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AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a
Feryel Zoghlami1, Marika Kaden2, Thomas Villmann2
1Automation, Maintenance and Factory Integration, Infineon Technologies Dresden GmbH & Co. KG, 01099 Dresden, Germany.
Sensors (Basel, Switzerland)
|July 2, 2021
Summary
This study introduces a smart, interpretable artificial intelligence (AI) system for sensor fusion. It analyzes sensor data to improve classification accuracy and evaluate individual sensor contributions, reducing costs and enhancing safety in industrial applications.
Area of Science:
- Artificial Intelligence
- Sensor Fusion
- Machine Learning
Background:
- Sensor fusion is crucial in semiconductor, automotive, and medical industries.
- Challenges exist in sensor selection and fusion architecture design.
- Deep neural networks offer potential but have drawbacks like storage and interpretability issues.
Purpose of the Study:
- To develop a smart and interpretable bi-functional artificial intelligence (AI) system for sensor fusion.
- To enable discrimination of combined sensor data into predefined classes.
- To evaluate individual sensor contributions and robustness within the fusion system.
Main Methods:
- Training a prototype-based neural network for automatic sensor weighting.
- Implementing a reject option for classification certainty measurement.
- Validating the approach using diverse industrial sensor fusion applications.
Main Results:
- The developed AI system effectively discriminates sensor data.
- It automatically weights sensor influence for classification decisions.
- Classification certainty can be measured, enhancing system reliability.
Conclusions:
- The proposed prototype-based AI system offers an interpretable and efficient solution for sensor fusion challenges.
- It reduces application costs and engineering effort through pre-analysis and intelligent data handling.
- The system enhances safety by minimizing misclassification and evaluating sensor robustness.
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