Introduction: AI and MRO Problem Solving
Maintenance, repair and operations (MRO) represents a significant cost and potential point of weakness for manufacturing companies, particularly in the Italian machine tool sector. Unplanned downtime not only interrupts production, but also generates high costs due to emergency labor, urgent spare parts and lost revenue. The main challenge is to identify failures early, before they manifest as catastrophic outages. In this context, the integration of smart sensors with embedded artificial intelligence (AI) capabilities emerges as a critical solution.
This technology transforms the reactive or time-based approach to maintenance into a predictive and prescriptive model. Embedded AI allows machinery components to monitor their health, detect anomalies and diagnose potential problems autonomously, reducing the reliance on human intervention for initial inspection and analysis. The objective is to maximize plant uptime, optimize the use of resources and improve operational safety, aligning with reliability standards such as UNI EN 13306.
How It Works: The AI Technology Behind Self-Diagnosis
The principle behind self-diagnosing sensors is based on the continuous collection of operational data and their local processing using AI algorithms. The process includes several stages:
- Data Acquisition: High-precision sensors (accelerometers, thermistors, pressure sensors, current transducers) monitor critical parameters such as vibration (frequency and amplitude), temperature (anomalous hot spots), pressure (anomalous fluctuations) and energy consumption. The sampling frequency can vary from 1 kHz to 100 kHz, depending on the dynamics of the phenomenon to be detected (for example, vibrations on high-speed bearings).
- Edge processing: The acquired raw data is processed directly on the sensor or on a local gateway (edge device). This on-machine processing, performed by AI-optimized microcontrollers or FPGAs, reduces latency and the volume of data to be transmitted, while preserving network bandwidth. The AI algorithms used include lightweight neural networks, machine learning algorithms for anomaly detection (e.g. Isolation Forest, One-Class SVM) or pre-trained pattern recognition models.
- Anomaly and Pattern Detection: AI models learn behavior
