Machine Learning (ML) explained simply
Imagine a machine that learns not through fixed instructions, but through experience – just like a human being learns from repetition and observation. That is exactly what Machine Learning does: systems independently recognise patterns from their data, make predictions and optimise themselves automatically – without every single step being predefined in code.
In practice this could mean: a production plant independently adjusts its settings because it has learned that certain patterns indicate wear. Or a system recognises trends in sensor data before a failure occurs.
Background information
Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that enables systems to learn from data and improve without being explicitly programmed. Unlike traditional software, ML systems do not follow a static set of rules – they identify patterns, derive models and apply these to new data.
In industrial applications, especially in the Industrial Internet of Things (IIoT), Machine Learning makes it possible to analyse huge volumes of data from sensors, machine controls and ERP systems in real time. This not only optimises processes, but also reduces downtime and increases product quality. This real-time data analysis is crucial for effective Production Monitoring and process optimization.
Model development in the IIoTIIoT is the internet-based networking of industrial machines, systems, and devices for data collection and process optimization. context
The development of ML models for industrial use follows a structured process:
- Data collection
Sensors, machine controls, Manufacturing Execution Systems and external sources provide the raw data. In the IIoT context this may include temperature curves, vibration data or production volumes. - Data preprocessing
Raw data is often incomplete, noisy or inconsistent. It is cleaned, normalised and, if necessary, enriched with additional contextual information (e.g. environmental conditions). - Feature engineering
Meaningful features are derived from the raw data, e.g. statistical indicators, frequency spectra or trend values. - Model training and validation
The data is divided into training and test sets. Supervised, unsupervised or reinforcement learning is used depending on the objective. - Deployment and monitoring
The trained model is integrated into the production environment – often via edge computing directly at the machine – and continuously monitored in order to be retrained when conditions change.
Applications in the industrial environment
Machine Learning opens up a wide range of practically relevant use cases in IIoT:
- Predictive maintenance
Early detection of machine problems by analysing vibrations, temperatures or power consumption to prevent failures. - Quality control
Image processing models check components for microscopic defects and prevent faulty deliveries. - Adaptive process optimisation
Production parameters are adjusted in real time to minimise rejects and increase throughput. - Energy consumption optimisation
ML models identify inefficient machines or production steps and suggest improvements. - Anomaly detection in sensor data
Deviations from normal patterns are detected immediately – ideal for safety, process stability and quality assurance.
By applying Machine Learning to industrial data, companies can achieve significant gains in efficiency, leading directly to improved OEE Optimisation and overall productivity.