Industrial leaders partner with us

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Solve the Challenges

Unplanned downtime, insufficient effectiveness or expensive machine maintenance reduce productivity.

Unlock the Potential

Empower your production to be more productive and reliable with lower downtime and maintenance.
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Track Conditions, Events, Operations

See at a glance if your machines are working as intended and get notifications otherwise.

Evaluate Availability, Performance, Quality

Monitor the Overall Equipment Effectiveness of groups of or individual machines easily.

Predict & Minimize Downtime & Maintenance

Get to know about downtime risks or necessary service activities in time to plan most cost-efficiently.


From data to information

Control Data
Sensor Data
Program Data
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Monitor Overall Equipment Effectiveness.


Handle and supervise many machines.


Predict & request maintenance & service.


Monitor & improve performance & quality.

Predictive Maintenance Solutions

IconPro APOLLO for better machine effectiveness.
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Monitor & Track OEE

Get summarized or detailed evaluations for controlling the machines’ effectiveness.

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Predict & Request Services

Predict & request maintenance or service operations for minimal costs & downtime.

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Evaluate & Improve
Performance & Quality

Track & analyze availability, performance and quality to eliminate issues decreasing productivity.

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Handle & Supervise
Many Machines

Know the current operation and routine progress states
of all machines, assure proper operation conditions.


Trusted by

Mechanical – Wire Eroding
Mechanical – Wire Eroding

Project Subtitle
Data-based evaluation of the wire electrical discharge machining process

Companies & Partners
WBA Aachener Werkzeugbau Akademie GmbH, Mak…

Energy – Minimize Consumption
Energy – Minimize Consumption

Project Subtitle
Automation of network edge infrastructure & applications with artificial intelligence

Companies & Partners
Opel Automobile GmbH, Frau…

Manufacturing – Visual Inspection
Manufacturing – Visual Inspection

Project Subtitle
System for adaptive phototonic surface testing with adaptive image evaluation in combination with a cleaning system.

Companies and Partners

Minimize maintenance or calibration costs, reduce service + downtime!

Enter your Parameters
IconPro APOLLO saves dozens of thousands of euros per year by predicting and dynamizing maintenance and service efforts or reducing downtime of assets to a minimum.

Asset Value

1 k€

  • 5
  • 1000

Number of Assets


  • 1
  • 1000
  • Annual Maintenance, Downtime, Administration Costs

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    1 k€

  • Realize Annual Savings of

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    0 k€

How to start?

We will guide you through 6 systematic steps in less than 6 months. Get a reliable proof-of-concept in the most cost-efficient way!
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  • Understand required machine information

  • Complete standardized machine interfaces with us

  • Get a running and validated pilot deployment

  • Know the return on investment

  • Get roll-out roadmap after proof of concept


Our Offering

Make it as easy, reliable and cost-efficient for you as possible.
We cover the full range of services.
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Condition Monitoring & Predictive Maintenance: Measurement Machines

Get to know the most relevant application scenarios and added values for Condition Monitoring & Predictive Maintenance for measurement machines.

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Our team will be happy to help you. Get in touch!



What is the definition of Condition Monitoring and Predictive Maintenance?

Predictive maintenance is a proactive maintenance strategy that uses data and analysis from Condition Monitoring to evaluate the condition of assets and predict their failure. Predictive Maintenance is subject to the objective of minimizing unplanned downtime by performing maintenance before equipment failure occurs. At the same time, only necessary maintenance is performed, which significantly reduces maintenance costs. Condition Monitoring is usually limited to the use of sensors and other monitoring devices that collect data on the performance and condition of equipment. The collected data is stored in a structured manner and made available to predictive maintenance algorithms via standardized interfaces.


How do Predictive Maintenance and Condition Monitoring work together?

  • Data Collection (Condition Monitoring): Sensors and other monitoring devices are installed on assets to collect data about their performance and condition. The sensors collect data on various parameters such as temperature, vibration, pressure or sound. At the same time, information is extracted from the asset controls, such as program sequences or machine faults. Data from different assets are continuously collected at specific time intervals and stored centrally in accessible databases.
  • Predictive Modeling (Predictive Maintenance): Based on data analysis, predictive models are created that predict when maintenance is required based on the current condition of the asset. The predictive models use statistical and machine learning algorithms to analyze the data and identify anomalies, patterns, and trends that indicate when equipment failure is likely.
  • Maintenance Planning (Predictive Maintenance): The information provided by the predictive models is used to plan maintenance more efficiently. The maintenance team uses the information to schedule maintenance when it is needed, rather than when equipment fails. This way, maintenance can be performed during the scheduled downtime, rather than when an unexpected failure occurs.


What are the benefits of Predictive Maintenance and Condition Monitoring?

  • Reduced Downtime: By proactively identifying and correcting equipment problems, Predictive Maintenance helps reduce downtime caused by unplanned equipment failures. This can lead to higher productivity and lower costs for the business.
  • Minimized Maintenance Costs: Predictive Maintenance helps minimize maintenance costs because maintenance can be performed during planned downtime rather than as a result of an unexpected failure. In addition, by proactively identifying equipment problems, companies can schedule maintenance more efficiently, reducing the need for emergency repairs. At the same time, unnecessary maintenance calls are avoided, which in turn saves on maintenance costs.
  • Longer Equipment Life: By proactively addressing equipment issues, Predictive Maintenance can help extend equipment life. This can lead to reduced costs for the business, as equipment is less likely to need to be replaced more frequently.
  • Increased Safety: Predictive Maintenance helps increase safety in industrial and manufacturing operations by reducing the risk of equipment-related accidents. It does this by both avoiding unnecessary maintenance calls and reducing the likelihood of potentially safety-threatening machine failures.


What is an example of a use case for Predictive Maintenance and Condition Monitoring?

An application example for Condition Monitoring and Predictive Maintenance would be the operation of Coordinate Measurement Machines (CMMs) and machine tools in production. For both types of devices, control data such as program sequences or machine errors, as well as additional data from temperature and vibration sensors, would be continuously extracted and stored. The data is made accessible for visualization in a dashboard, so Condition Monitoring allows machine operators to know at a glance the operating states of many machine tools on the shopfloor or CMMs in different measuring rooms, if necessary. As soon as a program finishes running, a machine error occurs or environmental conditions become problematic, this can be detected via the dashboard. In this way, an operator can more reliably take care of more machines or devices without having to be present locally all the time.

At the same time, via the Predictive Maintenance functionalities of the software, the operator receives a message as soon as a critical asset condition is predicted in the short or medium term. This can refer to the wear of a tool for the machine tool, to a machine component such as a bearing, or to the measurement capability of the CMM. In this way, necessary maintenance, calibration or service activities can then already be predicted and planned before unplanned and thus expensive downtime occurs.