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AI Machine Monitoring · Privacy-First

AI Machine Idle & Utilization Monitoring

Know Exactly When Every Machine Is Running, Stopped, or Idle — On Existing Cameras

Your OEE report says 85%, but the shop floor tells a different story — machines sit idle mid-shift, "cleaning" stops stretch far longer than they should, and nobody has real numbers. Retrofitting IoT sensors on 40 mixed-age machines is expensive and slow. Tentosoft's machine utilization monitoring uses your existing cameras — across plants in India and worldwide — to detect machine state, measure downtime, and, during a stop, tell whether the operator is cleaning or idle, as anonymized productivity data, not employee surveillance.

Book a Machine Monitoring Demo → See How It Works
No Sensor Retrofit
Runs on existing CCTV
Real Downtime Data
Automatic, not manual logs
Privacy-First
Anonymized, not surveillance
Hero image — CNC machine on CCTV with a "RUNNING / STOPPED" state label + a utilization dashboard overlay AI machine idle monitoring dashboard showing CNC machine running and stopped states and utilization from existing CCTV
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"Manufacturers lose an estimated 18–23% of available OEE to causes that are invisible without machine-level data — micro-stoppages, idle time between shifts, and undocumented changeovers that never trigger an alarm — a pattern seen on shop floors from India to Europe and North America."
— Context: CII & NASSCOM (India) and global manufacturing productivity estimates
The problem

The Hidden Cost of Machine Downtime You Can't See

On paper, utilization looks healthy. On the floor, capacity quietly leaks away in ways that manual logs never capture and paper OEE never admits. For a plant running high-value machines around the clock, every hidden idle hour is lost output you've already paid for — and it's exactly what proper machine utilization monitoring is meant to expose.

📊 Paper OEE vs real utilization

Manually calculated OEE almost always overstates the truth, because idle time, micro-stoppages, and stretched stops go uncaptured. A line reported at 85% availability may really be running far less — but without machine-level data, nobody can prove it or fix it.

🧽 "Cleaning" stops that run long

A machine scheduled to stop 2 hours a day for cleaning may actually be down longer — part genuine cleaning, part idle. Is it 2 hours of cleaning, or 1 hour of cleaning and 1 hour of idle? Without visibility, the difference is invisible — and expensive.

📝 Manual downtime logs are unreliable

Handwritten downtime registers depend on operators recording their own stops. They miss short interruptions, are filled in from memory at shift end, and can be gamed. The data that reaches your continuous-improvement reviews is incomplete before it even arrives.

🔌 IoT-sensor retrofit is costly & slow

Wiring PLC connectivity or clip-on sensors onto a fleet of mixed-age, multi-brand machines is expensive, disruptive, and often impossible on older equipment with no exposed controller. On a shop floor of 40 machines, the retrofit bill alone stalls the project.

🌐 Global & India context Shop floors worldwide — and especially across India — typically run mixed-age machine fleets with a manual-logging culture and labour-sensitive environments where anything resembling individual surveillance triggers works-council, union, or privacy pushback. A workable solution has to give real machine-utilization numbers without a costly retrofit and without tracking named workers — exactly the gap camera-based, privacy-first analytics is built to fill.
Foundation

What is AI Machine Idle Monitoring?

AI machine idle monitoring is a camera-vision system that detects whether an industrial machine is running, stopped, or idle, measures how long it is down, and — during a stop — classifies whether the operator is performing a legitimate task such as cleaning or changeover or is simply idle. Those signals roll up into utilization and downtime data that can feed your OEE and productivity dashboards. Unlike IoT sensors, which read a machine's controller or vibration signals and must be fitted to each machine, a camera-based machine utilization monitoring system runs on the CCTV you already have — and it can see something a sensor never can: what is actually happening around a stopped machine.
🔒 Privacy-first by design This is productivity and safety analytics, not employee surveillance. The system is designed to focus on the machine's state and anonymized activity type — cleaning, changeover, idle — rather than identifying individuals. It can be configured to log only machine-state and activity metadata (not faces, not identity, not stored personal video), with edge or secure storage options, so outputs are aggregate machine-, line-, and shift-level metrics — supporting data-privacy regimes such as India's DPDPA 2023 and the EU's GDPR. This framing is what makes the insight usable on labour-sensitive shop floors without the pushback that individual monitoring provokes.
Capabilities

What Tentosoft's Machine Monitoring System Does

Five core capabilities that turn passive shop-floor cameras into an accurate, always-on record of how your machines are actually used.

