems to build or connect. Your existing infrastructure stays intact. On top of it, we add a diagnostic layer powered by generative AI.
Documentation, PLC projects, databases, and people's know-how. Nothing in your production systems gets changed.
Components, code, data, and people's notes linked into one system that understands how your line actually behaves.
A technician asks in plain language or with a photo. Edmund searches every data source and answers from it.
Edmund tells the technician what to do, finds the part ID and the replacement procedure, and logs the work into the maintenance journal — instantly available across the company.
References from our customers
Before, when a machine stopped and the fault wasn't obvious, just finding the right information took longer than the repair itself: manuals on the shared drive, electrical docs elsewhere, repair history in another system. Now everything is at the machine. Downtime is roughly 30% shorter and we save tens of hours every month. The biggest benefit is being able to focus on the repair, not on hunting for information.
AI is the only tool that can systematically work with data from multiple sources. Using Edmund has led to a lower frequency of a specific failure, or even its complete elimination.
We've had a very positive experience with Edmund. The results have an immediate impact on the efficiency of our production processes.
New production lines and machines are coming to our plant, and we don't yet have the experience to diagnose them efficiently. At the same time, we can't grow the maintenance team to keep up. So we use Edmund AI as an "invisible co-worker": it fills the missing know-how, supports our technicians' decisions, and speeds up finding and fixing faults.
Industry 4.0 promised a lot but delivered very little. Edmund is genuinely making a difference.
One question from the shopfloor. Five disconnected sources, schematics, PLC code, datasheets, IoT readings, decades of maintenance journals. One traced answer, with citations.
Edmund classifies the question and locates the exact file in the right silo of your factory's knowledge graph, the PLC project, the function block, the line of structured text where the gripper-open routine is written.
From that line of code, Edmund follows the mag_ejected variable into the wiring diagram, traces the wire to the actual sensor, opens the sensor's datasheet, and surfaces the section in the manual that describes the retraction routine.
Edmund matches this fault signature against years of maintenance journals, every paper notebook, SAP ticket, SharePoint entry, and recognises this exact failure has been logged before. Across multiple machines. With a known resolution path.
Not a 50-PDF search result. Not a chatbot guess. A traceable answer the technician can act on, with every source clickable, every claim cited.
The gripper-open logic lives in Eject_Mag.scl. It only fires when:
eject_mag = TRUEmag_empty = FALSEmag_eject_fault or mag_retract_faultThis exact fault has been logged 47 times across 12 machines in the last 90 days, and resolved 41 of 47 times by re-pressurising Module 3 to 5.5 bar and resetting the magazine.
Suggested first check: pneumatic pressure on Module 3.
This project was realised via financial support from Technological Incubation program