Key to innovative and efficient engineering
Artificial Intelligence is the ability of an IT system to show human-like intelligent behaviors. AI-based assistance systems understand the intention of the user and derive the task to be solved. Even without explicit programming, they generate suitable action plans and perform actions that enable efficient problem-solving. AI-based assistance systems can possess various capabilities:
The AI Marketplace AI tags hierarchically structure the AI methods relevant to engineering. The total of 99 AI tags can be divided into 6 main categories of AI methods:
‘Signal Processing’ encompasses the handling of signal data. This includes all applications of time series and sensor data in general, such as vibration or temperature data. It mainly includes methods of supervised learning, which means learning algorithms that use labeled training datasets.
Application example:
an example in engineering is the analysis of machine signals to detect anomalies and associated faulty behavior.
‘Decision Support’ deals with methods of decision support in complex environments.
Application example:
an example of this is case-based reasoning, where problem solutions are determined through analogy, meaning similar problems are approached with similar solutions.
‘Modeling Languages’ deal with formal models. In the context of engineering, this particularly refers to diagrams of modeling languages such as UML or SysML.
Application example:
important examples include the transformation of platform-independent models into platform-specific models or the translation between different formalisms.
‘Natural Language Processing’ encompasses all methods for handling human language in speech and text.
Application example:
examples in the engineering context include the analysis of requirement documents or customer reviews.
‘Computer Vision’ encompasses all methods for handling image and video data. This includes activities related to analyzing and understanding camera images.
Application example:
a classic example is object recognition, where objects such as components in an image are localized and labeled.
‘Knowledge Discovery’ encompasses activities related to discovering and representing knowledge. This includes the discovery of knowledge in large data sets using methods of data mining, as well as the handling of structured knowledge, such as knowledge graphs. It mainly involves methods of unsupervised learning, which means learning without labeled training data.
Application example:
EA classic example is cluster analysis, which involves grouping data based on underlying similarity structures.
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