Detection

Machine State Detection

The foundation every other metric is built on. The AI can be configured to classify each machine as running, stopped, or idle — per machine, continuously, 24×7 — from visual cues such as moving parts, spindle motion, output on the line, indicator lights, and operator interaction. No PLC or controller access is required, so it works even on machines that have no data port at all.

  • Running / stopped / idle status, per machine
  • Continuous machine-state detection, day and night
  • Works without any PLC or controller access
Image — CCTV of a machine with a green "RUNNING" / red "STOPPED" state label AI detecting machine running versus stopped state from a CCTV feed
Downtime

Downtime & Idle Logging

Every stop is captured automatically with its start time, duration, and frequency — building an honest, gaming-resistant downtime record. Micro-stoppages that manual logs miss are counted, so your unplanned downtime detection reflects what really happened, not what was remembered at shift end. Stops can be tagged planned or unplanned and rolled up by machine and shift.

  • Automatic downtime logging — duration, frequency, timeline
  • Captures micro-stoppages manual logs miss
  • Planned vs unplanned downtime, by machine and shift
The hook

Cleaning-vs-Idle Classification

The capability that makes the difference. When a machine stops, the system can be configured to tell whether the stoppage is productive — a legitimate cleaning or changeover task — or unproductive idle. So a two-hour stop can be reported as "1h cleaning, 1h idle," turning a blind spot into a number you can act on. It is reported anonymously, at the machine level, as an activity type — never as a judgment of a named worker.

  • Distinguishes cleaning / changeover from idle during a stop
  • Answers "is this downtime actually productive?"
  • Reported as anonymized, aggregate activity — not per person
Image — stopped machine, activity classified as "CLEANING" vs "IDLE" (anonymized) AI classifying a machine stoppage as cleaning versus idle, anonymized
Dashboards

Utilization & OEE Dashboards

State and downtime data roll up into utilization trends by machine, shift, and line. This feeds the availability and downtime side of OEE and can typically be exported to your MES, SCADA, or OEE platform — so your effectiveness numbers finally reflect reality instead of optimistic manual estimates. Compare machines, spot the worst idle offenders, and track improvement over time.

  • Utilization trends by machine, shift, and line
  • Feeds the availability/downtime side of OEE monitoring
  • Can be configured to export to MES / SCADA / OEE dashboards
Privacy

Privacy-First Anonymized Analytics

TentoVision is built to measure machines, not people. It can be configured to log only machine state and anonymized activity type — never identity, never faces, never stored personal video — with edge or secure storage and aggregate machine-, line-, and shift-level reporting, designed to align with data-privacy regulations such as India's DPDPA 2023 and the EU's GDPR. That is what makes it deployable on labour-sensitive shop floors. For broad person-level oversight, see our separate employee monitoring solution.

  • No faces, no identity, no stored personal video
  • Aggregate machine / line / shift metrics only
  • Edge processing option; DPDPA 2023 & GDPR aligned
Universal by design

Customized Analytics, Built Around Your Requirements

There's no fixed template. Every TentoVision deployment is configured around your exact plant — your machines, your shifts, your definition of a stop, and the utilization and downtime metrics your team actually reports on. Whatever the equipment or the goal, the analytics are shaped to what you need.

Universal Compatibility

Works With Your Existing Cameras — No Sensors

Tentosoft's machine monitoring connects to any IP camera using RTSP or ONVIF protocol. No PLC integration, no clip-on sensors, no controller access, no rip-and-replace. Point a camera at the machine zone and go live within days.

+ 200 additional camera brands supported via RTSP and ONVIF protocols
Process

How Camera-Based Machine Monitoring Works

From an ordinary shop-floor camera to a logged, classified downtime event — here is the end-to-end flow of camera-based machine utilization monitoring, calibrated once per machine.

01

Point & calibrate

An existing camera views the machine zone. Each machine is calibrated once so the AI learns its running and stopped signatures.

02

Detect machine state

The AI classifies running, stopped, or idle from motion, output, indicator lights, and operator interaction — continuously, per machine.

03

Measure & classify the stop

When it stops, duration is measured and the stop is classified — cleaning, changeover, or idle — as anonymized activity, not identity.

04

Log & feed dashboard

Every event is logged and rolled up into utilization and downtime trends that can feed your OEE, MES, or ERP dashboards.

