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Anomaly detection + Predictive Maintenance with audio analysis

Service description

We provide advanced audio analysis solutions for detecting anomalies in industrial environments. By leveraging AI-powered sound recognition, the service identifies unusual patterns or malfunctions in machinery, enabling early detection of issues and reducing downtime through proactive maintenance.
Expected results: Early detection of potential equipment failures, reduced maintenance costs, minimized unplanned downtime, improved predictive maintenance strategies, and enhanced operational continuity through proactive alerts.
Methodology: Assessment: Review the current method of detecting anomalies or malfunctions in equipment, focusing on areas where audio signals can provide early indicators.
Data Collection: Install audio sensors to capture operational sounds from machinery.
AI Model Development: Train AI models to recognize normal and abnormal sound patterns associated with machinery.
Real-Time Analysis: Implement the audio analysis model to monitor sounds in real time and detect anomalies.
Alert System: Set up an alert system to notify operators of detected anomalies.
Model Refinement: Continuously improve the models based on feedback and evolving operational conditions.
Target: Manufacturing & Automotive
  • Organisation: REPLY
  • Type: Experiment
  • SUT/E: Predictive AI models
  • Average duration: 4.0 -- 6.0 months
  • Contact: m.diflorio@reply.it

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Co-Funded by the European Union Under grant agreement number 101100707

Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Directorate-General for Communications Networks, Content and Technology. Neither the European Union nor the granting authority can be held responsible for them.

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