Abboush Mohammad, Bamal Daniel, Knieke Christoph, Rausch Andreas
Institute for Software and Systems Engineering, Technische Universität Clausthal, 38678 Clausthal-Zellerfeld, Germany.
Sensors (Basel). 2022 May 27;22(11):4066. doi: 10.3390/s22114066.
Hardware-in-the-Loop (HIL) has been recommended by ISO 26262 as an essential test bench for determining the safety and reliability characteristics of automotive software systems (ASSs). However, due to the complexity and the huge amount of data recorded by the HIL platform during the testing process, the conventional data analysis methods used for detecting and classifying faults based on the human expert are not realizable. Therefore, the development of effective means based on the historical data set is required to analyze the records of the testing process in an efficient manner. Even though data-driven fault diagnosis is superior to other approaches, selecting the appropriate technique from the wide range of Deep Learning (DL) techniques is challenging. Moreover, the training data containing the automotive faults are rare and considered highly confidential by the automotive industry. Using hybrid DL techniques, this study proposes a novel intelligent fault detection and classification (FDC) model to be utilized during the V-cycle development process, i.e., the system integration testing phase. To this end, an HIL-based real-time fault injection framework is used to generate faulty data without altering the original system model. In addition, a combination of the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is employed to build the model structure. In this study, eight types of sensor faults are considered to cover the most common potential faults in the signals of ASSs. As a case study, a gasoline engine system model is used to demonstrate the capabilities and advantages of the proposed method and to verify the performance of the model. The results prove that the proposed method shows better detection and classification performance compared to other standalone DL methods. Specifically, the overall detection accuracies of the proposed structure in terms of precision, recall and F1-score are 98.86%, 98.90% and 98.88%, respectively. For classification, the experimental results also demonstrate the superiority under unseen test data with an average accuracy of 98.8%.
硬件在环（HIL）已被ISO 26262推荐为确定汽车软件系统（ASS）安全和可靠性特征的重要测试平台。然而，由于HIL平台在测试过程中记录的数据复杂且数量庞大，基于人类专家进行故障检测和分类的传统数据分析方法无法实现。因此，需要开发基于历史数据集的有效方法，以便高效地分析测试过程的记录。尽管数据驱动的故障诊断优于其他方法，但从众多深度学习（DL）技术中选择合适的技术具有挑战性。此外，包含汽车故障的训练数据很少，且被汽车行业视为高度机密。本研究使用混合DL技术，提出了一种新颖的智能故障检测与分类（FDC）模型，用于V循环开发过程，即系统集成测试阶段。为此，使用基于HIL的实时故障注入框架来生成故障数据，而不改变原始系统模型。此外，采用卷积神经网络（CNN）和长短期记忆（LSTM）的组合来构建模型结构。在本研究中，考虑了八种类型的传感器故障，以涵盖ASS信号中最常见的潜在故障。作为案例研究，使用汽油发动机系统模型来证明所提方法的能力和优势，并验证模型的性能。结果证明，与其他独立的DL方法相比，所提方法具有更好的检测和分类性能。具体而言，所提结构在精度、召回率和F1分数方面的总体检测准确率分别为98.86%、98.90%和98.88%。对于分类，实验结果还证明了在未见测试数据下的优越性，平均准确率为98.8%。
