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Listening to Machines for Smarter Maintenance

At cross-ING’s Competence Center AI , we develop solutions that unlock new possibilities in automated data processing and create more efficient decision trails. Our mission is to provide companies with practical support – from identifying valuable use cases to developing AI systems that generate real value on the shop floor.


Sound-based predictive maintenance

In the past, experienced operators could detect anomalies simply by listening – a rattle or hiss often served as an early warning of failure. Today, artificial intelligence amplifies this intuition. By focusing on critical components and applying ML algorithms to filtered audio streams, companies can shift to proactive, 24/7 monitoring.

This sound-based approach has proven to be a powerful, non-invasive method suitable for retrofit installations. Machine components naturally emit characteristic acoustic patterns, and deviations frequently indicate early-stage faults or functional deterioration. By continuously monitoring and analyzing audio with AI, subtle changes can be detected early, enabling maintenance activities to be scheduled with sufficient lead time.

For specific applications, commercial microphones can be arranged in arrays to capture relevant frequency ranges and angles, reducing the impact of reflections and interferences. Processing data directly at the edge (e.g., via iPC) further simplifies correlation with vision systems, sensors, actuators, and device state monitoring. This edge-first approach ensures only the most relevant data points are processed – reducing cloud costs significantly.


Our White Paper on sound-based predictive maintenance explains the technical background, showcases practical use cases, and highlights how edge computing can cut costs while improving reliability.


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