# Automatic Failure Modes and Effects Analysis of an Electronic Fuel Injection Model
## 1 Introduction  
In safety-critical fields such as automobiles, aerospace, and railways, it is important to comply with the international standards or guidelines for functional safety to prevent harm caused by potential system failures. Industries employ their own standards, such as SAE ARP 4754A/4761A and RTCA DO-178C/DO-254 for aerospace, or ISO26262 for the automotive industry [1, 2, 3, 4, 5, 6]. The purpose of these guidelines is to identify hazards and decrease the occurrence of accidents to socially acceptable levels. HAZOP (hazard and operability analysis), FMEA (failure modes and effects analysis), and FTA (fault tree analysis) activities are key to achieving these goals [7, 8, 9, 10]. Among them, FMEA investigates the causality of faults and failures to improve the safety and reduce the likelihood of recall. However, traditional FMEA activities tend to be considered difficult and time-consuming tasks, so the effectiveness of the results falls short of expectations [11, 12]. To facilitate FMEA tasks, various types of tools are used to increase the quality and the effectiveness of the FMEA reports. Traditional FMEA analysis tools use spreadsheet-based FMEA templates [13, 14, 15, 16]. The spreadsheet-based FMEA tool provides users with the FMEA templates according to the regulations or specifications agreed upon by the stakeholders in their field. Then, the users identify each potential failure mode and assess their local and global failure impacts on safety, including the severity of the occurrence. Meanwhile, the complexity of software in electric equipment has increased rapidly, making it very difficult to analyze system fault-failure causalities and their impacts [17]. To address thesechallenges, leading organizations are pursuing model-based SW development, including UML, SysML, and Simulink [18; 19; 20; 21; 22; 23; 24]. Model-based FMEA techniques use design models to support a visible and automated analysis environment. In model-based design (MBD), the design model describes the function of the system and its behavior by utilizing graphical objects of various types [25; 26; 27; 28; 29; 30; 31; 32; 33]. The MBD and FMEA processes can be integrated to simulate the failure effects at the various levels, from the local level up to the vehicle or system level [21]. This approach can evaluate the fault-failure causality for a limited number of cases [34; 35]. In addition, FMEA users expect support for the risk priority number (RPN) to respond to unacceptable failure effects. This paper explains an automatic FMEA tool that integrates the MBD, FMEA, and simulated fault injection techniques in a single environment. The automatic FMEA investigates the fault-failure causality of the system using simulated fault injection to find the potential failure modes and their effects at various levels, from local to global. In addition, it incorporates an AI-based failure effect classification module to identify the severity and occurrence rate so that the risk priority matrix can be automatically presented. In addition, the automatic FMEA is based on MATLAB/Simulink to support large numbers of users in safety-critical fields around the world. The automatic FMEA tool has the following advantages compared to the legacy FMEA tools as well as the model-based FMEA tools: In the legacy FMEA, the users are responsible for the creation of the FMEA report, but in the automatic FMEA, the tool produces it and the user only needs to review the contents of the FMEA results [13; 14; 15; 16]. The legacy FMEA tools provide the FMEA templates, and users enter the entries of the FMEA table using design documents and safety guidelines. The automatic FMEA tool generates FMEA results by analyzing the failure effects due to failure mode, using the integrated FMEA environment with model simulation and simulated fault injection.