AI

Artificial Intelligence (AI) explained simply

Imagine your computer or your machine could think like a human – only in bits and bytes. That is exactly what Artificial Intelligence is about: machines learn to recognise patterns, make decisions and solve problems – almost as if they had a kind of digital intuition. Think of a factory control system that automatically sets the optimal production parameters, or a machine that detects when a part is wearing out before it breaks – and all of this without you having to intervene manually. Sounds almost like science fiction? But it is no longer!

Background information

Artificial Intelligence – or AI – refers to the use of algorithms and models that enable machines to learn from data, draw conclusions and perform tasks independently. In the industrial context, especially in the Industrial Internet of Things (IIoT), AI plays a central role: it analyses huge amounts of sensor data in real time, identifies correlations and supports intelligent automation.

An essential aspect is the use of machine learning (ML) and deep learning (DL), in which neural networks recognise complex patterns – for example in detecting material fatigue in a machining process or predicting machine maintenance (predictive maintenance). This not only increases efficiency but also reduces downtime.

In addition, AI opens up new possibilities in the IIoT field in quality assurance and production optimisation: through anomaly detection, image-based defect inspection or adaptive process management in real time. AI-supported systems offer flexibility and resilience – they adapt to changing production conditions and continuously optimise. This makes AI a cornerstone of modern digital factories.

Technical methods of AI in IIoT

In industrial practice, different AI methods are used, selected and combined depending on the application:

  • Machine learning (ML): Algorithms learn from historical and current sensor data to make predictions or detect anomalies.
  • Deep learning (DL): Deep neural networks process complex data such as images or acoustic signals and recognise patterns invisible to humans.
  • Time series analysis: Particularly important for production and sensor data in order to predict trends, deviations and failures.
  • Computer vision: Image processing systems identify defects, measure components or visually monitor production processes.
  • Natural language processing (NLP): Processing of text and speech data, e.g. for operator assistance systems or automated documentation.

These methods are often combined – for example deep learning for image recognition and time series analysis for process monitoring – to create a holistic, robust IIoT system.

Use cases in industrial practice

AI in IIoT is no longer a promise for the future, but a real component of modern manufacturing:

  • Predictive maintenance: By analysing sensor data, failures are predicted and maintenance is planned in good time – thus downtime is significantly reduced.
  • Quality assurance: AI-based systems detect microscopically small defects in products that are difficult to see with the human eye, ensuring consistently high quality.
  • Adaptive process control: Production parameters such as temperature, pressure or feed are automatically adjusted in real time to minimise rejects.
  • Energy optimisation: AI models control machines so that energy consumption and production output are optimally balanced.
  • Supply chain optimisation: By predicting material requirements and delivery times, bottlenecks can be avoided and inventories optimised.

These use cases show how AI makes the leap from pure data analysis to autonomous, learning systems.

Further information and links

Privacy Overview

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.

Strictly Necessary Cookies

Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.

Analytics

This website uses Google Analytics to collect anonymous information such as the number of visitors to the website and the most popular pages.

Keeping this cookie enabled helps us improve our website.

Powered by  GDPR Cookie Compliance