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Equipment monitoring

Machine downtime tracking from your camerasKnow what every machine is doing, and why

Agnify tracks machine downtime from the cameras you already have. It reads each machine's state: running, changeover, maintenance, waiting on material, down. Every stop gets its reason and the clip, every part is counted and checked, and OEE shows the reason behind every loss. No codes typed into a tablet.

Book a free feasibility study See the OEE losses
See it on a real bag press: machine state and every bag counted →
Your existing cameras, plus one over a machine where it's needed Fuses PLC, stack-light and sensor signals where you have them Dashboards, alerts and an API alongside your MES or spreadsheets

OEE with the reason behind every loss

Each of the six big losses with its time, cause and clip.

See OEE

Downtime reasons for every stop

Tool change, empty cart or nobody at the press: the camera shows which.

See stop reasons

Machine states you choose

Running, changeover, waiting or down, per machine.

See machine states

Shorter changeovers

Changeover time split into waiting, fetching and the real work.

See changeovers

Machines without sensors that report anyway

Gauges and stack lights read by camera.

See machine health

Watch it on a real press

Bags counted, stops timed, an alert when the operator leaves.

Watch the press

Watch one ceiling camera time every stop on a real press

Real footage from a hand-fed paper-bag press. Every overlay is model output: people masked, every bag counted, running and stopped time, and an alert when the operator walks away.

Real footage, model output. 33 seconds cut from a 1 minute 51 second run; the printing stretch plays at 6x speed.
Bags counted33 of 33Every bag in this clip; 60 of 61 over the full recording.
Running time0:57 of 1:51The press feeds a bag every 1.65 seconds, so a 5-second gap means it has stopped. No extra model needed.
Nobody at the station6.5 sFrom the operator walking away to the alert.
People maskedEvery frameA solid mask over every person before anything else runs.

Measured, not claimed. Counts were scored against a tape mark on the press cylinder that passes the camera once per bag. Open the interactive example →

States and stop reasons for presses, lines, cells, ovens and cranes

If a person can see a machine's state, so can the camera. Each machine type gets its own states and stop reasons.

Which of your machines should we look at first?Send a few hours of footage. We'll show you its states and OEE losses.

Book a free feasibility study

One camera shows the state of every press in the cell

Five presses in one view: two running, one in a mold change, one under maintenance with the lockout on, and one idle with nobody at it. Agnify names each state and starts the clock, so the idle press is flagged after six minutes, not at the end of the shift.

A camera view of five injection molding presses in a row: two running, one in a mold change with the mold on a crane, one under maintenance with a lockout tag, one idle with nobody at it
CAM-02 · Molding cell B · presses P1 to P510:35
P1Running42 s cycle
P2ChangeoverMold swap, 23 min
P3MaintenanceLockout on, 12 min
P4IdleNo operator, 6 min
P5Running41 s cycle

OEE tracking with the reason behind every loss

Availability from real stop times, performance from counted cycles, quality from rejects seen at the outfeed. Each of the six big losses gets its own time, cause and clip.

Availability

80%

Time the machine was scheduled but not running.

BreakdownsSetup and adjustment
Why it stopped →

Performance

85%

Running, but slower than it should.

Small stopsReduced speed
Shift timeline →

Quality

96%

Parts made that weren't good.

Start-up rejectsProduction rejects
Quality applications →
Molding cell B, shift 165%OEE, with every lost minute explained

Example data. OEE = availability × performance × quality.

Every stop gets its downtime reason and the clip

Clamps, cycle sensors and stack lights show the same red for a tool change, an empty parts cart or nobody at the press. The reason waits for someone to enter it, often hours later. The camera sees the cause and saves the clip.

What a sensor records

P7 · 09:30 to 10:30 · example data

09:3010:0010:30
State
Stopped, 10:02 to 10:16
Duration
14 min
Reason
Not entered
Evidence
None
Terminal · P7 · qualify stop 10:02
MaterialChangeoverMaintenanceNo operatorOther

Someone picks a reason at the end of the shift. Often it's "Other".

A stopped injection molding press with an amber stack light, an empty parts cart beside it and nobody at the station, while a forklift brings a box of resin pellets down the aisle
CAM-05 · Molding cell B · P710:14
P7 · Waiting on material · 14 min · clip saved
  • Parts cart emptyNothing staged for the next run
  • No operator at the pressStation empty since 10:03
  • Resin delivery arrivingForklift at the press at 10:14
Reasons without the tabletClassified from the scene. Operators confirm instead of typing from memory.
Evidence, not a codeEvery stop, changeover and reject keeps its clip.
Small stops and slow cyclesThe performance losses nobody writes down, timestamped by machine and shift.
01 · Availability and performance

Machine state monitoring for every minute of the shift

Every minute lands in an OEE loss you can act on. You choose each machine's states, and we build them from what the camera sees: the gate, the mold, the operator, the parts cart, the technician. Below, half a shift on the presses above.

