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Reliability assessment and improvement of air circuit breaker (ACB) mechanism by identifying and eliminating the root causes

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Abstract

Air circuit breakers (ACBs) are widely used as electro-mechanical devices to protect an electrical circuit from damage caused by overload or short circuit. Its basic function is to isolate a fault condition by interrupting current flow and if it fails to function, then it may cause a major accident. The major functions in ACB relies on mechanical drives and linkages, hence assessing the reliability of these drives and links is of key importance. This paper demonstrates the process of assessment as well as improvement of Reliability of ACB by exploring and eliminating the root causes of failures based on various relevant tools. Reliability assessment of existing and improved ACB mechanism was carried out by using Lifetime distribution. Root causes were analyzed using FTA, Ishikawa diagram, CE Matrix, and Pareto chart. After analyzing various causes, the root cause of failures in ACB was malfunctioning of unidirectional bearing. Elimination of root cause increased the reliability from 17.61 to 87.82 % for 20,000 operations. The drastic increase in reliability of ACB after eliminating the root cause of failure served its purpose effectively and helped in securing a strong position in market. This research deals with reliability improvement of ACB that can cut-off/supply electricity from the substation. Improving reliability of such product reduces these possible risks and indirectly helps the society particularly in critical areas like Hospitals, Airports, Process industries, etc.

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References

  • Ahmadzadeh F, Lundberg J (2014) Remaining useful life estimation: review. Int J Syst Assur Eng Manag 5(4):461–474

    Article  Google Scholar 

  • Ammerman M (1998) The root cause analysis handbook. Productivity Press, New York

    Google Scholar 

  • Anderson B, Fagerhaug T (2006) Root cause analysis: simplified tools and techniques. Quality Press, Wisconsin

    Google Scholar 

  • Arcidiacono G (2003) Development of a FTA versus parts count method model: comparative FTA. Qual Reliab Eng Int 19(5):411–424

    Article  Google Scholar 

  • Artana KB, Ishida K (2002) Spreadsheet modeling of optimal maintenance schedule for components in wear-out phase. Reliab Eng Syst Saf 77:81–91

    Article  Google Scholar 

  • Asgharzadeh A, Valiollahi R, Raqab MZ (2011) Stress-strength reliability of Weibull distribution based on progressively censored samples. SORT 35(2):103–124

    MathSciNet  MATH  Google Scholar 

  • Bartlett LM, Hurdle EE, Kelly EM (2009) Integrated system fault diagnostics utilising digraph and fault tree-based approaches. Reliab Eng Syst Saf 94:1107–1115

    Article  Google Scholar 

  • Basak P, Basak I, Balakrishnan N (2009) Estimation for the three-parameter lognormal distribution based on progressively censored data. Comput Stat Data Anal 53(3580):3592

    MathSciNet  MATH  Google Scholar 

  • Bebbington M, Laia CD, Zitikis R (2007) A flexible Weibull extension. Reliab Eng Syst Saf 92:719–726

    Article  Google Scholar 

  • Bernstein N (1985) Reliability analysis techniques for mechanical systems. Qual Reliab Eng Int 1(4):235–248

    Article  Google Scholar 

  • Bertsche B (2007) Reliability in automotive and mechanical engineering. Springer, Berlin, pp 35–60 160–127

    Google Scholar 

  • Bhattacharya A, Dan PK (2014) Recent trend in condition monitoring for equipment fault diagnosis. Int J Syst Assur Eng Manag 5(3):230–244

    Article  Google Scholar 

  • Bichon BJ, McFarland JM, Mahadevan S (2011) Efficient surrogate models for reliability analysis of systems with multiple failure modes. Reliab Eng Syst Saf 96:1386–1395

    Article  Google Scholar 

  • Billinton R, Karki R (2010) Incorporating wind power in generating system reliability evaluation. Int J Syst Assur Eng Manag 1(2):120–128

    Article  Google Scholar 

  • Boudreau J, Poirier S (2014) End-of-life assessment of electric power equipment allowing for non-constant hazard rate—application to circuit breakers. Electr Power Energy Syst 62:556–561