See it on your machines — Book a demo →
How it compares

Camera-AI vs IoT Sensors vs Manual Logs

Each approach has a place. Here's an honest comparison — including where IoT sensors have the edge — so you can see why camera-based machine utilization monitoring is often the fastest way to get real data on a mixed fleet.

Factor Camera-AI (TentoVision) IoT / PLC sensors Manual logs
Retrofit neededNone — existing CCTVSensor / controller per machineNone
Works on existing camerasYesN/AN/A
Sees operator activity (cleaning vs idle)Yes — uniqueBlind to itOnly if written down
Legacy / mixed-age machinesWorks — no controller neededOften can't connectWorks
Setup cost & timeLow — live in daysHigh CapEx, slow rolloutLow, but ongoing labour
Gaming-resistant dataAutomatic & objectiveAutomatic & objectiveEasily gamed / forgotten
Direct machine-signal precisionInferred visuallyExact from controllerNone
Feeds OEE / downtime dataAvailability & downtime sideFull machine signalsPartial, delayed
⚖️ The honest take IoT sensors win on one thing: direct, controller-level precision — exact cycle counts and machine signals. What they can't do is see what happens around a stopped machine, and they need a retrofit on every machine. Camera-AI wins on fast, no-retrofit deployment across mixed fleets, plus operator-activity context (cleaning vs idle). For many plants the two are complementary — start with cameras for instant fleet-wide visibility, add sensors on the highest-value machines where signal precision pays back.
Where it's used

Where Machine Idle Monitoring Is Used

Any machine whose activity is visible to a camera can be monitored. The same machine utilization monitoring approach adapts to each machine type's run and stop signatures.

🛠️

CNC Machining

The classic case: high-value CNC machines meant to run around the clock. See true spindle-up time, catch extended cleaning stops, and separate real changeover from idle.

🔩

Stamping & Presses

Track press uptime and stroke activity, log every stop, and surface the micro-stoppages that quietly erode a shift's output on high-volume lines.

📦

Packaging & Labelling Lines

Monitor packaging and labelling machines for stalls and slow-downs, and quantify changeover time between SKUs to rebalance the line.

🧵

Textile Machines

Measure loom and knitting-machine utilization across large halls, flag idle machines during a shift, and log stoppages for maintenance planning.

💊

Pharma Lines

Track utilization and downtime on filling, blistering, and inspection lines where changeover and cleaning stops are frequent — with anonymized activity data.

🏭

Injection Moulding & Assembly

Monitor moulding-machine cycle activity and assembly-station uptime, and identify equipment sitting idle during expected operating hours.

Industries

Built for Manufacturing Sectors — in India & Worldwide

Equipment utilization monitoring tuned to the machines, stops, and shift patterns of each sector — deployed for manufacturers in India and globally.

🚗

Auto Components

Machining, forming, and assembly lines running to tight OEM schedules.

🛠️

CNC & Machining

High-value machines where every idle hour is measurable lost output.

📦

Packaging

High-speed lines where micro-stoppages and changeovers add up fast.

🧵

Textiles

Large machine halls where floor-wide utilization visibility is hard.

💊

Pharma & FMCG

Frequent cleaning and changeover cycles that need honest downtime data.

Explore manufacturing solutions →
Why TentoVision

Why Choose Tentosoft for Machine Monitoring

A machine-utilization layer inside a complete video-analytics platform — not a single-purpose sensor box.

🔌 No sensor retrofit

Runs on existing CCTV. No PLC wiring, no clip-on sensors, no controller access — ideal for mixed-age fleets.

🧽 Activity classification

Sees what sensors can't: whether a stopped machine is being cleaned, changed over, or is simply idle.

🔒 Privacy-first

Anonymized, machine-centric analytics designed to align with DPDPA 2023 and GDPR — deployable on labour-sensitive floors.

🧩 Full analytics platform

Part of Tentosoft's AI Video Analytics on our video management software — run machine monitoring alongside PPE detection and fire & smoke detection on one fleet. See pricing.

🌐 India & global delivery

Microsoft Azure hosting with India-region options, India-based implementation, and remote onboarding for sites worldwide.

🛡️ Camera health included

Bundled CCTV health monitoring flags offline or degraded cameras so your machine-monitoring coverage never goes dark unnoticed. Scale across sites with multi-site monitoring.

Book a Machine Monitoring Demo →
See it on your floor

Find Out What Your Machines Are Really Doing

In a 15-minute session, our India-based team shows how Tentosoft detects machine state from a sample feed, measures downtime, and separates cleaning from idle — on existing cameras, with no sensor retrofit.

