As of today, performances of antenna systems are evaluated analysing both measured and modelled data. If an anomaly occurs, an iterative process is made to determine the root cause. The goal of this project is to develop and validate AIDA (Artificial-Intelligence-Assisted Performance and Anomaly Detection and Diagnostic), a machine-learning-based software for the detection of RF anomalies and the identification of the associated root causes.
AIDA intends to contribute to the antenna experts analysis and to reduce the diagnosis time by implementing the following software capabilities:
- Early identification of antenna system anomalies, using an AI approach to classify patterns data, implementing generalization strategies in order to foster re-use of a trained model for different antennas under test (AUTs).
- Accurate anomaly quantification, thanks to a wide labelled database which is used for this purpose.
Verification of the anomaly classification and quantification output, comparing the measured patterns with the EM model data updated with the AIDA diagnostic output.