Digital design

This paper presents a non-invasive, real-time fault detection and predictive maintenance framework for Connected Electric Truck powertrain. Leveraging the ultra-low-power ISM330DHCX sensor’s Machine Learning Core (MLC), the system performs on-sensor processing of accelerometer and gyroscope signals using Edge AI and TinyML techniques. Decision Trees (DTs) and an eight-DT based Random Forest (RF) model were employed to classify vibration patterns under varying operating conditions. Five experiments were conducted on electric motor test benches to simulate rotor imbalance and gather data in both normal and faulty states. Vibration signals were first classified into three broader fault categories and then mapped into 12 subclasses using a novel output mapping scheme. Guided by the Design, Implementation, Potential Failure and Functional Failure (DIPF) curve, this framework enables accurate detection of rotor unbalance faults. Experimental results showed that while single-DT models achieved high training accuracies (95−98.42%) but poor validation performance (<50%), the eight-DT based Random Forest with subclass splitting significantly improved generalization, achieving 98.74% validation accuracy. By minimizing power consumption and bandwidth requirements, this on-sensor Edge AI sensor network approach offers a scalable solution for predictive maintenance, with the potential to reduce downtime and maintenance costs by 30−50%. The findings highlight the promise of ultra-low-power intelligent sensing for enhancing the reliability and efficiency of Connected Electric Trucks.

This report presents a comprehensive overview of the activities carried out in Work Package 4 (WP4), specifically Task 4.3, of the RHODAS project, which focuses on the design and development of a digital twin framework for key components of electric powertrains.  The developed framework comprises three individual digital twins: the inverter digital twin, the e-motor digital twin, and the gearbox digital twin. The inverter digital twin models the electrothermal behavior of power electronics modules (SiC and GaN) to support performance monitoring and failure prediction. The e-motor digital twin captures the thermal behavior of the rotor and stator of the RHODAS e-motor, enabling accurate simulation under various driving conditions. The gearbox digital twin is based on a data-driven thermal equivalent circuit model that estimates the gearbox oil temperature using a range of input data.  All digital twin models are implemented in both MATLAB/Simulink and C++ to enable seamless integration into the RHODAS cloud platform, supporting both online and offline thermal monitoring and efficient computation.  The deliverable also includes extensive sensitivity analyses to investigate the behavior of the e-motor, power inverter, and gearbox under different operating modes. This outcome delivers a robust and scalable digital twin architecture that enhances monitoring, diagnostics, and predictive maintenance capabilities within the RHODAS electric vehicle platform. 

Modern Smart Grids are complex systems incorporating physical components like distributed energy resources and storage, along with cyber components for advanced control, networking, and monitoring. This study proposes an integrated methodology for risk prioritization and failure mode classification into low, moderate and high-risk faults using Grey Relational Analysis (GRA) together with Failure Mode and Effects Analysis (FMEA) and Deep Learning algorithms. The results demonstrate that, especially in complex systems like Smart Microgrids, the proposed method more accurately captures the coupling relationships between failure modes compared to the conventional FMEA method.