Book a Machine Monitoring Demo → Read the FAQ first
FAQ

Machine Idle Monitoring — Frequently Asked Questions

The questions plant managers, production heads, and CI leads ask most when evaluating camera-based machine monitoring — answered directly and honestly.

Machine idle monitoring is a camera-based way of detecting whether a machine is running, stopped, or idle, measuring how long it is down, and — during a stop — classifying whether the stoppage is a legitimate task like cleaning or changeover or is unproductive idle time. TentoVision does this from existing CCTV, with no sensor retrofit, and reports the results as anonymized machine-level productivity data that can feed utilization and OEE dashboards.
The AI can be configured to classify a machine's state from visual cues in the camera view — moving parts, spindle or tool motion, product moving on the line, indicator lights, and operator interaction. Each machine is calibrated once so the model learns its running and stopped signatures, then every state change is time-stamped automatically. Accuracy depends on the camera having a reasonable view of the machine's activity, which is confirmed during setup.
Yes — this is the core use case. When a machine stops, the system can be configured to classify the stoppage by observing anonymized activity in the machine zone, distinguishing a legitimate task such as cleaning or changeover from an unproductive idle period. So a two-hour stop can be reported as, say, one hour of cleaning and one hour of idle. Crucially, this is reported as aggregate, machine-level activity data — not as tracking of any named individual.
No. TentoVision uses your existing CCTV cameras and AI — there's no PLC integration, no clip-on sensors, and no controller access required. That's the main advantage over IoT-based systems on a shop floor of mixed-age or legacy machines, where wiring every machine for sensors is costly, disruptive, and sometimes impossible on older equipment.
Yes. TentoVision connects to existing IP and CCTV cameras via RTSP and is compatible with 100+ camera brands. Each machine zone needs a reasonable camera view of the machine's activity; where a view is obstructed, our team advises on placement during setup. Most sites can be configured to go live within days.
TentoVision primarily captures the availability and downtime side of OEE — automatically logging run, stop, and idle time along with downtime reasons. This data can typically be exported to your MES, SCADA, or OEE platform. Full OEE also depends on performance (speed) and quality inputs, which are usually combined from those systems; TentoVision provides the accurate downtime and utilization foundation that manual logs miss.
No — this is the point of the privacy-first design. TentoVision focuses on the machine's state and anonymized activity type, not on identifying individuals. It can be configured to log only machine-state and activity metadata — not faces, not identity, not stored personal video — with edge or secure storage options, and to report only aggregate machine-, line-, and shift-level metrics. This approach is designed to align with data-privacy regulations such as India's DPDPA 2023 and the EU's GDPR, and to be deployable on labour-sensitive floors worldwide without the pushback individual monitoring provokes.
Employee monitoring is about broad oversight of people. Machine idle monitoring is about the machine — is it running, stopped, or idle, and is a stoppage productive? Any operator-activity signal here is scoped strictly to what's happening around a stopped machine and is reported anonymously and in aggregate. If you specifically need person-level oversight, that's a different tool — see our employee monitoring page. For operator active/idle time at a fixed workstation, our workstation productivity monitoring covers that separately, and phone use on the floor is handled by mobile phone detection.
It can be configured for a wide range of equipment as long as the machine's activity is visible to a camera — CNC machines, stamping presses, lathes, packaging and labelling lines, textile machines, pharmaceutical filling and blistering lines, injection-moulding machines, and assembly stations. Because it doesn't rely on a controller, it works across mixed-brand and older machines that IoT sensors often can't reach.
Typically yes. Utilization and downtime data can be configured to export to MES, SCADA, OEE, or ERP dashboards through standard APIs or exportable reports, so machine-state data flows into the systems your team already uses for planning and continuous improvement rather than living in a separate silo.
Still have questions? Talk to our team →
Book a free demo

Book a Machine Monitoring Demo

In 15 minutes, our India-based team shows how Tentosoft detects machine state from a sample feed, measures downtime, and separates cleaning from idle — on existing cameras, no sensor retrofit, no slide deck.

🔌

No retrofit to see it

We run detection on a real machine feed so you see running/stopped/idle classification live.

🧽

Cleaning-vs-idle in action

See how a stoppage is classified as a productive task or idle — the number you're missing today.

🔒

Privacy-first, explained

We'll walk through the anonymized, machine-centric design and how it aligns with DPDPA 2023.

🇮🇳

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Microsoft Azure hosting with India-region options and an India-based team supporting deployments worldwide.

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