    Article  Google Scholar 

  • Bourezg A, Meglouli H (2014) Reliability analysis of breaker arrangements in distribution substations. Int J Syst Assur Eng Manag 1–7. doi:10.1007/s13198-014-0282-x

  • Bourouni K (2013) Availability assessment of a reverse osmosis plant: comparison between reliability block diagram and fault tree analysis methods. Desalination 313:66–76

    Article  Google Scholar 

  • Chen C (2006) Tests of fit for the three-parameter lognormal distribution. Comput Stat Data Anal 50:1418–1440

    Article  MathSciNet  MATH  Google Scholar 

  • Cheng S, Lin B, Hsu B, Shu M (2009) Fault-tree analysis for liquefied natural gas terminal emergency shutdown system. Expert Syst Appl 36:11918–11924

    Article  Google Scholar 

  • CIGRE SC13 (1991) High voltage circuit breaker reliability data for use in system reliability studies. CIGRE Publication, Paris

    Google Scholar 

  • Cohen AC (1951) Estimating parameters of logarithmic-normal distributions by maximum likelihood. J Am Stat Assoc 46(206):212

    MathSciNet  MATH  Google Scholar 

  • Contini S, Matuzas V (2011) Analysis of large fault trees based on functional decomposition. Reliab Eng Syst Saf 96:383–390

    Article  Google Scholar 

  • Crow EL, Shimizu K (eds) (1988) Lognormal distributions: theory and applications. Marcel Dekker, New York

    MATH  Google Scholar 

  • De S, Das A, Sureka A (2010) Product failure root cause analysis during warranty analysis for integrated product design and quality improvement for early results in downturn economy. Int J Prod Dev 12(3/4):235–253

    Article  Google Scholar 

  • Dutuita Y, Rauzy A (2005) Approximate estimation of system reliability via fault trees. Reliab Eng Syst Saf 87:163–172

    Article  Google Scholar 

  • Ericson CA (1999) Fault tree analysis—a history. In: 17th International System Safety Conference

  • Fajdiga M, Jurejevčič T, Kernc J (1996) Reliability prediction in early phases of product design. J Eng Des 7(2):107–128

    Article  Google Scholar 

  • Ferjencik M (2010) Root cause analysis of an old accident in an explosives production plant. Saf Sci 48:1530–1544

    Article  Google Scholar 

  • Fisher FE, Fisher JR (2000) Probability applications in mechanical design. Marcel Dekker, New York, pp 200–205

    Book  MATH  Google Scholar 

  • Grattan D, Nicholson S (2010) Integrating switchgear breakers and contactors into a safety instrumented function. J Loss Prev Process Ind 23:784–795

    Article  Google Scholar 

  • Han HS, Lee KH (2015) Root cause analysis of the fracture of a sonar window caused by hydrostatic, hydrodynamic, and transient forces around a ship. Eng Fail Anal 48:218–235

    Article  Google Scholar 

  • Harter HL, Moore AH (1966) Local-maximum-likelihood estimation of the parameters of the three-parameter lognormal populations from complete and censored samples. J Am Stat Assoc 61:842–851 Corrections: 61 (1966) 1247; 62 (1967) 1519 1520; 63 (1968) 1549

    Article  MathSciNet  Google Scholar 

  • Hill BM (1963) The three-parameter lognormal distribution and Bayesian analysis of a point-source epidemic. J Am Stat Assoc 58(72):84

    MathSciNet  MATH  Google Scholar 

  • Huang H, Tonga X, Zuo MJ (2004) Posbist fault tree analysis of coherent systems. Reliab Eng Syst Saf 84:141–148

    Article  Google Scholar 

  • Ibáñez-Llano C, Rauzy A, Meléndez E, Nieto F (2010) A reduction approach to improve the quantification of linked fault trees through binary decision diagrams. Reliab Eng Syst Saf 95:1314–1323

