Wann ist die Software von aiomatic die richtige Wahl für meine Wartungsstrategie?

Die KI-Software von aiomatic passt zu Ihrem Projekt und Ihrer Wartungsstrategie, wenn Sie eine Lösung suchen, die kontinuierlich Maschinendaten analysiert und Anomalien in Echtzeit erkennt. Unsere Software konzentriert sich auf die Analyse und Erkennung von Abweichungen, jedoch ohne Optimierung von Produktionsparametern, automatisierte Ersatzteilbestellungen oder die Erstellung von Wartungsplänen.

Welche Sensorik sollte bei meiner Maschine vorhanden sein?

Ihre Maschine sollte bereits über Sensoren verfügen, die zentrale Prozessgrößen wie Temperatur, Druck oder Durchfluss messen. Bei Motoren sind Sensoren für Leistung, Solldrehzahl und Schwingungen essenziell. Falls eine Nachrüstung erforderlich ist, beraten wir Sie gerne und vermitteln Ihnen Unterstützung durch unsere erfahrenen Partner.

Wie viel Datenexpertise ist auf Unternehmensseite notwendig?

Mit unserer Lösung erhalten Sie eine unkomplizierte, einsatzbereite KI-Software zur Maschinendatenanalyse. Für den Projekterfolg sind jedoch weiterhin Ihre Prozess- und Maschinenexperten, Wartungspersonal und Entwicklungsingenieure unverzichtbar.

Müssen die Daten meiner Maschinen bereits digital erfasst werden?

Die Maschinen sollten über einen mittleren bis hohen Digitalisierungsgrad verfügen, sodass Zustandsdaten bereits digital erfasst werden. Am besten erfolgt dies in regelmäßigen, festen Intervallen. Bei eventbasierter Datenerfassung ist ebenfalls ein klar definierter Rhythmus erforderlich (z. B. stündlich).

Use Cases for Maintenance Software in Various Industries

The biggest potential for you

Success factors for a Predictive Maintenance project with aiomatic

Our Predictive Maintenance software helps you optimize your machine performance and maintenance cycles. For maximum added project value, we recommend these basics:

Your machines generate 5 - 200 data channels each
that we should digitally monitor for you

You can provide us with your machine data via digital interfaces such as OPC UA

You have an in-house process/machine expert as well as

Customer success stories

Monitoring gearmotors at Canyon Bicycles

Challenge & goal

Canyon is looking for an innovative solution for monitoring critical gearmotors in order to avoid failures due to undetected errors and to relieve the maintenance team through automated processes.

Our solution

Thanks to an edge device with an LTE connection, independent of Canyon IT, our software monitors and analyses high-frequency vibration and operating data around the clock in order to identify deviations at an early stage.

Benefits for Canyon

Canyon saves costs through the targeted use of spare parts, avoids failures thanks to error forecasts, relieves personnel resources and increases the efficiency of production lines.

Monitoring of gear motors by an engineer

Hidden machine faults — and how to identify
them at an early stage

Machine faults often go unnoticed for a long time — until it is too late. Engines, transmissions and bearings are particularly affected. Typical damage can be identified early on in operating and sensor data — if you know which parameters are decisive during monitoring.

PDF document of machine types and their typical faults

Conveyor belt: Common types of faults

Belt wear due to abrasion or aging

Bearing damage on rollers or pulleys (lead blockage or squeak)

Drive defect (transmission damage or motor overload due to traffic jams)

Material jam and jamming of conveyed goods

Conveyor belt: Monitorable parameters

Vibration sensors on the gearbox bearing (early detection of bearing defects)

Temperature sensors on engine and gearbox (overheating)

Speed and torque

Current monitoring on the drive motor (blockage/overload detection)

Centrifugal pump: Common types of faults

Leaking mechanical seal

Roller bearing wear or failure (e.g. as a result of vibrations)

Impeller imbalance or shaft misalignment

Cavitation under unfavorable operating conditions

Centrifugal pump: Monitorable parameters

Vibration sensors on the bearing housing (e.g. monitoring of imbalance,)

Temperature sensors on motor and bearings (early detection of overheating)

Flow sensors (flow rate monitoring)

Current measurement of drive motor

Agitator: Common types of faults

Bearing exhaustion and transmission damage

Gearwheels can be damaged due to overload or material fatigue

Seal defects: Worn mechanical seals

Drive overheating: high mixing viscosities or blockages

Agitator: Monitorable parameters

Vibration monitoring at bearings and gearbox

Temperature sensors on motor and bearing/gearbox (display of overheating)

Motor torque or power consumption

Speed sensors for monitoring the target actual speed

Get insights into other machine types & typical faults

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Customer success stories

Monitoring of gravure presses at Nitto

Challenge & goal

Nitto's printing press combines complex processes such as wrapping, printing, embossing and winding in one unit. A failure of individual components can shut down the entire production process. The aim is to prevent such failures through early fault detection.

Our solution

Our software integrates data from the rotogravure press's PLC, which records not only operating data but also temperature and vibration values. The analysis of 190 data channels shows that orbital speed has a decisive influence. With this knowledge, our maintenance assistant reliably detects deviations, even when operating parameters fluctuate.

Benefits for Nitto

Our software continuously monitors the condition of the machine and detects problems at an early stage. For example, our software has already identified a bearing defect and a leak in the silicone hose and repaired them in good time.

Monitoring of a flexographic printing press

Ensuring energy supply and efficiency

Requirements on energy suppliers are increasing — traditional maintenance is reaching its limits. In our white paper, we explain how AI optimizes processes and redefines machine monitoring in the energy sector.

Why traditional machine monitoring is reaching its limits

How AI monitoring effectively protects complex components from failures

What to consider when getting started with AI-based monitoring

Ready for the future of energy supply?
Discover efficiency potential now.
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Frequently asked questions

Here you will find answers to the most frequently asked questions about the requirements for using our software and previous use cases.

When is aiomatic software the right choice for my maintenance strategy?

aiomatic's AI software fits your project and maintenance strategy if you're looking for a solution that continuously analyses machine data and detects anomalies in real time. Our software focuses on analyzing and identifying deviations, but without optimizing production parameters, automated spare parts orders, or creating maintenance plans.

Which sensors should my machine have?

Your machine should already have sensors that measure key process variables such as temperature, pressure, or flow. Sensors for power, target speed and vibrations are essential for motors. If a retrofit is required, we will be happy to advise you and provide you with support from our experienced partners.

How much data expertise is required on the company side?

With our solution, you get uncomplicated, ready-to-use AI software for machine data analysis. However, your process and machine experts, maintenance personnel and development engineers are still essential for project success.

Does the data from my machines already have to be digitally recorded?

The machines should have a medium to high degree of digitization so that status data is already recorded digitally. It is best to do this at regular, fixed intervals. Event-based data collection also requires a clearly defined rhythm (e.g. hourly).

Any further questions?

Our mechanical engineer Joel is happy to help you!

Contact sales

Success factors & Use Cases of our software

Which requirements will allow you to benefit most from our software?

High level of digitization of your machines as well as a secure and scalable data infrastructure

Reliable sensor data that allows conclusions to be drawn about the status of the systems

Machine data is digitally recorded periodically or event-based at least once an hour

Success factors for a Predictive Maintenance project with aiomatic