基于GRU的Denoising自动编码器用于在汽车软件系统的系统集成测试过程中检测和聚类未知的单一和并发故障
Mohammad Abboush1, Christoph Knieke1, Andreas Rausch1
1Institute for Software and Systems Engineering, Technische Universität Clausthal, 38678 Clausthal-Zellerfeld, Germany.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种深度学习方法,使用基于GRU的无噪自动编码器和k-means用于自动汽车传感器故障检测. 它有效地识别了杂的硬件在循环模拟数据中的单独和同时故障.
科学领域:
- 汽车工程 汽车工程
- 软件质量保证 软件质量保证
- 人工智能的人工智能
背景情况:
- 硬件在循环 (HIL) 模拟可以提高汽车软件系统 (ASS) 的质量保证.
- HIL测试产生了大量的数据集,使手动故障分析耗时且困难.
- 现有的基于专家知识的故障检测方法资源密集.
研究的目的:
- 开发一种高效的,自动化的深度学习方法来检测和识别汽车传感器信号故障.
- 为了应对分析大型HIL测试数据集的挑战,而无需人类干预.
- 在噪音条件下准确检测和集群单个和未知的同时故障.
主要方法:
- 开发了一种基于GRU的混合式无声自编码器 (基于GRU的DAE) 模型.
- 基于GRU的DAE与故障检测和集群的k-means算法集成.
- 该方法在HIL模拟中使用高保真性汽油发动机,车辆动态和整个车辆模型进行了验证.
主要成果:
- 拟议的基于GRU的DAE与k-means精确检测和集群单个和未知的同时故障在顺序数据中.
- 该模型与其他自动编码器变体相比表现出更高的性能,在各种噪声水平下显示较低的重建误差.
- 汽车案例研究的验证证实了高检测和聚类准确性,优于独立技术.
结论:
- 新的深度学习方法为HIL测试中的汽车传感器故障检测提供了一个高效和自动化的解决方案.
- 基于GRU的DAE与k-means相结合,可以有效处理噪音数据,并识别复杂的故障场景.
- 这种方法通过自动化数据分析,显著改善了汽车软件系统的质量保证过程.
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