用改进的U-Net模型去除发动机测试噪音，提升产线诊断准确率。
Improving Engine Sound Analysis in Hot-Test Environments via a RAB-U-Net (Residual Attention Block U-Net) Noise Removal Method
- 采用带残差注意力块的U-Net结构，有效分离发动机声与背景噪声
- 在真实产线环境下显著提升噪声检测准确率，优于传统人工听诊
- 适合汽车制造中需实时高精度发动机状态监测的场景
在生产线热测试中，发动机声音分析对保证产品质量和性能至关重要。然而，背景噪声常干扰声音分析，导致发动机诊断误差。传统上依赖经验丰富的技术人员听声判断，但存在较大主观误差。本文提出一种基于深度学习的噪声去除方法，采用增强型U-Net网络结构（RAB-U-Net），通过引入残差注意力块提升特征提取能力，实现对发动机声音记录中背景噪声的有效抑制。该智能降噪系统显著提升了发动机噪声检测的准确性，在真实产线环境中表现优异，为工业级实时应用提供了可靠解决方案。本研究为汽车工业利用深度学习技术提升发动机诊断质量提供了重要进展。
原文摘要 · Abstract (English)
During hot tests on a production line, engine-sound analysis is crucial to ensuring product quality and performance. However, background noise often interferes with accurate sound analysis, leading to potential errors in engine diagnostics. Traditionally, skilled technicians listen to engine sounds to assess engine health, but this is prone to significant inaccuracies. This study presents an innovative deep learning-based approach to address this issue by removing background noise from engine sound recordings using a U-Net neural network structure enhanced with Residual Attention Blocks (RAB-U-Net). Our intelligent noise removal system significantly improves the accuracy of engine noise detection, outperforming traditional techniques and providing a robust solution for real-time applications in production line environments. This study proposes a novel system for engine noise detection in production lines, marking a valuable advancement for the automotive industry in applying deep learning methods to improve the quality of engine diagnostics.
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