A System Engineering Approach Using FMEA and Bayesian Network for Risk Analysis—A Case Study

Sima Rastayesh*, Sajjad Bahrebar, Frede Blaabjerg, Dao Zhou, Huai Wang, John Dalsgaard Sørensen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

This paper uses a system engineering approach based on the Failure Mode and Effect Analysis (FMEA) methodology to do risk analysis of the power conditioner of a Proton Exchange Membrane Fuel Cell (PEMFC). Critical components with high risk, common cause failures and effects are identified for the power conditioner system as one of the crucial parts of the PEMFCs used for backup power applications in the telecommunication industry. The results of this paper indicate that the highest risk corresponds to three failure modes including high leakage current due to the substrate interface of the metal oxide semiconductor field effect transistor (MOSFET), current and electrolytic evaporation of capacitor, and thereby short circuit, loss of gate control, and increased leakage current due to gate oxide of the MOSFET. The MOSFETs, capacitors, chokes, and transformers are critical components of the power stage, which should be carefully considered in the development of the design production and implementation stage. Finally, Bayesian networks (BNs) are used to identify the most critical failure causes in the MOSFET and capacitor as they are classified from the FMEA as key items based on their Risk Priority Numbers (RPNs). As a result of BNs analyses, high temperature and overvoltage are distinguished as the most crucial failure causes. Consequently, it is recommended for designers to pay more attention to the design of MOSFETs’ failure due to high leakage current owing to substrate interface, which is caused by high temperature. The results are emphasizing design improvement in the material in order to be more resistant from high temperature.
Original languageEnglish
Article number77
JournalSustainability
Volume12
Issue number1
Number of pages18
ISSN2071-1050
DOIs
Publication statusPublished - 2020

Keywords

  • Bayesian network
  • Failure mode and effect analysis
  • Proton exchange membrane fuel cell
  • Power conditioner
  • Risk analysis

Cite this

Rastayesh, Sima ; Bahrebar, Sajjad ; Blaabjerg, Frede ; Zhou, Dao ; Wang, Huai ; Sørensen, John Dalsgaard. / A System Engineering Approach Using FMEA and Bayesian Network for Risk Analysis—A Case Study. In: Sustainability. 2020 ; Vol. 12, No. 1.
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abstract = "This paper uses a system engineering approach based on the Failure Mode and Effect Analysis (FMEA) methodology to do risk analysis of the power conditioner of a Proton Exchange Membrane Fuel Cell (PEMFC). Critical components with high risk, common cause failures and effects are identified for the power conditioner system as one of the crucial parts of the PEMFCs used for backup power applications in the telecommunication industry. The results of this paper indicate that the highest risk corresponds to three failure modes including high leakage current due to the substrate interface of the metal oxide semiconductor field effect transistor (MOSFET), current and electrolytic evaporation of capacitor, and thereby short circuit, loss of gate control, and increased leakage current due to gate oxide of the MOSFET. The MOSFETs, capacitors, chokes, and transformers are critical components of the power stage, which should be carefully considered in the development of the design production and implementation stage. Finally, Bayesian networks (BNs) are used to identify the most critical failure causes in the MOSFET and capacitor as they are classified from the FMEA as key items based on their Risk Priority Numbers (RPNs). As a result of BNs analyses, high temperature and overvoltage are distinguished as the most crucial failure causes. Consequently, it is recommended for designers to pay more attention to the design of MOSFETs’ failure due to high leakage current owing to substrate interface, which is caused by high temperature. The results are emphasizing design improvement in the material in order to be more resistant from high temperature.",
keywords = "Bayesian network, Failure mode and effect analysis, Proton exchange membrane fuel cell, Power conditioner, Risk analysis",
author = "Sima Rastayesh and Sajjad Bahrebar and Frede Blaabjerg and Dao Zhou and Huai Wang and S{\o}rensen, {John Dalsgaard}",
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A System Engineering Approach Using FMEA and Bayesian Network for Risk Analysis—A Case Study. / Rastayesh, Sima; Bahrebar, Sajjad; Blaabjerg, Frede; Zhou, Dao; Wang, Huai; Sørensen, John Dalsgaard.

In: Sustainability, Vol. 12, No. 1, 77, 2020.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - A System Engineering Approach Using FMEA and Bayesian Network for Risk Analysis—A Case Study

AU - Rastayesh, Sima

AU - Bahrebar, Sajjad

AU - Blaabjerg, Frede

AU - Zhou, Dao

AU - Wang, Huai

AU - Sørensen, John Dalsgaard

PY - 2020

Y1 - 2020

N2 - This paper uses a system engineering approach based on the Failure Mode and Effect Analysis (FMEA) methodology to do risk analysis of the power conditioner of a Proton Exchange Membrane Fuel Cell (PEMFC). Critical components with high risk, common cause failures and effects are identified for the power conditioner system as one of the crucial parts of the PEMFCs used for backup power applications in the telecommunication industry. The results of this paper indicate that the highest risk corresponds to three failure modes including high leakage current due to the substrate interface of the metal oxide semiconductor field effect transistor (MOSFET), current and electrolytic evaporation of capacitor, and thereby short circuit, loss of gate control, and increased leakage current due to gate oxide of the MOSFET. The MOSFETs, capacitors, chokes, and transformers are critical components of the power stage, which should be carefully considered in the development of the design production and implementation stage. Finally, Bayesian networks (BNs) are used to identify the most critical failure causes in the MOSFET and capacitor as they are classified from the FMEA as key items based on their Risk Priority Numbers (RPNs). As a result of BNs analyses, high temperature and overvoltage are distinguished as the most crucial failure causes. Consequently, it is recommended for designers to pay more attention to the design of MOSFETs’ failure due to high leakage current owing to substrate interface, which is caused by high temperature. The results are emphasizing design improvement in the material in order to be more resistant from high temperature.

AB - This paper uses a system engineering approach based on the Failure Mode and Effect Analysis (FMEA) methodology to do risk analysis of the power conditioner of a Proton Exchange Membrane Fuel Cell (PEMFC). Critical components with high risk, common cause failures and effects are identified for the power conditioner system as one of the crucial parts of the PEMFCs used for backup power applications in the telecommunication industry. The results of this paper indicate that the highest risk corresponds to three failure modes including high leakage current due to the substrate interface of the metal oxide semiconductor field effect transistor (MOSFET), current and electrolytic evaporation of capacitor, and thereby short circuit, loss of gate control, and increased leakage current due to gate oxide of the MOSFET. The MOSFETs, capacitors, chokes, and transformers are critical components of the power stage, which should be carefully considered in the development of the design production and implementation stage. Finally, Bayesian networks (BNs) are used to identify the most critical failure causes in the MOSFET and capacitor as they are classified from the FMEA as key items based on their Risk Priority Numbers (RPNs). As a result of BNs analyses, high temperature and overvoltage are distinguished as the most crucial failure causes. Consequently, it is recommended for designers to pay more attention to the design of MOSFETs’ failure due to high leakage current owing to substrate interface, which is caused by high temperature. The results are emphasizing design improvement in the material in order to be more resistant from high temperature.

KW - Bayesian network

KW - Failure mode and effect analysis

KW - Proton exchange membrane fuel cell

KW - Power conditioner

KW - Risk analysis

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DO - 10.3390/su12010077

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VL - 12

JO - Sustainability

JF - Sustainability

SN - 2071-1050

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ER -