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

Abboush, Mohammad; Knieke, Christoph; Rausch, Andreas

doi:10.3390/s23146606

Cited by 4 publications

(1 citation statement)

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“…Employing a representative real-time dataset generated from HIL, and based on hybrid DL techniques, an intelligent fault classification model has been proposed in [41,42] to be used during the development phases of ASSs, i.e., system integration testing. The basis of the developed model is the faulty data collected by programmatically injecting different sensor faults into the target system in real time without changing the model.…”

Section: Related Workmentioning

confidence: 99%

Representative Real-Time Dataset Generation Based on Automated Fault Injection and HIL Simulation for ML-Assisted Validation of Automotive Software Systems

Abboush,

Knieke,

Rausch

2024

Electronics

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Recently, a data-driven approach has been widely used at various stages of the system development lifecycle thanks to its ability to extract knowledge from historical data. However, despite its superiority over other conventional approaches, e.g., approaches that are model-based and signal-based, the availability of representative datasets poses a major challenge. Therefore, for various engineering applications, new solutions to generate representative faulty data that reflect the real world operating conditions should be explored. In this study, a novel approach based on a hardware-in-the-loop (HIL) simulation and automated real-time fault injection (FI) method is proposed to generate, analyse and collect data samples in the presence of single and concurrent faults. The generated dataset is employed for the development of machine learning (ML)-assisted test strategies during the system verification and validation phases of the V-cycle development model. The developed framework can generate not only time series data but also a textual data including fault logs in an automated manner. As a case study, a high-fidelity simulation model of a gasoline engine system with a dynamic entire vehicle model is utilised to demonstrate the capabilities and benefits of the proposed framework. The results reveal the applicability of the proposed framework in simulating and capturing the system behaviour in the presence of faults occurring within the system’s components. Furthermore, the effectiveness of the proposed framework in analysing system behaviour and acquiring data during the validation phase of real-time systems under realistic operating conditions has been demonstrated.

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Section: Related Workmentioning

confidence: 99%

Representative Real-Time Dataset Generation Based on Automated Fault Injection and HIL Simulation for ML-Assisted Validation of Automotive Software Systems

Abboush,

Knieke,

Rausch

2024

Electronics

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An autoencoder-based arithmetic optimization clustering algorithm to enhance principal component analysis to study the relations between industrial market stock indices in real estate

Yang,

Lee,

Lee

et al. 2025

Expert Systems with Applications

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

Cited by 4 publications

References 64 publications

Representative Real-Time Dataset Generation Based on Automated Fault Injection and HIL Simulation for ML-Assisted Validation of Automotive Software Systems

Representative Real-Time Dataset Generation Based on Automated Fault Injection and HIL Simulation for ML-Assisted Validation of Automotive Software Systems

An autoencoder-based arithmetic optimization clustering algorithm to enhance principal component analysis to study the relations between industrial market stock indices in real estate

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

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