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%.

A well-known challenge in the development of safety-critical systems in vehicles today is that reliability and safety assessment should be rigorously addressed and monitored. As a matter of fact, most safety problems caused by system failures can lead to serious hazards and loss of life. Notwithstanding the existence of several traditional analytical techniques used for evaluation based on specification documents, a complex design, with its multivariate dynamic behavior of automotive systems, requires an effective method for an experimental analysis of the system’s response under abnormal conditions. Simulation-based fault injection (FI) is a recently developed approach to simulate the system behavior in the presence of faults at an early stage of system development. However, in order to analyze the behavior of the system accurately, comprehensively and realistically, the real-time conditions, as well as the dynamic system model of the vehicle, should be considered. In this study, a real-time FI framework is proposed based on a hardware-in-the-loop (HiL) simulation platform and a real-time electronic control unit (ECU) prototype. The framework is modelled in the MATLAB/Simulink environment and implemented in the HiL simulation to enable the analysis process in real time during the V-cycle development process. With the objective of covering most of the potential faults, nine different types of sensor and actuator control signal faults are injected programmatically into the HiL system as single and multiple faults without changing the original system model. Besides, the model of the whole system, containing vehicle dynamics with the environment system model, is considered with complete and comprehensive behavioral characteristics. A complex gasoline engine system is used as a case study to demonstrate the capabilities and advantages of the proposed framework. Through the proposed framework, transient and permanent faults are injected in real time during the operation of the system. Finally, experimental results show the effects of single and simultaneous faults on the system performance under a faulty mode compared to the golden running mode.