Robust and cost-efficient monitoring solutions are needed for timely detection of anomalies and quality issues in machines and vehicles. Today, monitoring solutions often rely on sensors that are permanently installed and/or integrated on the components or subsystems. However, in some applications this is not feasible, because:
- it is too costly to install monitoring systems on all components which might fail and
- for some machines it is impossible to integrate a monitoring system.
Therefore, there is an industrial need for objective, cost-effective, non-intrusive and easy deployable monitoring of the condition of machinery and final products that works robustly in an industrial environment. Acoustic monitoring would be a solution but there is a lack of methods and tools that can deal with low signal to noise ratio caused by background noise and uncontrolled acoustic disturbances in industrial environments.
Secondly, there is a lack of methods and supporting tools to efficiently deploy and validate an acoustic monitoring solution in different products, machines and/or environments.
The goal of the project is to deliver:
- A toolchain to isolate relevant acoustic sources from background noise in an industrial environment;
- Methods and supporting tools for improved model training to discriminate relevant signals from disturbances in an industrial environment, and;
- Methods and supporting tools for easier deployment and validation of the acoustic monitoring system.
By early detection of anomalies, these tools will allow company partners to reduce the costs associated with unexpected machine downtime by optimizing the maintenance program and to detect quality issues in the manufactured product.
ACMON_ICON is an Industrial Research ICON project. We are looking for companies to join the User Group and work with us on the valorisation of the project.
Interested? Complete the form below and we will contact you as soon as possible.