Apu Fmea Validation and Its Application to Fault Identification

FMEA (Failure Mode and Effects Analysis) is a systematic method to characterize product and process problems. As a standard document, an FMEA is produced during the design of a product or system. However, once a system is deployed, the corresponding FMEA is rarely validated and updated. This is mainly due to the lack of method to validate and update FMEA. This paper argues that historical maintenance and operational data could be used to help address this problem. Building on data mining and database techniques, the paper introduces a FMEA validation and update technique. The proposed technique derives statistics from real world historical operation and maintenance data and uses these statistics to update key FMEA parameters such as Failure Rate and Failure Mode Probability. The paper then shows how the validated FMEA can be used with data mining for fault detection and identification of root contributing component for a given failure mode or failure effect. The paper presents the developed methodology for FMEA validation and experimental results for fault identification. The results show that the updated FMEA can provide more reliable and accurate information that could benefit the decision-making process and improve maintenance efficiency. INTRODUCTION Failure Mode and Effects Analysis (FMEA) has been used for fault identification and prevention in maintenance industry as a systematic method, since it was originally developed by NASA to enhance the reliability of space program hardware [1]. Theoretically, FMEA provides a foundation for qualitative reliability, maintainability, safety and logistic analysis; it documents the relationships between failure cause and failure effects. In particular, FMEA contains useful information such as Severity Class (SC), Failure Rate (FR), and Failure Mode Probability (FMP) for determining the effects of each failure mode on system performance. Most of the research related to FMEAs focus on the generation process. For examples, techniques have been introduced to automatically generate FMEA documents [4], model the manufacturing processes [5] and identify the failure modes [6]. These enhancements result in improved FMEA quality and cost reduction. The need to further improve maintenance practices lead to recent investigations on the use of FMEA information to help develop intelligent fault diagnostic systems [2, 3]. This paper suggests that prior to use FMEA information, one must try to confirm its validity. Since FMEAs are produced at design time and then hardly validated after deployment of the corresponding system, there is a risk that the information provided is incomplete or no longer accurate. The likelihood for such inaccuracies is particularly high for complex systems such as aircraft engines that operate over a long period time. In such cases, using the initial FMEA information without adequate validation could result in the introduction of irrelevant maintenance actions. To avoid such issues, the initial FMEA information needs to be validated and then updated as required. In this paper, we propose to perform this task using real-world readily available maintenance and operational data. In particular, the paper investigates validation and updating of an FMEA for an APU (Auxiliary Power Unit engine). Our study relies on an “in-house” representation of an APU FMEA prepared by domain experts based on a full FMEA received from the OEM. The operation and maintenance data are from a commercial airline using a similar APU. The APU used in the aircraft is also from the same OEM, but it is not the same model as the one described in the FMEA available. Nevertheless, we consider the two APUs to be sufficiently similar to warrant this study. To constrain the study, we decided to focus on components related to the “Inability to start” failure effect. This paper complements a previous study [7] which presented an initial investigation with preliminary results.