Molding cell B · shift 1Press states from 06:00 to 10:35 · example data
Running 78%

Select a segment to see what the camera saw.

What the camera sawSelect a segment on the timeline
    02 · Setup and adjustment loss

    Changeover time tracking, minute by minute and step by step

    Changeover is an availability loss. A sensor or app logs when it starts and ends. The camera shows what happened in between: which minutes were waiting, which were fetching and which were the work.

    A setter guides a steel mold hanging from an overhead crane into an injection molding press, with a tooling cart of clamps and hoses beside the machine
    CAM-02 · Molding cell B · P2Tue 14:31
    Also measuredDie changeTool changeFixture swapLabel and film roll changeFlavor or product changeNozzle changeColor change and purge
    P2 · mold swap, job 4471 to 4480Tuesday, shift 2 · example data
    37 min

      What shorter changeovers are worth on one machine

      Use your own numbers. Illustrative, not a promise.

      45 minBack each production day on this machine
      188 hA year, at 250 production days

      Minutes saved × changeovers a day × 250 days. Freed time becomes output only if the machine is the constraint and there's demand.

      Timed the same way every shiftLast good part to first good part, both visible on camera.
      Work that belongs before the stopFetching tooling, waiting for a setter, hunting for clamps.
      Your best changeover as the standardTrain from the fastest crew's clip for the same job.
      03 · Machine health

      Legacy machine monitoring with no network port needed

      US manufacturing equipment averages 9 years old by value, and many machines are far older.5 Legacy equipment is manufacturers' top smart-factory roadblock.6 Those machines still have gauges, sight glasses and stack lights. A camera can read them.

      The side of an older hydraulic press: two analog pressure gauges, an oil sight glass, a stack light and a drip of oil on the floor below a fitting
      CAM-09 · Press shop · hydraulic press H207:45
      Hydraulic press H2No controller connection · example data
      2 to check
        Also visibleCoolant levelChip buildupConveyor slipSmoke or mistGuards open while runningHMI alarm screenProof a PM was doneTime to respond and repair

        50 things a camera can tell you about your equipment

        Each one is scoped in a feasibility study and tested on your footage.

        Downtime and bad parts cost more than most plants see

        8.3%

        Of planned production time lost to downtime in US discrete manufacturing, about $245B a year1

        NIST, Annual Report on the U.S. Manufacturing Economy, 2024
        $150K

        Top-end cost of one hour of unplanned downtime for a small or mid-sized manufacturer2

        Siemens, True Cost of Downtime 2024
        85%

        Of defective parts caught by trained inspectors, who also rejected 35% of good ones3

        Sandia National Laboratories, 2015
        70%

        Of manufacturers still enter production data by hand4

        NAM Manufacturing Leadership Council, 2024

        Start with your existing cameras and add one where a machine needs it

        No sensor on every machine and no controller integration to start. We connect to your existing feeds, add a camera where a view is missing, and bring in machine signals where they help.

        Step 1

        Connect the views

        RTSP streams, your NVR or recorded footage, mapped to the machines each camera sees.

        Step 2

        You define the states

        Your team decides which states and stop reasons matter. Agnify's engineers program them and test on your footage before go-live.

        Step 3

        Run the floor on it

        State timelines by machine and shift, alerts with the frame, reports, and events through the API.

        Cloud by default

        Nothing to host. Results in minutes for dashboards, shift reports and trend alerts.

        Real time on site

        Alerts the moment a machine stops, from a server next to your cameras.

        Signals in, events out

        Machine signals in. States, counts and events out to your MES, BI or spreadsheets.

        See your machines' downtime reasons before you commit

        A free feasibility study on one cell or line, with an agreed end date. We reply within one business day.

        1. You send a few hours of footage, or camera accessPick the machines you most want to understand.
        2. We come back with a written resultWhich states and reasons we can see, how reliably, and what the first timeline would show.
        3. If it's useful, we scope a pilot togetherA few machines, live, judged against criteria agreed up front.

        Questions about cameras, privacy or IT? Read the FAQ

        Or email info@agnify.ai