ADIOSS Automatic Diagnostic Of System Simulations

2026-03-13

AI Summary

This study addresses the challenge of efficiently localizing faulty modules in automotive system-level 0D simulations following model updates—a process that traditionally incurs high verification costs and prolonged cycles. To overcome this, the authors propose a novel diagnostic approach based on graph-structured modeling, which uniquely integrates Dynamic Mode Decomposition (DMD), linear programming, and autoencoders to embed system simulation behaviors into a graph representation. This framework enables automatic fault module identification with only a minimal number of simulation runs. The proposed method substantially reduces computational overhead, enhances fault localization efficiency, and seamlessly integrates into existing engineering validation workflows, offering both practical utility and strong scalability.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Automotive engineering makes extensive use of numerical simulation throughout the design process. The development of numerical models, their validation against experimental tests, and their updating during vehicle and engine projects constitute a core engineering activity. However, this activity must continuously evolve to reduce costs and lead times. In this context, we propose a method for detecting faulty modules within a system-level simulation workflow, represented as a graph of 0D models, following model updates. The proposed method requires a very limited number of system simulations and can therefore be easily integrated into existing engineering processes. It is designed as a toolbox based on well established and widely validated techniques, including Dynamic Mode Decomposition commonly used for 3D model reduction, linear programming, and autoencoders.

Problem

• system-level simulation
• fault detection
• model updating
• 0D models
• simulation workflow

Innovation

• Automatic Diagnosis
• System-level Simulation
• Dynamic Mode Decomposition
• Autoencoder
• 0D Modeling