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Project

Self-learning intelligent monitoring of cyber-physical system fleet

The PhD candidate will work on machine-learning and artificial-intelligence methods for self-learning and self-monitoring cyber-physical systems. A framework based on novelty detection will be established using dynamically updated clustering methodologies, capturing the transition of the system between multiple steady and time-varying operating modes. Moreover, self-organizing model methods will be investigated and further developed. ESR2 will establish a procedure in order to perform comparisons of identical or possibly similar cyber-physical systems (drivetrains, vehicles, machines) using similarity measures, in order to identify and monitor abnormal phenomena, such as wear, failures and noise. The developed methodologies and algorithms will first be tested and evaluated on simulated data and on two dedicated similar but not identical laboratory drivetrains, in order to quantify the rate of false alarms and missed detections. The final methodology evaluation will be performed on real industrial cases.

Date:16 Jun 2022 →  Today
Keywords:condition monitoring, fleet monitoring, reliability engineering, anomaly detection, machine learning, clustering, health management
Disciplines:Manufacturing safety and quality, Machine learning and decision making, Signal processing, Acoustics, noise and vibration engineering, Engineering instrumentation
Project type:PhD project