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Get to know the most relevant application scenarios and added values for Condition Monitoring & Predictive Maintenance for machine tools.
Learn more about concrete use cases and added values by using Condition Monitoring and Predictive Maintenance for Metrology.
Get to know the most relevant application scenarios and added values for Condition Monitoring & Predictive Maintenance for machine tools.
Learn more about concrete use cases and added values by using Condition Monitoring and Predictive Maintenance for Metrology.
MONITORATE 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. For that, your machines are equipped with sensors that collect data in real time.
Availability, performance & EVALUATE quality
Algorithms analyze the collected data. Easily monitor the effectiveness of all machines, groups or individual machines.
Failures & PREDICT maintenance
Get notified of upcoming failures or necessary maintenance or calibration activities.
Overall equipment effectiveness monitoring
Monitoring of all machine states
Maintenance forecast & request
Control and improvement of performance and quality
Get detailed or aggregated machine effectiveness reports to evaluate profitability.
Predict necessary maintenance or service work and request it in the most optimal way to minimize costs and downtime.
Track machine availability and performance or process capability to optimize productivity and quality.
Track machine statuses and program progress or receive notifications – anytime and anywhere.
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:
Project Subtitle
Data-based evaluation of the wire electrical discharge machining process
Companies & Partners
WBA Aachener Werkzeugbau Akademie GmbH, Mak…
Project Subtitle
Automation of network edge infrastructure & applications with artificial intelligence
Companies & Partners
Opel Automobile GmbH, Frau…
Project Subtitle
System for adaptive phototonic surface testing with adaptive image evaluation in combination with a cleaning system.
Companies and Partners
P…
Project Subtitle
Data-based evaluation of the wire electrical discharge machining process
Companies & Partners
WBA Aachener Werkzeugbau Akademie GmbH, Mak…
Project Subtitle
Automation of network edge infrastructure & applications with artificial intelligence
Companies & Partners
Opel Automobile GmbH, Frau…
Project Subtitle
System for adaptive phototonic surface testing with adaptive image evaluation in combination with a cleaning system.
Companies and Partners
P…
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.
Annual Maintenance, Downtime, Administration Costs
1 k€
Realize Annual Savings of
0 k€
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:
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.
The introduction of Predictive Maintenance (PdM) brings with it challenges such as integrating suitable sensors, choosing the right software solution, expanding the IT infrastructure, and ensuring sufficient server capacity. In addition, the shortage of skilled workers requires targeted training or new hires, and data security must also be taken into account.
IconPro is happy to advise you on how to successfully meet these challenges. We offer comprehensive support in the planning and implementation of PdM solutions so that you can benefit from the numerous advantages of this technology.
Machine Vision
Predictive Quality
Trend Analysis & Prediction
Software Development
Data Screening & Analysis
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
What is the definition of Predictive Maintenance?
Predictive maintenance is a proactive maintenance strategy that uses data from Condition Monitoring to evaluate the condition of equipment and predict when it will fail. The goal of Predictive Maintenance is to minimize unplanned downtime by performing maintenance before equipment fails. At the same time, only necessary maintenance is performed, which significantly reduces maintenance costs.
What are the use cases for Predictive Maintenance?
Predictive Maintenance is used in various industries to prevent failures and optimize maintenance work. In the manufacturing industry, for example, machine and process data can be used to perform demand-driven maintenance. In the transportation industry, on the other hand, predictive maintenance can help improve the reliability of vehicles. Energy producers can optimize the operation of power plants and wind farms. In healthcare, for example, medical equipment and devices can be operated more reliably.
What is the difference between Predictive Maintenance and Preventive Maintenance?
Predictive Maintenance (PdM) uses real-time data to predict the maintenance requirements of machines and systems, while Preventive Maintenance (PM) is time-based and performs maintenance at fixed intervals. Both methods aim to avoid production downtime. However, the major advantage of PdM over PM is that it takes the actual condition of the machine into account, thus avoiding unnecessary maintenance.
