# Automatic Failure Modes and Effects Analysis of an Electronic Fuel Injection Model
## 5 Conclusions  
We explained the automatic FMEA that integrates model-based design and a simulated fault injection tool to facilitate the failure modes and effects analysis (FMEA) process. Although the FMEA process is the key activity in the development of safety-critical systems, we could not utilize the merit of this process, due to various difficulties, such as the difficulty in organizing and proceeding of the FMEA committee, or the scarcity of experts--especially when the target system is a new product. To improve these limitations, automatic FMEA may contribute to overcoming these processes by integrating the model-based development, fault injection simulation, saboteur-based fault modeling, and rule-based severity classification.  
Automatic FMEA is designed to perform the fault injection simulation on a Simulink model, which has the most users in the domain of model-based development. It automatically generates FMEA reports by performing the fault injection simulation for each failure mode, and produces the failure effects. The severity information for the risk priority number is convincing, since we can use the traceability causality between any failure modes and failure effects, along with the rule-based failure diagnosis technique. The tool can investigate those events that may be overlooked by building a large number of test cases based on the HAZOP keywords and performing simulated fault injections accordingly. These processes are integrated into a single environment to help the safety engineers. In order to demonstrate the applicability of the automatic FMEA, we used an electronic fuel injection system (EFI) model with four sensors. The results of the automatic FMEA were compared to those of the legacy FMEA.  
We are in the process of integrating the fault tree analysis tool with our tool so that we can calculate the occurrence parameter for the given failure mode. Using the severity and the occurrence figure, we can calculate the criticality figure for every possible failure mode and rank the failure modes according to their criticality. This result can be beneficial to the certification authorities, developers, and consumers in the evaluation of the safety of the system. Moreover, we are trying to extend our tool to incorporate Simscape models, which support physical modeling methodology. This feature may expand the user group, since physical modeling using Simscape is easier than equation-based modeling with Simulink. In addition, in order to resolve the weak points in risk priority numbers (RPNs), such as the uncertainty issues and the linear scale in RPN figures, we plan to adopt a fuzzy inference engine in the evaluation of the severity and occurrence classification.  
The experiment, data analysis, and writing of the paper were conducted by [PERSON] ([PERSON]); the experiment and data analysis were completed by [PERSON] ([PERSON]); validation, methodology, and paper editing were handled by [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1F1A1061091).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.