    Article  Google Scholar 

  • Jaise J, AjayKumar NB, SivaShanmugam N, Sankaranarayanasamy K, Ramesh T (2013) Power system: a reliability assessment using FTA. Int J Syst Assur Eng Manag 4(1):78–85

    Article  Google Scholar 

  • Jayswala A, Li X, Zanwara A, Loua HH, Huang Y (2011) A sustainability root cause analysis methodology and its application. Comput Chem Eng 35:2786–2798

    Article  Google Scholar 

  • Jiang R, Murthy DNP (2011) A study of Weibull shape parameter: properties and significance. Reliab Eng Syst Saf 96:1619–1626

    Article  Google Scholar 

  • Jianing WU, Shaoze YAN (2011) Reliability analysis of the solar array based on fault tree analysis. J Phys IOP Publ Conf Ser 305:012006

    Article  Google Scholar 

  • Johnson NL, Kotz S, Balakrishnan N (1994) Continuous univariate distributions, vol 1, 2nd edn. Wiley, New York

    MATH  Google Scholar 

  • Kelleher K (1995) Cause-and-effect diagrams: plain and simple. Joiner Associates Incorporated, Madison

    Google Scholar 

  • Khorshidi HA, Gunawan I, Ibrahim MY (2015) Data-driven system reliability and failure behavior modelling using FMECA. Industr Inform IEEE Trans. doi:10.1109/TII.2015.243122

    Google Scholar 

  • Kim JS, Yum B (2008) Selection between Weibull and lognormal distributions: a comparative simulation study. Comput Stat Data Anal 53:477–485

    Article  MathSciNet  MATH  Google Scholar 

  • Klein JP, Moeschberger ML (2010) Survival analysis: techniques for censored and truncated data. Springer, New York

    MATH  Google Scholar 

  • Kuma S, Sahin B (2015) A root cause analysis for Arctic Marine accidents from 1993 to 2011. Saf Sci 74:206–220

    Article  Google Scholar 

  • Kumar A, Lata S (2011) Reliability analysis of piston manufacturing system. J Reliab Stat Stud 4(2):43–55

    MathSciNet  MATH  Google Scholar 

  • Kumar S, Chattopadhyay G, Kumar U (2007) Reliability improvement through alternative designs—a case study. Reliab Eng Syst Saf 92:983–991

    Article  Google Scholar 

  • Latino RJ, Latino KC (2006) Root cause analysis: improving performance for bottom-line results. CRC Press, Boca Raton

    Book  Google Scholar 

  • Lehtinen TOA, Mäntylä MV, Vanhanen J (2011) Development and evaluation of a lightweight root cause analysis method (ARCA method)—field studies at four software companies. Inf Softw Technol 53:1045–1061

    Article  Google Scholar 

  • Lindquist TM, Bertling L, Eriksson R (2008) Circuit breaker failure data and reliability modelling. IET Gener Transm Distrib 2(6):813–820

    Article  Google Scholar 

  • Loll V (1985) Mechanical reliability, how do we advance? Qual Reliab Eng Int 1(4):249–255

    Article  Google Scholar 

  • Lougha KG, Stonea R, Tumer IY (2009) The risk in early design method. J Eng Des 20(2):155–173

    Article  Google Scholar 

  • Mahmood YA, Ahmadi A, Verma AK, Srividya A, Kumar U (2013) Fuzzy fault tree analysis: a review of concept and application. Int J Syst Assur Eng Manag 4(1):19–32

    Article  Google Scholar 

  • Marković D, Jukic D (2010) On nonlinear weighted total least squares parameter estimation problem for the three-parameter Weibull density. Appl Math Model 34:1839–1848

    Article  MathSciNet  MATH  Google Scholar 

  • Mazza G, Michaca R (1985) The first international enquiry on circuit breaker failures and defects in service. ELECTRA 79:21–91

    Google Scholar 

  • Moeini A, Jenab K, Mohammadi M, Foumani M (2013) Fitting the three-parameter Weibull distribution with cross entropy. Appl Math Model 37:6354–6363

