How is AI used in predictive maintenance?

In predictive maintenance, AI agents are used to optimise the maintenance and monitoring of industrial systems. This is mainly done through three key processes:

1. Data acquisition and processingAI agents continuously collect data from various sensors and systems that indicate the physical and operational conditions of the machines. This includes vibration data, temperature measurements, energy consumption and other operating parameters. This data is pre-processed to reduce noise and extract relevant features.
2. Pattern recognition and modellingAI uses machine learning (ML) techniques, in particular supervised and unsupervised learning methods, to recognise patterns from this data. Using historical data in which known machine failures and their signs are documented, the AI agent trains models to evaluate the current state of a machine. Using methods such as regression, classification or anomaly detection, the AI agent learns to identify early warning signals for potential failures or maintenance requirements.
3. Forecasting and decision-makingBased on the recognised patterns and the trained model, the AI agent can make predictions about the future condition and maintenance requirements of the machines. These predictions enable the maintenance teams to act proactively instead of reacting to failures. The AI can also provide recommendations for optimal maintenance intervals and procedures to maximise operational efficiency and minimise downtime risks.

In addition, advanced AI systems in predictive maintenance often also integrate feedback loops in which the results of the maintenance measures carried out contribute to the continuous improvement of the prediction models. This leads to constant optimisation and adaptation to changing operating conditions and machine states.