DR. Pushparenu Bhattacharjee

Dr. Pushparenu Bhattacharjee

Associate Professor

PhD.

Research Interests & Expertise

Operations & Supply chain management, Supply chain management.

RESEARCH PAPERS

2023

Failure mode and effects analysis for submersible pump component using proportionate risk assessment model: a case study in the power plant of Agartala

Authors: Pushparenu Bhattacharjee, Syed Abou Iltaf Hussain, V. Dey, U. K. Mandal
Journal: International Journal of System Assurance Engineering and Management
Publication date: 01 Octuber 2023
Publisher: Springer
URL: Access Paper

Abstract

This study intended to identify the barriers that inhibit the adoption of online dating apps (ODAs). It applied innovation resistance theory, with attitude being used as a moderator. A sample of 440 responses from ODAs users was analyzed using structural equation modeling. The three barriers of risk, usage, and tradition have a significant negative influence on the adoption intention of ODAs. Attitude was found to moderate the association between the usage barrier and adoption intention, whereas attitude did not moderate the association between the risk barrier and adoption intention or that between the tradition barrier and adoption intention. Age, gender, income, educational qualification, and household size were used as control variables. The study contributes to the current body of knowledge by identifying the barriers that influence the adoption intention of ODAs. Practical implications for the professionals and firms engaged in ODAs, as well as theoretical contributions, demonstrate the benefits of the study.

Failure Mode and Effects Analysis (FMEA) using interval number based BWM—MCDM approach: Risk Expected Value (REV) method

Authors: Pushparenu Bhattacharjee, Vidyut Dey, U. K. Mandal

Journal: Soft Computing

Publication Date: 01 November 2023

Publisher: Springer Science and Business Media Deutschland GmbH

URL: Access Paper

Abstract

One of the most popular structured approaches in risk assessment is Failure Mode and Effects Analysis (FMEA) that helps in discovering potential failures existing within the design of a product or process. But numerous inadequacies are conjoined with it, for example, Risk Priority Number (RPN) used in FMEA fails to consider the individual effects of the risk factors, thereby neglecting the priority importance of each potential failure modes (PFMs). In this paper, a novel approach, namely, REV method is proposed, where subjective weights of risk factors are determined by using Interval number based Best Worst Method (BWM) to evaluate the weights of risk factors and determine their importance. REV is proposed as an alternative to RPN and aims to improve FMEA that could efficiently handle the vagueness and uncertainty of real-life situations. It is benefitted from decisions of both probability of risk of failure, for assessing the individual influence of the risk factors, as well as priority weights of PFMs from the preference decisions making ability of the MCDM methods with conflicting criteria. It is a user-friendly, flexible approach where suitable MCDM method of choice can be used for obtaining REVs. Here, MCDM techniques of TOPSIS, VIKOR, PROMETHEE and EDAS are used for reviewing individual impacts of PFMs. Furthermore, the proposed approach is endorsed with a case study involving failures in components of submersible pumps used in a power plant. The model is validated using Kendall Tau coefficient computed for different REVs and results are found to be satisfactory (0.849 for TOPSIS-VIKOR, 0.832 for PROMETHEE-EDAS, 0.851 for VIKOR- EDAS and 0.934 for TOPSIS- EDAS).

2022

Quantitative risk assessment of submersible pump components using Interval number-based Multinomial Logistic Regression(MLR) model

Authors: Pushparenu Bhattacharjee, Vidyut Dey, U. K. Mandal

Journal: Reliability Engineering and System Safety

Publication Date: 30 July 2022

Publisher: Elsevier Ltd

URL: Access Paper

Abstract: This paper aims at developing a methodology to explore the association among the risk factors affecting failure, concerning Potential Failure Mode (PFMs) using Multinomial Logistic Regression (MLR). MLR model helps in assigning different weights to the risk factors in the form of MLR equations, where the response variable ‘Failure’ is treated as a categorical variable with three defined levels (Critical ‘1′; Medium ‘2′ & Less Critical ‘3′). This model appointed three experienced decision-makers to articulate their opinions using linguistic variables, expressed as interval numbers about PFMs concerning nine risk factors, unlike the traditional approach, and provides flexibility to accommodate as many risk factors as possible. The proposed approach is demonstrated with the help of a case study involving failures in components of a submersible pump used in a power plant. The model’s probability of critical failure (‘1′) prediction is 86.3% and 83.4% with training and test data respectively. Furthermore, the model showed a higher overall accuracy of 91% with training data. Also, sensitivity analysis, specificity, and ROC curve are carried out to validate the model. The proposed methodology shows PFM 4 (‘O’ ring) with the highest probability of failure, followed by PFM 9 (gasket).

2020

Risk assessment by failure mode and effects analysis (FMEA) using an interval number based logistic regression model

Author: Pushparenu Bhattacharjee, Vidyut Dey, U. K. Mandal

Journal: Safety Science

Publication Date: 1 December 2020

Publisher: Elsevier B.V.

URL: Access Paper

Abstract

In order to reduce risks of failure, industries use a methodology called Failure Mode and Effects Analysis (FMEA) in terms of the Risk Priority Number (RPN). The RPN number is a product of ordinal scale variables, severity (S), occurrence (O) and detection (D) and product of such ordinal variables is debatable. The three risk attributes (S, O, and D) are generally given equal weightage, but this assumption may not be suitable for real-world applications. Apart from severity, occurrence, and detection, the presence of other risk attributes may also influence the risk of failure and hence should be considered for achieving a holistic approach towards mitigating failure modes. This paper proposes a systematic approach for developing a standard equation for RPN measure, using the methodology of interval number based logistic regression. Instead of utilizing RPN in product form for each failure, this method is benefited from decisions based on probability of risk of failure, ‘P’ which is more realistic in practical applications. A case study is presented to illustrate the application of the proposed methodology in finding the risk of failure of high capacity submersible pumps in the power plant. The developed logistic regression model (logit model) using R software helped in generating the probability of risk of failure equation for predicting the failures. The model showed the correct classification rate to be 77.47%. The Receiver Operating Characteristic (ROC) curve showed the logit-model to be 81.98% accurate with an optimal cut-off value of 0.56.

2018

Selection of optimal aluminum alloy using TOPSIS method under fuzzy environment

Author: Pushparenu Bhattacharjee, Abhiman Debnath, Sujoy Chakraborty

Journal: IOS Press

Publication Date: 28 December 2018

Publisher: Journal of Intelligent and Fuzzy Systems

URL: Access Link

Abstract

Selection of material plays an important role for any industry as an improper selection of material may lead to loss of lives, equipment and money. The present study aims to overcome this kind of problem by finding the best aluminum alloy for industries like aerospace and automotive which are seeks to made parts with this material because of its outstanding properties. A Multi Criteria Decision Making (MCDM) method known as TOPSIS (Technique of Order Preference by Similarity to the Ideal Solution) is utilized under fuzzy environment considering multiple qualitative and quantitative criterion values. A number of decision maker’s opinion is considered with a Triangular fuzzy numbers for weighing the criteria. Best alternative is chosen based on the distance of alternatives from positive and negative ideal solution. It is observed that Al-5083-H112 is the best alternative. Sensitivity analysis is also done to determine how different values of an independent variable will impact a particular dependent variable under a given set of assumptions.