    Article  MathSciNet  Google Scholar 

  • Mohammadian SH, Ait-Kadi D (2010) Design stage confirmation of life time improvement for newly modified products through accelerated life testing. Reliab Eng Syst Saf 95:897–905

    Article  Google Scholar 

  • Morello MG, Cavalca KL, Silveira ZC (2008) Development and reduction of a fault tree for gearboxes of heavy commercial vehicles based on identification of critical components. Qual Reliab Eng Int 24(2):183–198

    Article  Google Scholar 

  • Munro AH, Wixley RAJ (1970) Estimators based on order statistics of small samples from a three-parameter lognormal distribution. J Am Stat Assoc 65(212):225

    Google Scholar 

  • Nagatsuka H, Kamakurab T, Balakrishnan N (2013) A consistent method of estimation for the three-parameter Weibull distribution. Comput Stat Data Anal 58:210–226

    Article  MathSciNet  Google Scholar 

  • Nichols K (1992) Designing for quality and reliability. J Eng Des 3(2):139–148

    Article  MathSciNet  Google Scholar 

  • Pal A, Franciosa P, Ceglarek D (2014) Root cause analysis of product service failures in design a closed-loop lifecycle modelling approach. Proc CIRP 21:165–170

    Article  Google Scholar 

  • Pang W, Leung P, Huang W, Liu W (2005) On interval estimation of the coefficient of variation for the three-parameter Weibull, lognormal and gamma distribution: a simulation-based approach. Eur J Oper Res 164:367–377

    Article  MathSciNet  MATH  Google Scholar 

  • Panja SC, Ray PK (2007) Reliability analysis of a ‘point-and-point machine’ of the Indian railway signaling system. Qual Reliab Eng Int 23(7):833–848

    Article  Google Scholar 

  • Peng Z, Lu Y, Miller A, Johnson C, Zhao T (2014) Risk assessment of railway transportation systems using timed fault trees. Qual Reliab Eng Int. doi:10.1002/qre.1738

    Google Scholar 

  • Reid I, Smyth-Renshaw J (2012) Exploring the fundamentals of root cause analysis: Are we asking the right questions in defining the problem? Qual Reliab Eng Int 28(5):535–545 Special Issue: ENBIS 11

    Article  Google Scholar 

  • Remenyte-Prescott R, Andrews JD, Chung PWH (2010) An efficient phased mission reliability analysis for autonomous vehicles. Reliab Eng Syst Saf 95:226–235

    Article  Google Scholar 

  • Robitaille D (2004) Root cause analysis: basic tools and techniques. Paton Press LLC, Chico

    Google Scholar 

  • Rooney JJ, Heuvel LNV (2004) Root cause analysis for beginners. Qual Prog 37(7):45–53

    Google Scholar 

  • Rukhin AL (1984) Improved estimation in lognormal models. Technical Report No. 84–38, Department of Statistics, Purdue University, West Lafayette, Indiana

  • Shrouti C, Franciosa P, Ceglarek D (2013) Root cause analysis of product service failure using computer experimentation technique. Proc CIRP 11:44–49

    Article  Google Scholar 

  • Sinha RS, Mukhopadhyay AK (2015) Reliability centered maintenance of cone crusher: a case study. Int J Syst Assur Eng Manag 6(1):32–35

    Article  Google Scholar 

  • Smitha J, Clarkson PJ (2005) Design concept modelling to improve reliability. J Eng Des 16(5):473–492

    Article  Google Scholar 

  • Stapleberg RF (2009a) Handbook of reliability, availability, maintainability and safety in engineering design. Springer, Berlin, pp 45–55

    Google Scholar 

  • Stapleberg RF (2009b) Handbook of reliability, availability, maintainability and safety in engineering design. Springer, Berlin, p 636

    Google Scholar 

  • Surhone L, Timpledon M, Marseken S (2010) Pareto analysis: statistics, decision making, Pareto principle, fault tree analysis, failure mode and effects analysis, Pareto distribution. Wikipedia Betascript Publishing

  • Sutton IS (2008) Use root cause analysis to understand and improve process safety culture. Process Saf Prog 27(4):274–279

