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⌚Timestamps:
00:00 - Intro
01:44 - Problem
04:10 - Solution
06:59 - Opportunities
07:49 - What is needed to deploy machine learning?
09:37 - Wrap-Up
Modern companies understand the value of data. Data allows factories and plants to run their processes more efficiently to increase capacity and quality.
Raw data has to be analyzed and processed to yield the insights that will actually help a company improve its processes.
Until recently, advanced data analytics was a highly specialized skill that was not available to many manufacturing companies.
In this video, we will show you how recent advancements in machine learning are making it easier to apply machine learning to manufacturing data to extract insights from data and use those insights to make informed decisions on maintenance and quality monitoring.
Machine learning enables manufacturers to move from a preventative maintenance system to a predictive maintenance system.
Many companies have started implementing a digitalization strategy where they use modern technologies like Single Pair Ethernet to collect data from the factory floor, IoT gateways to transmit the data, and on-premise or cloud-based databases to store the data.
Although there are many different ways to analyze large data sets, machine learning is quickly emerging as the most efficient and cost-effective way to generate results.
Until recently, using machine learning on your data involved hiring a team of data scientists to build a bespoke model, waiting for months while they trained the model, and eventually getting some results from the project.
Since data scientists are so rare, it has been hard for manufacturing companies to hire people with the right combination of data science skills and manufacturing knowledge.
What was required to deploy machine learning on the factory floor was an easy-to-use software that allows engineers to build, train, and deploy machine learning knowledge without data science skills.
That is exactly what Weidmüller has delivered with their automated machine learning software Industrial AutoML.
It is a SaaS product that enables anyone to easily train machine learning algorithms on their data and deploy those algorithms either in the cloud or on the factory floor.
Training machine learning models in Industrial AutoML is an easy, three-step process.
You upload your data to Industrial AutoML and the tool visualizes your data in a time series graph.
In your visualized data, you use your domain knowledge to identify normal and abnormal data points.
Once your data has been identified as normal or abnormal, the tool creates a range of machine-learning data models.
Finally, you can deploy the trained model in the cloud or on-premises to detect and predict anomalies.
Tools like Weidmüller’s PROCON-WEB are built on modern web technologies and can be used to collect data from any type of PLC or controller and store it in an efficient database called a historian.
Once data is being collected, manufacturers can use software tools to monitor their data in real-time.
Weidmüller is offering a modular Industrial IoT software kit that can be easily integrated into customer environments.
In addition to PROCON-WEB, you can install ResMa®, to record and monitor energy flows to find inefficiencies in your energy usage.
@WeidmuellerGlobal
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AutoML tutorial from Weidmüller: • Weidmüller Industrial ...
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5 июл 2024
