# Automatic Failure Modes and Effects Analysis of an Electronic Fuel Injection Model
## 4 Case study: Electronic Fuel Injection System
### Comparison of Automatic FMEA Tool with Legacy FMEA Tool  
In order to illustrate the impact of the presented tool, we built two FMEA models for the drift failure of the electronic fuel injection system's sensors (TPS, CPS, EGO, and MAP), using a legacy FMEA tool and the automatic FMEA tool [44]. First, the legacy FMEA is presented in Table 8a. Here, we should organize the FMEA committee with the related stakeholders, including the developer and the maintenance experts, to identify the failure modes and failure effects of the EFI system's sensors. The human expert may identify the drift error of the sensor, which may cause a problem with the air-fuel mixture in the fuel injector, resulting in engine hesitation. In addition, based on expert opinions, the severity, occurrence, and detection of the failure effects are presented. Next, the same drift failure mode in the EFI system's sensors (TPS, CPS, EGO, and MAP) is analyzed using the automatic FMEA, as shown in Table 8b. Unlike the legacy FMEA, the automatic FMEA specifically investigates the drift failure mode of the sensors thoroughly. Here, many tests with various types, frequencies, and failure durations can be executed in the fault injection simulation environment. The results of each simulation can be summarized to build the failure effects of the failure mode of the sensors. Using these data, we can not only record the entries of the FMEA templates, but also identify the threshold value, average value, and maximum value of the variable of interest. In Table 8b, we can see that the impact and severity of failure vary depending on the type of mission, error, and sensor. First, in the case of TPS, it is shown that the failure effect varies depending on the type of mission, even though it is under the same failure mode condition. Second, it can be seen that CPS has a different severity depending on the signal size, even under the same mission and signal error conditions. That is, when the signal error is greater than or equal to 150%, an engine hesitation phenomenon occurs. However, if the signal error falls below 70%, the engine stop phenomenon occurs. From these data, we can see that the CPS error characteristic is more sensitive to deficiency. We can see that using this table, various defect-failure causal analyses--such as identifying failure conditions that cause specific failure effects--are possible. We can summarize the advantages of the automatic FMEA as follows: First, using the fault injection campaign, we may find the detailed causality information, such as the threshold or the acceptable range of the key performance variables. In the case of the legacy FMEA, one may identify the known failure effects of the given failure modes. Thus, the automatic FMEA may provide the in-depth causality between the failure modes and the failure effects. Second, data-driven failure diagnosis can be utilized. In the target design model, we may setup the monitoring probes--as many as desired--to build the database of the sensor data. These data can be used to diagnose the failure event using deep learning or neural networks. Unlike the legacy FMEA, the proposed FMEA may open a new horizon for failure diagnosis, or a prognosis for future failure events. Third, we can set up various fault models in many different operating environments. While the legacy FMEA is used to identify the failure effects using the knowledge and experience of the participating member of the FMEA committee, the proposed FMEA can set up the various test cases, and the tool provides summarized results to the experts to find rare failure events that could be overlooked.