CONDITION MONITORING &
PREDICTIVE MAINTENANCE

Added value through data

Being in the right place at the right time

Each year, significant financial losses accrue due to the failure of technology, plants and machines. Small causes often have huge effects in this context because production environments optimised for efficiency and productivity are often composed of closely intertwined production steps. Unexpected machine or component failures can bring an entire production line to a grinding halt. The essential lesson is that the costs of production downtimes are generally far superior to the costs of troubleshooting.

Why Condition Monitoring?

KNOW THE STATE AND CONDITION OF YOUR MACHINES

In which condition is the spindle? When does cooling lubricant have to be refilled? Why is there a pressure drop? Does the hydraulic pump motor work as expected? Is the temperature within the set range?
Condition monitoring helps you to access this and other information about your existing machines and plants via different parameters. This has the advantage that production staff and maintenance personnel can guarantee a permanent condition monitoring, enabling them to react before a component fails.

Why Predictive Maintenance?

DON’T REPAIR – PREVENT

Predictive maintenance goes one step further. However, a reliable condition monitoring is essential for predictive maintenance. Existing data can be used as a basis for statistical evaluations, pattern recognition can hint at causes or effects, and neural networks with machine learning algorithms can learn from and work on the data. This allows to detect, for example, wear patterns, anomalies and outliers.

compacer’s IoT solution

Our IoT solution for Condition Monitoring and Predictive Maintenance can be used in various areas. In most cases, it is implemented where machines and equipment are in use. compacer provides support where data is concerned: to enable the monitoring of processes and machine status without interfering with the existing automation,
the software platform edbic with its two components IoT gateway and IoT hub, will prove to be very helpful. The main purpose of edbic is to collect and harmonize data from controllers, machines, devices, processes and production and to forward these data to a superordinate IT system for analysis and aggregation.
CONDITION MONITORING &
PREDICTIVE MAINTENANCE

THE SPECIAL THING ABOUT IT:

The integration takes place both horizontally and vertically – from the shop floor to your accounting department – thanks to our e-invoicing solution.

The monitoring of pressure, power consumption, vibration, temperature, rotational speed and many other parameters is done in real time. Configuring the monitoring is a breeze. By setting thresholds according to your rules, anomalies, as well as errors and outliers are detected in a targeted manner to provide early warnings.


This rule-based evaluation of information enables condition monitoring as well as scheduled and predictive maintenance – no more unplanned downtimes!

The data can be retrieved from any terminal, so the system provides full transparency and you can easily monitor production and optimize your production.

Customer case:
Our solution in use

BURKHARDT+WEBER



CONDITION MONITORING AS A STABLE FOUNDATION FOR A WAY TOWARDS PREDICTIVE MAINTENANCE


Planning capability of production processes and simultaneous cost reduction


Find out more

For better decisions throughout your company!

All advantages at a glance:

 

USER ADVANTAGES:

  • Root cause analysis and problem solving:
    Improve your daily efficiency and gain better insight into the causes of failures. Handle the problem remotely at any time!
  • Less expensive maintenance
    By integration into your PPS, APS, MES or ERP system, you can take your production planning to a higher level. And what’s more: it decreases your costs!
  • Full user flexibility:
    Save costs in your repair strategy: real-time condition monitoring and various types of data analysis for predictive maintenance reduce maintenance downtimes.
  • Better production planning:
    View the collected data on a central HMI or directly in the cloud. Create your personalized dashboard with individual widgets and receive alerts, for example via WhatsApp, about irregularities.
  • Quality assurance in production:
    Determine individual KPIs that are a helpful starting point for your long-term production increase. With the newly gained information, you will not only be able to improve production processes, but also to improve product quality.


FOR YOUR SAFETY:


FOR OUR CUSTOMERS:

We ensure the security and protection of your data with established encryption standards and proven gateway functions to various cloud solutions. With edbic as an IoT gateway, you are better prepared for cyber security threats for all kinds of industrial plants.

If you require further information on our blockchain technology and its use in the IoT environment, please contact us!
Of course, your customers also benefit from numerous advantages, such as an improved customer service based on the increased process transparency and optimized information capability!




A pioneer in machine learning

The Center Smart Services on the campus of RWTH Aachen University has provided initial insights into the results of a current market analysis of industrial machine learning. It shows clearly that compacer, one of the most important German IT service and software providers for the reliable and format-independent data exchange of IT systems, plays an important and pioneering role in this market.
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How compacer makes predictive maintenance work


Which data will be used to gain insights and predict maintenance works depends on the individual use case. Most machines and systems already generate their own data. Where this is not already the case, a digital retrofit will make it possible..

Our business integration cluster edbic reads the data and processes and evaluates them. It analyses them in real time according to definable rules and stores them according to various strategies. If desired, the data contained within the historical memory can be fed to all kinds of analytical systems. The data can then be automatically evaluated by machine learning and statistical methods.

Our support for your successful implementation!

1
Existing and required data sources are connected via edbic and all data will be integrated. In many cases, Extract Transform Load (ETL) processes will set up and manage an asynchronous analytics database, the so-called data warehouse.
2
In order to ensure a complete and error-free data management, edpem will monitor the processes, log any events and alert an operator if necessary. To ensure a particularly high level of data quality, we can also set up tools for data cleansing.
3
In the case of real-time and operational intelligence, edbic will process all data directly, e.g. machine sensor data in manufacturing. Often, this will be realised via synchronous data streaming, where the data will be processed instantly upon creation. edbic will still monitor all data streams.
4
Predictive analytics uses data mining in tandem with neural networks and statistical methods to develop models that provide a prediction based on the current data situation. These are evaluated by means of widely used procedures, such as pattern recognition and functional procedures. Additionally, you will always have the possibility to transfer the data to your chosen business intelligence tool.

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Interested? No problem!

Our experts will help you with IoT readiness.

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