QUALITY ENHANCEMENT IN MAINTENANCE PLANNING THROUGH NON-IDENTICAL FMECA APPROACHES

The purpose of this paper is to investigate the scope of reliability improvement of aluminium wire rolling mill. This paper addresses the performance reliability of continuous process industry of interest to many applications in maintenance planning where multi-attribute decision making (MADM) approaches are very useful. The paper addresses the process of discriminating critical components through substantial shop-floor failure data. The research work narrates a method for evaluating risk priority number (RPN) traditionally. Moreover, the maintainability criticality index (MCI) for each failure cause of identified critical components is evaluated through two disparate MADM failure models: technique for order preference by similarity to ideal solution (TOPSIS) and preference section index (PSI) to overcome the limitations of more traditional approaches. The primary findings of this research work are to enhance quality in planning the maintenance activities of critical components of targeted process industry through traditional as well as nontraditional failure analysis models. The research work is focused on potential failure causes of critical components like; bearings, gears, and shafts of aluminium wire rolling mill which are commonly representing the most critical components in a large range of industrial processes including aluminium wires. The proposed work will illustrate the working lives of components and associated failures. It will help to elucidate maintenance issues of major process industries and recommended deliverable keys.

[1]  Lijun Yang,et al.  Cloud model-based failure mode and effects analysis for prioritization of failures of power transformer in risk assessment , 2013 .

[2]  Andreas Archenti,et al.  Condition based maintenance of machine tools: Vibration monitoring of spindle units , 2017, 2017 Annual Reliability and Maintainability Symposium (RAMS).

[3]  Wojciech Sałabun,et al.  The mean error estimation of TOPSIS method using a fuzzy reference models , 2013 .

[4]  F. Zhang,et al.  Failure modes and effects analysis based on fuzzy TOPSIS , 2015, 2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS).

[5]  Rajeev Rathi,et al.  A fuzzy-MADM based approach for prioritising Six Sigma projects in the Indian auto sector , 2017 .

[6]  Yong-Huang Lin,et al.  Dynamic multi-attribute decision making model with grey number evaluations , 2008, Expert Syst. Appl..

[7]  Francesco Zammori,et al.  ANP/RPN: a multi criteria evaluation of the Risk Priority Number , 2012, Qual. Reliab. Eng. Int..

[8]  Balbir S. Dhillon Quality control, reliability, and engineering design , 1985 .

[9]  Goutam Kumar Bose,et al.  Multi criteria FMECA for coal-fired thermal power plants using COPRAS-G , 2014 .

[10]  Mohammadsadegh Mobin INTEGRATING FAHP WITH COPRAS-G METHOD FOR SUPPLIER SELECTION ( CASE STUDY : AN IRANIAN MANUFACTURING COMPANY ) , 2015 .

[11]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[12]  S. Chakraborty,et al.  Cutting tool material selection using grey complex proportional assessment method , 2012 .

[13]  Anish Sachdeva,et al.  Multi-factor failure mode critically analysis using TOPSIS , 2009 .

[14]  Loon Ching Tang,et al.  Fuzzy assessment of FMEA for engine systems , 2002, Reliab. Eng. Syst. Saf..

[15]  Warren Gilchrist,et al.  Modeling Failure Modes and Effects Analysis , 1993 .

[16]  P. A. Bennett Practical Reliability Engineering , 1982 .

[17]  Nalinee Chanamool,et al.  Fuzzy FMEA application to improve decision-making process in an emergency department , 2016, Appl. Soft Comput..

[18]  Marcello Braglia,et al.  Monte Carlo simulation approach for a modified FMECA in a power plant , 2000 .

[19]  Willy W. Vandenbrande,et al.  How to Use FMEA to Reduce the Size of Your Quality Toolbox , 1998 .

[20]  Priyank J. Prajapati,et al.  Maintenance Optimization for Critical Equipments in Process Industries , 2018 .

[21]  T. Sahoo,et al.  Maintenance Optimization for Critical Equipments in process industries based on FMECA Method , 2014 .

[22]  Chiu-Chi Wei,et al.  FAILURE MODE AND EFFECTS ANALYSIS USING FUZZY METHOD AND GREY THEORY , 1999 .

[23]  K. Maniya,et al.  A selection of material using a novel type decision-making method: Preference selection index method , 2010 .

[24]  Navid Akar,et al.  Risk analysis of geothermal power plants using Failure Modes and Effects Analysis (FMEA) technique , 2013 .

[25]  Dinesh Khanduja,et al.  Application of Fuzzy TOPSIS MADM approach in ranking & underlining the problems of plywood industry in India , 2016 .

[26]  Marcello Braglia,et al.  MAFMA: multi‐attribute failure mode analysis , 2000 .

[27]  Ana Pavlovic,et al.  USING A TOTAL QUALITY STRATEGY IN A NEW PRACTICAL APPROACH FOR IMPROVING THE PRODUCT RELIABILITY IN AUTOMOTIVE INDUSTRY , 2014 .