Production facilities in the past have been, and often still are, monitored using knowledge that was created by humans and is therefore often incomplete and difficult to verify and maintain. Alternatively, data-driven approaches offer a promising solution to automatically process large amounts of data that casn be used for monitoring and preditcive maintenance. Data relevant for monitoring is fed to algorithms and automatically generates the necessary knowledge. The following issues need to be considered: Data acquisition (often in real time) and storage, data analysis, human-machine interfaces, as well as feedback and control mechanisms.
In this course, we explain artificial intelligence (AI) and machine learning (ML) approaches in production facilities using services such as predictive maintenance, diagnostics and energy optimisation. We present current success stories with different use cases and processes from different industries as examples.
KI im Team lernen – in zwei Tagen vermitteln wir Ihnen und Ihrem Team die wichtigsten Grundlagen für ML und KI in der Industrie.
In this practical workshop, we provide easy-to-implement solutions for integrating AI and ML into existing production systems. The focus is less on deep understanding of the algorithmic methods and more on the opportunities and problems of the heterogeneous ML approaches. Pre-conditions for the use of ML learning methods in production can be examined with this course, advantages and disadvantages of different methods can be weighed up and implemented in own projects with the help of concrete examples.
- Introduction to AI/ML
- Data acquisition and data semantics
- Statistical data analysis
- Dimensionality reduction
- Neural Networks
- Buy or do it yourself?
- Ethics and law
- Education and training
- Research / Future
- Practical exercises with Python / Jupyter notebooks:
- Anomaly detection with probability functions, neural networks, CNNs.