APU FMEA Validation and Its Application to Fault Identification

Létourneau

2013 IEEE Conference on Prognostics and Health Management (PHM)

et al. 2013

Self Cite

FMEA (Failure Mode and Effects Analysis), which was developed to enhance the reliability of complex systems, is a standard method to characterize and document product and process problems and a systematic method for fault identification/isolation in maintenance industry. Fault identification for a given failure effect or mode is a reactive process. Usually, a failure has occurred and it needs to identify which component is the root cause or to isolate the fault to a specific contributing component. Traditional method is to conduct TSM (Trouble Shooting Manuals)-based fault isolation,which is complicated, expensive, and time-consuming. To efficiently perform fault isolation, this paper proposed data mining-based framework for fault isolation by using FMEA information to rank data-driven models. In this paper, we present the proposed framework along with a case study for APU fau It identification.

Section: Overview Of the Fmea V Altdationmentioning

confidence: 99%

Section: Overview Of the Fmea V Altdationmentioning

confidence: 99%

See 1 more Smart Citation

Data mining based fault isolation with FMEA rank: A case study of APU fault identification

Létourneau

2013 IEEE Conference on Prognostics and Health Management (PHM)

et al. 2013

Self Cite

“…The [53] presented the APU failure mode and effect analysis. Through an investigation of maintenance records, the paper validated all the possible component faults that can lead to the "APU inability to start" failure effect.…”

Section: Objectivesmentioning

confidence: 99%

Aircraft APU Starter Health Monitoring and Failure Prognostics

Lou¹

Auxiliary power unit (APU) is a gas turbine engine on aircraft that provides energy for functions other than propulsion. Its starter is a crucial component that outputs assistant power to support the APU starting process. Starter performance degradation significantly impairs the whole APU life and raises risks for the aircraft flight. However, the current maintenance policy for the starter is still "run it till it breaks". An effective technique for the starter diagnostics and prognostics has not been reported yet. The aim of this thesis is to propose a framework for enabling the online detection and prediction of starter degradation. For this purpose, the thesis makes use of a dataset containing information about 52 APU "inability to start" failure events that were collected from actual aircraft operations over a period of ten years. Through the establishment of the relationship between the starter degradation and gas turbine engine starting performance, 13 of these 52 failures were identified as being caused by the starter degradation. Once this determination has been made, an online classifier based on moving autocorrelation is designed to detect the initial phase of degradation for each failure. Finally, a particle filtering based approach with an associated system state model is proposed to achieve the fault diagnostics and failure prognostics. The results demonstrate that a condition based maintenance program for the APU starter can be implemented to avoid unnecessary economic losses and to enhance aircraft operating safety. i First and foremost, I would like to express my deepest gratitude to my supervisor Dr. LIU Jie for his enthusiastic supervision and patient guidance. He is the best supervisor I had ever met, with encourage, enthusiasm, and immense knowledge. He supported and guided me throughout the entire master program. Without him, it would not have been possible to have this thesis. Also, I would like to sincerely thank our research collaborator Dr. YANG Chunsheng from National Research Council Canada for his insights and nice helps in this two-year project.

“…Failing to start the APU is extremely dangerous for an aircraft during a flight, when engines need a restart. Starter failure has been identified as the primary reason for the APU starting failures [11], [12]. A starter failure can also cause operational interruptions with escalated maintenance costs.…”

Section: List Of Tablesmentioning

confidence: 99%

Performance Estimation and Fault Diagnostics for the Starter of Auxiliary Power Unit

Zhang¹