Convincing Reference Projects

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Solve the Challenges

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

Unleash potential

IconPro APOLLO enables more effectiveness, less downtime and lower maintenance costs.

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MONITRATE states, errors and programs

See at a glance whether your machines are operating in good conditions, if they are running and what programs are running.

Availability, performance & EVALUATE quality

Monitor the effectiveness of all machines, of groups, in the simplest way or individual machines.

Failures & PREDICT maintenance

Get notified of upcoming failures or necessary maintenance or calibration activities.


From data to information to added value

Control Data

Sensor Data

Program Data

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Overall equipment effectiveness monitoring


Monitoring of all machine states


Maintenance forecast & request


Control and improvement of performance and quality

Condition Monitoring &
Predictive Maintenance

IconPro APOLLO for better machine efficiency
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Evaluation and monitoring of
OEE parameters

Get detailed or aggregated machine effectiveness reports to evaluate profitability.





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Prediction & request from
Maintenance, calibration and service

Predict necessary maintenance or service work and request it in the most optimal way to minimize costs and downtime.





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Evaluation and improvement of
Performance and Quality

Track machine availability and performance or process capability to optimize productivity and quality.





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Operate more machines more efficiently and reliably

Track machine statuses and program progress or receive notifications – anytime and anywhere.





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Condition Monitoring & Predictive Maintenance: Machine Tools

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

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What are the benefits of predictive maintenance software?

Predictive maintenance with a data set from condition monitoring is a solution that can be used to significantly optimize the profitability of the machine park in production. Specific benefits include:

  • Reduced downtime: By predicting equipment problems using machine learning, predictive maintenance helps reduce downtime caused by unplanned equipment outages. This increases the utilization, productivity and profitability of the connected machines.
  • Minimized maintenance costs: Predictive maintenance helps ensure that maintenance is carried out during planned downtime and not as a result of unexpected failures. Maintenance can thus be planned more efficiently and outside of production times. At the same time, unnecessary maintenance work is avoided, which in turn saves maintenance costs.
  • Longer equipment life: By proactively fixing machine issues, such as wear and tear, before many machine components are affected, predictive maintenance helps extend the life of equipment equipment.
  • Increased safety: Predictive maintenance software helps to increase safety in industrial and manufacturing companies by reducing the risk of plant-related accidents. This is done both by avoiding unnecessary maintenance calls and by reducing the likelihood of potentially safety-threatening machine errors.

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Case Studies

Leading companies use IconPro.

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

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…

Automotive Metal Forming

Automotive Metal Forming

Project Subtitle
Industrial Reinforcement Learning for the Quality Control of Metal Forming Processes

Companies & Partners
Mubea, Tailor Rolled Blanks Gmb…

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 get started with
Predictive Maintenance?

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  • Understanding the use case, goals and requirements

  • Specification of the required machine and sensor data

  • Identification and evaluation of the existing interfaces

  • Implementation and testing of a machine connection

  • Assessment and improvement of data quality

  • Connection of a pilot machine in APOLLO

  • Implementation plan after successful proof-of-concept

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How do condition monitoring and predictive maintenance work together?

By collecting, processing, storing and providing relevant system or machine data, condition monitoring provides the necessary basis for the use of predictive maintenance. The two methods complement each other through their functions on the following levels:

  • Data Acquisition: Predictive maintenance uses data that is acquired via condition monitoring. Sensors and other monitoring devices are installed on equipment and its controls to collect data on their performance and condition. The data includes parameters for temperature, vibration, pressure or sound. At the same time, operating states, program sequences or machine errors are extracted. The data is continuously recorded at certain intervals and stored or made available centrally.
  • Predictive Modelling: Data analysis and correlation are used to derive forecasts that predict when maintenance will be required based on the current condition of the asset. The predictive maintenance models use machine learning to automatically detect anomalies, patterns and trends that indicate when equipment is likely to fail.
  • Maintenance Planning: The information provided by the predictive maintenance models is used for more efficient maintenance planning. The maintenance team uses the information to schedule maintenance when it’s needed, rather than when equipment fails. At the same time, unnecessary maintenance is avoided.

What is an example of predictive maintenance in production?

An example of the use of condition monitoring and predictive maintenance is the operation of coordinate measuring machines (CMMs) and machine tools in production. With both device types, control data such as program sequences or machine errors as well as additional data from temperature and vibration sensors are continuously extracted and stored. The data is made accessible for visualization in a dashboard, so that machine operators know at a glance the operating states of many machine tools on the shop floor or CMMs in possibly different measuring rooms. As soon as a program has finished running, a machine error occurs or the environmental conditions become problematic, this is recognized via the dashboard. This allows one operator to attend to more machines or equipment more reliably without having to be on site all the time.

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

You might also be interested in this:

Machine Vision
Predictive Quality
Trend analysis & Prediction
Software Engineering
Data Screening & Analysis

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What is the definition of predictive maintenance?

Predictive maintenance is a proactive maintenance strategy that uses condition monitoring data to evaluate the condition of assets and predict potential failures. The aim of predictive maintenance is to minimize unplanned downtime by carrying out maintenance before the system fails. At the same time, only necessary maintenance is carried out, which significantly reduces maintenance costs.


What distinguishes predictive maintenance from condition monitoring?

Condition monitoring monitors the condition of systems and uses sensors and measuring devices to collect data that is required and provided for predictive maintenance. The latter applies machine learning algorithms to this data to predict maintenance before a failure occurs.


What are the areas of application for predictive maintenance?

Predictive maintenance is used in various industries to avoid breakdowns and optimize maintenance work. In the manufacturing industry, machine and plant data can be used to carry out needs-based maintenance. In the transport industry, on the other hand, predictive maintenance can be used for transport vehicles for the same purpose. Energy producers can optimize the utilization and maintenance of power plants and wind farms.