    Article  MathSciNet  Google Scholar 

  • Tang J (2001) Mechanical system reliability analysis using a combination of graph theory and Boolean function. Reliab Eng Syst Saf 72:21–30

    Article  Google Scholar 

  • Venu VV, Verma AK (2010) Reliability of electric power systems: challenges in the deregulated environment—a research perspective. Int J Syst Assur Eng Manag 1:24–31

    Article  Google Scholar 

  • Vera JF, Díaz-García JA (2008) A global simulated annealing heuristic for the three-parameter lognormal maximum likelihood estimation. Comput Stat Data Anal 52:5055–5065

    Article  MathSciNet  MATH  Google Scholar 

  • Vesely WE, Goldberg FF, Roberts NH, Haasi DF (1981) The fault tree handbook. US Nuclear Regulatory Commission, NUREG 0492, Commission Headquarters, Rockville, pp VII_1–VII_8

    Google Scholar 

  • Wang Y, Wu X, Zhou Z, Li Y (2003a) Investigation of reliability and lifetime distribution of the gas sensors based on C2H5OH. Solid State Electron 47:107–110

    Article  Google Scholar 

  • Wang Y, Wu X, Zhou Z, Li Y (2003b) The reliability and lifetime distribution of SnO2- and CdSnO3-gas sensors for butane. Sens Actuators, B 92:186–190

    Article  Google Scholar 

  • Wee YY, Cheah WP, Tan SC, Wee K (2015) A method for root cause analysis with a Bayesian belief network and fuzzy cognitive map. Expert Syst Appl 42:468–487

    Article  Google Scholar 

  • Wilson P, Dell L, Anderson G (1993) Root cause analysis: a tool for total quality management. Quality Press, Wisconsin

    Google Scholar 

  • Yadav OP, Singh N, Goel PS (2006) Reliability demonstration test planning: a three dimensional consideration. Reliab Eng Syst Saf 91:882–893

    Article  Google Scholar 

  • Yadav OP, Choudhary N, Bilen C (2008) Complex system reliability estimation methodology in the absence of failure data. Qual Reliab Eng Int 24(7):745–764

    Article  Google Scholar 

  • Zhang T, Dwight R (2013) Choosing an optimal model for failure data analysis by graphical approach. Reliab Eng Syst Saf 115:111–123

    Article  Google Scholar 

  • Zhang X, Gockenbach E (2011) Age-dependent maintenance strategies of medium-voltage circuit-breakers and transformers. Electr Power Syst Res 81:1709–1714

    Article  Google Scholar 

  • Zhang X, Gockenbacha E, Liub Z, Chenc H, Yangc L (2013) Reliability estimation of high voltage SF6 circuit breakers by statistical analysis on the basis of the field data. Electr Power Syst Res 103:105–113

    Article  Google Scholar 

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Acknowledgments

This research paper was made possible through the great resources and help provided by two great organizations: Sardar Patel College of Engineering and Larsen and Toubro Switchgear Ltd. We sincerely wish to acknowledge a deep sense of gratitude for the valuable guidance, suggestions and generous help extended by Satyaprakash Sharma. In addition, we would like to thank Pushkar Phadke for his great help and co-operation. We are also very thankful to Abdul Aziz Gazdar who actually read our paper at micro level and gave us many valuable suggestions. We sincerely thank all the authors that have made available sufficient literature in this domain that helped us and kept us in the right direction. The product of this research paper would not be possible without all of them.

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  1. Department of Mechanical Engineering, Sardar Patel College of Engineering, Munshi Nagar, Andheri –West, Mumbai, 400058, India

    Santosh B. Rane & Yahya A. M. Narvel

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  1. Santosh B. Rane
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Correspondence to Yahya A. M. Narvel.

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Rane, S.B., Narvel, Y.A.M. Reliability assessment and improvement of air circuit breaker (ACB) mechanism by identifying and eliminating the root causes. Int J Syst Assur Eng Manag 7 (Suppl 1), 305–321 (2016). https://doi.org/10.1007/s13198-015-0405-z

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