Hardware-in-the-Loop-Based Real-Time Fault Injection Framework for Dynamic Behavior Analysis of Automotive Software Systems

Abboush, Mohammad; Bamal, Daniel; Knieke, Christoph; Rausch, Andreas

doi:10.3390/s22041360

Cited by 14 publications

(9 citation statements)

References 55 publications

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“…By doing so, the data samples of the normal system behavior, i.e., the driving scenario, can be collected as multivariate time-series data. To generate representative fault data, the real-time fault injection developed in the previous study [ 48 ] was used. Various types of single and simultaneous faults are programmatically injected into the CAN bus in real time without changing the original system models.…”

Section: Methodsmentioning

confidence: 99%

GRU-Based Denoising Autoencoder for Detection and Clustering of Unknown Single and Concurrent Faults during System Integration Testing of Automotive Software Systems

Abboush

Knieke

Rausch

2023

Sensors

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Recently, remarkable successes have been achieved in the quality assurance of automotive software systems (ASSs) through the utilization of real-time hardware-in-the-loop (HIL) simulation. Based on the HIL platform, safe, flexible and reliable realistic simulation during the system development process can be enabled. However, notwithstanding the test automation capability, large amounts of recordings data are generated as a result of HIL test executions. Expert knowledge-based approaches to analyze the generated recordings, with the aim of detecting and identifying the faults, are costly in terms of time, effort and difficulty. Therefore, in this study, a novel deep learning-based methodology is proposed so that the faults of automotive sensor signals can be efficiently and automatically detected and identified without human intervention. Concretely, a hybrid GRU-based denoising autoencoder (GRU-based DAE) model with the k-means algorithm is developed for the fault-detection and clustering problem in sequential data. By doing so, based on the real-time historical data, not only individual faults but also unknown simultaneous faults under noisy conditions can be accurately detected and clustered. The applicability and advantages of the proposed method for the HIL testing process are demonstrated by two automotive case studies. To be specific, a high-fidelity gasoline engine and vehicle dynamic system along with an entire vehicle model are considered to verify the performance of the proposed model. The superiority of the proposed architecture compared to other autoencoder variants is presented in the results in terms of reconstruction error under several noise levels. The validation results indicate that the proposed model can perform high detection and clustering accuracy of unknown faults compared to stand-alone techniques.

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Section: Methodsmentioning

confidence: 99%

GRU-Based Denoising Autoencoder for Detection and Clustering of Unknown Single and Concurrent Faults during System Integration Testing of Automotive Software Systems

Abboush

Knieke

Rausch

2023

Sensors

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“…For this purpose, the ModelDesk tool is used to specify both the internal and the external system specifications according to the user's requirements. The core specifications of the selected case study are listed in [47].…”

Section: A System Architecture Of the Case Studymentioning

confidence: 99%

“…Owing to the HIL simulator with the high-fidelity automotive simulation models, high-quality datasets can be collected considering the realtime constraints. Besides, employing the real-time FI framework proposed in a previous work[47], representative faulty and healthy data are generated in real-time. For this purpose, the target system is executed under faulty conditions, i.e., in the case of random sensor/actuator fault occurrence.…”

mentioning

confidence: 99%

Intelligent Identification of Simultaneous Faults of Automotive Software Systems Under Noisy and Imbalanced Data Using Ensemble LSTM and Random Forest

Abboush,

Knieke,

Rausch

2023

IEEE Access

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According to ISO 26262 standard, functional validation of the developed Automotive Software Systems (ASSs) is crucial to ensure the safety and reliability aspects. Hardware-in-the-loop (HIL) has been introduced as a reliable, safe and flexible test platform to enable the validation process in real-time. However, the traditional failure analysis process of HIL tests is time-consuming, extremely difficult and requires considerable effort. Therefore, an intelligent solution that can overcome the above challenges is required. Following a data-driven approach, the development of deep learning methods for fault detection and classification has gradually become a hot topic. However, despite the fruitful results, most of the previous studies were conducted for single faults without considering the simultaneous occurrence of multiple faults and ignoring the noisy conditions. In this study, based on multi-label ensemble long short term memory (LSTM) and random forest (RF) techniques, a novel method for simultaneous fault classification under noisy conditions is developed. To improve the robustness of the model against noise, a GRU-based denoising autoencoder (DAE) was implemented. Furthermore, to overcome the challenge of imbalanced data, a random undersampling algorithm was employed. By doing so, the single and simultaneous sensor faults occurring during HIL testing of ASSs can be efficiently and automatically detected and identified. To evaluate the capabilities and robustness of the proposed method, a high-fidelity gasoline engine with a dynamic vehicle system and driving environment was used as a case study. The analysis results demonstrate that the proposed model can achieve a high degree of accuracy under noise with an average detection accuracy of 99.43%. Moreover, compared to the individual methods, the proposed ensemble learning architecture with DAE provides more promising fault identification performance with improved accuracy and robustness. Specifically, the test results show that the proposed model is superior to other state-of-the-art models in identifying simultaneous faults with 92.65% F1-Score. INDEX TERMSAutomotive software systems, fault detection and diagnosis, deep learning, denoising autoencoder, LSTM, random forest, real-time simulation, fault injection; Hardware-in-the-Loop (HIL).

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“…The advantage of model-implemented fault injection is that engineers can apply the technique in the early phases of system development when the product is just a computer model. Finding errors in the early stages of development reduces the cost tremendously and saves time and effort for the software [ 23 ] and hardware prototypes [ 24 , 25 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Failure Identification Using Model-Implemented Fault Injection with Domain Knowledge-Guided Reinforcement Learning

Moradi

Acker

Denil

2023

Sensors

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The safety assessment of cyber-physical systems (CPSs) requires tremendous effort, as the complexity of cyber-physical systems is increasing. A well-known approach for the safety assessment of CPSs is fault injection (FI). The goal of fault injection is to find a catastrophic fault that can cause the system to fail by injecting faults into it. These catastrophic faults are less likely to occur, and finding them requires tremendous labor and cost. In this study, we propose a reinforcement learning (RL)-based method to automatically configure faults in the system under test and to find catastrophic faults in the early stage of system development at the model level. The proposed method provides a guideline to utilize high-level domain knowledge about a system model for constructing the reinforcement learning agent and fault injection setup. In this study, we used the system (safety) specification to shape the reward function in the reinforcement learning agent. The reinforcement learning agent dynamically interacted with the model under test to identify catastrophic faults. We compared the proposed method with random-based fault injection in two case studies using MATLAB/Simulink. Our proposed method outperformed random-based fault injection in terms of the severity and number of faults found.

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Hardware-in-the-Loop-Based Real-Time Fault Injection Framework for Dynamic Behavior Analysis of Automotive Software Systems

Cited by 14 publications

References 55 publications

GRU-Based Denoising Autoencoder for Detection and Clustering of Unknown Single and Concurrent Faults during System Integration Testing of Automotive Software Systems

GRU-Based Denoising Autoencoder for Detection and Clustering of Unknown Single and Concurrent Faults during System Integration Testing of Automotive Software Systems

Intelligent Identification of Simultaneous Faults of Automotive Software Systems Under Noisy and Imbalanced Data Using Ensemble LSTM and Random Forest

Failure Identification Using Model-Implemented Fault Injection with Domain Knowledge-Guided Reinforcement Learning

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