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How does Predictive Maitnenance help you?
Nowadays it is common to replace wear parts of machines periodically in order to prevent failures.
Maintenance costs can be saved through predictive maintenance, since maintenance is only carried out when parts of the machine are in danger of breaking.
In addition, machine downtimes can be completely avoided through predictive maintenance, which can lead to a significant increase in the productivity of the machine, increased customer satisfaction, more sales or saved repair costs.
Plan maintenance efficiently
Recognize when maintenance is really necessary.
Reduce maintenance costs
Find out early on when parts are about to break.
Discover impending damage and repair it before it comes to a standstill.
Save repair costs and increase the productivity of your machines at the same time.
What is predictive maintenance?
The aim of predictive maintenance is to avoid machine downtimes and unplanned failures. Contrary to the classic, periodic maintenance, with predictive maintenance the maintenance or inspection takes place when it is actually necessary.
Predictive maintenance means the ability to evaluate the condition of a machine at any time. This makes it possible to predict how long a machine will remain fully operational and when it will need to be serviced. With this information, downtimes can be avoided and maintenance costs can be minimized.
The Westphalia DataLab supported us in a project to analyze the connections between error messages in our construction machines. The project results have given us previously unknown insights into the relationships between our data.
René Ahlgrim, Head of Data Analytics
– Zeppelin GmbH
At a glance
All you need to know about predictive maintenance
With the help of predictive maintenance, malfunctions, errors and failures in machines and components can be detected before they occur and maintenance can be optimally planned. But how does the implementation succeed and which data is required for the implementation? And how exactly is machine learning used in all of this? You can find out all of this here.
Why Predictive Maintenance?
Predictive maintenance combines advantages from the area of cost reduction with a significant increase in performance. The following advantages of predictive maintenance can be specifically named:
- Increase in system and machine availability
- Increase in product and process quality
- Uncover the causes of malfunctions and failures
- Reduction of maintenance and warranty costs
- Minimization of production losses
- Maximizing Overall Equipment Effectiveness (OEE)
- Optimization of spare parts handling
- Increase in the service life of the systems or machines
- Minimizing the risk of accidents
Where can Predictive Maintenance be used?
Machine downtimes are a nightmare for companies and come with significant costs. The aim is therefore to avoid machine downtimes and, in particular, unplanned failures as good as possible. With predictive maintenance, the inspection or maintenance takes place when actually necessary and not according to rigid, routine intervals (preventive maintenance) or even after a defect or failure has occurred (reactive maintenance).
Which industries benefit most from Predictive Maintenance?
The use of predictive maintenance is worthwhile wherever expensive and complex machines are in operation around the clock and unforeseen failures and downtimes lead to high costs.
The industries that are particularly predestined for predictive maintenance solutions include mechanical and plant engineering, medical and electrical engineering, the aerospace and automotive industries, but also the chemical and pharmaceutical industries and, last but not least, the area of Logistics and transport, to name just a few examples.
What kind of data do I need for Predictive Maintenance?
In order to be able to predict failures or errors based on the machine data, two different data sources are required:
- Machine data
- Damage data
Machine data includes all data that is sent by a machine itself during operation or that is recorded by sensors outside the machine. In addition to the machine master data, this includes sensor data and error messages, process states and other quality indicators.
Damage data is understood to be the data that describe the machine failures or error patterns. These are usually generated through quality controls in the factory, repair reports from workshops or warranty applications.
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In the first step, we define and concretise the expectations and goals for the implementation of your use case.
We then check your data sources for quality and quantity and show you what you can expect from us.
Proof of Concept
By means of a proof of concept, we show the expected added value of our solution and provide you with a basis for decision-making.
If the results meet your expectations, we will develop an individual software solution.
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