Towards Machine Learning Support for Embedded System Tests

Scharoba, Stefan; Basener, Kai-Uwe; Bielefeldt, Jens; Wiesbrock, Hans-Werner; Hübner, Michael

doi:10.1109/dsd53832.2021.00034

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Despite of the rapid development of real-time testing platforms, research on developing an intelligent system able to detect, isolate and identify faults during the development process is still in the early stages. For example, as an improvement of the embedded system testing process, Scharoba et al [39] have proposed a proximity-based anomaly detection system using ML techniques to automatically evaluate the test runs and identify the faulty behaviour. The method has been developed based on the historical test records so that deviations from the normal behaviour of the test object can be detected.…”

Section: A Fault Detection and Diagnosis In Automotive Domainmentioning

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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Section: A Fault Detection and Diagnosis In Automotive Domainmentioning

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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Enhancing Clustering Performance of Failed Test Cases during HIL Simulation: A Study on Deep Auto-Encoder Structures and Hyperparameter Tuning

Abboush,

Knieke,

Rausch

2024

Applied Sciences

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Over the last decade, hardware-in-the-loop (HIL) simulation has been established as a safe, efficient, reliable, and flexible method for performing real-time simulation. Furthermore, in the automotive sector, the HIL system has been recommended in the ISO 26262 standard as a powerful platform for performing realistic simulation during system integration testing. As a result of performing HIL black-box tests, the results of executing test cases (TCs) are reported as pass/fail without determining the nature and root causes of the underlying failures. The conventional analysis process of the failed TCs relies on expert knowledge. The higher the number of failed TCs, the higher the cost of manual analysis in terms of time and effort. In light of the shortcomings of existing methodologies, this study presents a novel intelligent framework capable of analyzing failed TCs without the need for expert knowledge or code access. To this end, a convolutional auto-encoder-based deep-learning approach is proposed to extract representative features from the textual description of the failed TCs. Furthermore, k-means-based clustering is used to categorize the extracted features according to their respective failure classes. To illustrate the effectiveness and validate the performance of the proposed method, a virtual test drive with real-time HIL simulation is presented as a case study. The proposed model exhibits superior clustering performance compared to other standalone k-means algorithms, as demonstrated by the David Bouldin Index (DBI) and accuracy values of 0.5184 and 94.33%, respectively. Furthermore, the proposed model shows a significant advantage in terms of feature extraction and clustering performance compared to the current state-of-the-art fault-analysis method. The proposed approach not only supports the validation process and improves the safety and reliability of the systems but also reduces the costs of manual analysis in terms of time and effort.

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Towards Machine Learning Support for Embedded System Tests

Cited by 2 publications

References 14 publications

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

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

Enhancing Clustering Performance of Failed Test Cases during HIL Simulation: A Study on Deep Auto-Encoder Structures and Hyperparameter Tuning

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