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

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.

[1]  Sima Rastayesh,et al.  DYNAMIC AVAILABILITY ASSESSMENT ON TEHRAN RESEARCH REACTOR WATER COOLING SYSTEM , 2014 .

[2]  Frede Blaabjerg,et al.  Reliability assessment of power conditioner considering maintenance in a PEM fuel cell system , 2018, Microelectron. Reliab..

[3]  D. Depernet,et al.  Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system , 2017 .

[4]  Lisa M. Jackson,et al.  Failure Mode and Effect Analysis, and Fault Tree Analysis of Polymer Electrolyte Membrane Fuel Cells , 2016 .

[5]  Huai Wang,et al.  Mission Profile Based System-Level Reliability Analysis of DC/DC Converters for a Backup Power Application , 2018, IEEE Transactions on Power Electronics.

[6]  O. Capatina,et al.  Effects of temperature and mechanical strain on Ni-Fe alloy CRYOPHY for magnetic shields , 2019, Journal of Magnetism and Magnetic Materials.

[7]  Seung J. Rhee,et al.  Using cost based FMEA to enhance reliability and serviceability , 2003, Adv. Eng. Informatics.

[8]  Sellappan Narayanagounder,et al.  A New Approach for Prioritization of Failure Modes in Design FMEA using ANOVA , 2009 .

[9]  Mohammad Modarres,et al.  Reliability engineering and risk analysis : a practical guide , 2016 .

[10]  Sima Rastayesh,et al.  IMPORTANCE ANALYSIS OF A TYPICAL DIESEL GENERATOR USING DYNA MIC FAULT TREE , 2014 .

[11]  Akhtar Kalam,et al.  Experimental Investigation of H2 Generator and PEM Fuel Cell as a Remote Area Back-Up Power , 2012 .

[12]  D. Karpinsky,et al.  Magnetic and dipole moments in indium doped barium hexaferrites , 2018, Journal of Magnetism and Magnetic Materials.

[13]  Huai Wang,et al.  System-level reliability assessment of power stage in fuel cell application , 2016, 2016 IEEE Energy Conversion Congress and Exposition (ECCE).

[14]  A. Trukhanov,et al.  AC and DC-shielding properties for the Ni 80 Fe 20 /Cu film structures , 2017 .

[15]  I. M. Fita,et al.  Magnetic properties of La0.70Sr0.30MnO2.85 anion-deficient manganite under hydrostatic pressure , 2006 .

[16]  Jannie Sønderkær Bayesian Networks as a Decision Tool for O&M of Offshore Wind Turbines , 2015 .

[17]  Zerrouki Hamza,et al.  Mapping Fault Tree into Bayesian Network in safety analysis of process system , 2015, 2015 4th International Conference on Electrical Engineering (ICEE).

[18]  Michael Havbro Faber,et al.  Statistics and Probability Theory , 2012 .

[19]  Mohammad Modarres Risk Analysis in Engineering : Techniques, Tools, and Trends , 2016 .

[20]  Jun Shen,et al.  A review of PEM fuel cell durability: Degradation mechanisms and mitigation strategies , 2008 .

[21]  D. Barends,et al.  Risk analysis of analytical validations by probabilistic modification of FMEA. , 2012, Journal of pharmaceutical and biomedical analysis.

[22]  Rastayesh,et al.  Risk Assessment and Value of Action Analysis for Icing Conditions of Wind Turbines Close to Highways , 2019, Energies.

[23]  Frede Blaabjerg,et al.  Lifetime Estimation and Failure Risk Analysis in a Power Stage Used in Wind-Fuel Cell Hybrid Energy Systems , 2019, Electronics.

[24]  Kyungmee O. Kim,et al.  General model for the risk priority number in failure mode and effects analysis , 2018, Reliab. Eng. Syst. Saf..

[25]  Sima Rastayesh,et al.  TIME DEPENDENT RELIABILITY OF EMRGENCY DIESEL GENERATOR STATION , 2014 .

[26]  Jiujun Zhang,et al.  PEM fuel cell electrocatalysts and catalyst layers : fundamentals and applications , 2008 .

[27]  H.Arabian-Hoseynabadi,et al.  Failure Modes and Effects Analysis (FMEA) for Wind Turbines , 2011 .

[28]  Uffe Kjærulff,et al.  Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis , 2007, Information Science and Statistics.