Summary of Ai-driven Transformer Model For Fault Prediction in Non-linear Dynamic Automotive System, by Priyanka Kumar
AI-driven Transformer Model for Fault Prediction in Non-Linear Dynamic Automotive System
by Priyanka Kumar
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an AI-based fault classification and prediction model for diesel engines that can be applied to any highly non-linear dynamic automotive system. The model uses the Transformer architecture with 27 input dimensions, 64 hidden dimensions with two layers, and nine heads to classify faults in diesel engines according to the Worldwide Harmonized Light Vehicle Test Procedure (WLTP) driving cycle. The model was trained on a high-performance compute cluster with NVIDIA V100 GPUs, 40-core CPUs, and 384GB RAM, achieving an accuracy of 70.01% on a held-out test set. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops an artificial intelligence-based system to detect and predict faults in diesel engines. This technology can be used in any car engine that produces complex data. The researchers created a special computer model called Transformer that helps identify problems with the engine. They tested this model using real-world driving patterns and it worked well, correctly identifying faults 70% of the time. |
Keywords
» Artificial intelligence » Classification » Transformer
