Suprakash Gupta

Selection of maintenance strategy for aircraft systems using multi-criteria decision making methodologies

This paper, proposes the Multi-Criteria Decision Making (MCDM) methodology for selection of a maintenance strategy to assure the consistency and effectiveness of maintenance decisions. The methodology is based on an AHP-enhanced TOPSIS, VIKOR and benefit-cost ratio, in which the importance of the effectiveness appraisal criteria of a maintenance strategy is determined by the use of AHP. Furthermore, in the proposed methodology the different maintenance policies are ranked using the benefit-cost ratio, TOPSIS and VIKOR. The method provides a basis for consideration of different priority factors governing decisions, which may include the rate of return, total profit, or lowest investment. When the preference is the rate of return, the benefit-cost ratio is used, and for the total profit TOPSIS is applied. In cases where the decision maker has specific preferences, such as the lowest investment, VIKOR is adopted. The proposed method has been tested through a case study within the aviation context for an aircraft system. It has been found that using the methodology presented in the paper, the relative advantage and disadvantage of each maintenance strategy can be identified in consideration of different aspects, which contributes to the consistent and rationalized justification of the maintenance task selection. The study shows that application of the combined AHP, TOPSIS, and VIKOR methodologies is an applicable and effective way to implement a rigorous approach for identifying the most effective maintenance alternative.

Reliability Analysis of a conveyor system using hybrid data

The quantification of a fault tree is difficult without an exact probability value for all of the basic events of the tree. To overcome this difficulty in this paper, we propose a methodology which employs ‘hybrid data’ as a tool to analyse the fault tree. The proposed methodology estimates the failure probability of basic events using the statistical analysis of field recorded failures. Under these circumstances, where there is an absence of past failure records, the method follows a fuzzy set based theoretical evaluation based on the subjective judgement of experts for the failure interval. The proposed methodology has been applied to a conveyor system. The results of the analysis reveal the effectiveness of the proposed methodology and the instrumental role played by the experience of experts in providing reliability oriented information. Copyright

Replacement and maintenance analysis of longwall shearer using fault tree technique

Abstract The productivity and profits of a mining company arise out of the reliable performance of its major equipment. Shearer is cutting and loading equipment used in the longwall mining of coal. Systematic reliability analysis reveals the sources of weakness that adversely affects the production and loopholes in the system. The purpose of the reported study is to develop a method from maintenance information such as time to failure (TTF) for pin pointing the weak links in the system. The present study has used fault tree (FT) technique to understand the failure logic of a longwall shearer and its components were ranked by Birnbaum factor. The subsequent analysis shows that results can be useful in replacement policy as well as in maintenance.

An analytical hierarchy process (AHP)-guided decision model for underground mining method selection

Decision making in mining, especially the selection of an appropriate stoping method for an ore deposit, is a critical and challenging task owing to its compliance with a set of criteria. The lack of quantitative data and the qualitative nature of the task forces one to adopt an experience- and intuition -based technique like the analytical hierarchy process (AHP). This article examines the application of the AHP for the selection of an appropriate stoping method from a group of alternatives. The developed model has been verified by a number of case studies.

A control chart guided maintenance policy selection

In addition to adopting new and advanced technology, effective maintenance management is one of the key parameters for meeting ever increasing production targets both in terms of quality and quantity. A lot of capital may be drained off in the absence of sound maintenance policy and there is ample scope to minimise this loss through proper maintenance decisions. This study aims to develop a scheme for maintenance policy decisions through a time-based control chart (t-chart) that monitors the failure process of the component or system under investigation. On the basis of the nature of the control chart, eight maintenance policy zones were identified. A case study on an Armoured Flexible Conveyor (AFC) used in an underground coal mine in India was undertaken to illustrate the use of the developed scheme for making maintenance policy decisions.

Cost-effective importance measure : a new approach for resource prioritization in a production plant

Purpose – The purpose of this paper is to propose a new measure for criticality analysis of different components of a production plant, called cost‐effective importance measure (CEIM) that considers the components performance, system structure and economic aspects.Design/methodology/approach – In this work, an explorative literature study covering the concept of importance measure and criticality analysis has been carried out on contemporary literature. The literature study shows that the commonly used importance measures consider the probability of failure of a component and systems structure, and ignore the effect or severity of failures, which is an important factor in engineering decision making. It is not clear how to use the concept of importance measure in combination with cost parameter. Hence, a cost‐effective importance measure (CEIM) is defined and a case study is presented, to demonstrate the application of the proposed importance measure.Findings – The paper indicates that CEIM useful for th...

AN APPROACH FOR RISK ASSESSMENT OF RAIL DEFECTS

Rail defects appear in a greater variety and frequency due to higher axle loads and increasing traffic density in passenger and freight trains. Many of these rail defects, if left undetected, can develop into rail breaks, which may lead to train derailments. Reduction in the number of such catastrophic events requires huge investments on inspection and maintenance activities. Therefore proper maintenance planning and risk assessment is required to reduce the rail maintenance cost without compromising safety. In this paper, an approach has been developed for risk assessment of rail defects to support the decision-making process during scheduling of rail inspection and grinding frequency, based on the type of defect and its risk of occurring and developing into a rail break. The approach will help in increasing the safety of passengers and rolling stock as well as reducing the overall rail maintenance cost, as it helps in making effective decisions related to inspection frequency (i.e. resource allocation according to the need). The approach is presented with the aid of a case study from the Swedish National Rail Administration (Banverket). Both, quantitative and qualitative analysis technique has been used in this paper to assess the risk of occurrence of a rail defect and its development into a rail break.

Synthesis, characterization and application of novel cationic and amphoteric flocculants based on amylopectin.

The synthesis of novel cationic flocculants based on amylopectin (AP), acrylamide (AM) and (3-acrylamidopropyl) trimethylammonium chloride (ATMAC) were done by free radical polymerization using ammonium persulphate (APS) as an initiator. Three different grades of novel cationic flocculants (AP-g-C 1 to AP-g-C 3) were synthesized by varying the proportion of acrylamide and (3-acrylamidopropyl) trimethylammonium chloride monomers. Through the hydrolysis of these flocculants, in presence of NaOH, three different grades of amphoteric polymers (AP-AT-C 1 to AP-AT-C 3) were synthesized. The synthesized polymers were characterized by various methods, namely, infrared spectroscopy, NMR spectroscopy, thermal analysis, viscosity measurement, scanning electron microscopy and X-ray diffractometry. The flocculation performance of AP-g-C and AP-AT-C were studied in kaolin suspension using jar test and settling test methods at neutral pH. Dye (Methylene blue) removal tests were performed using polymer beads and analysed by UV-vis spectroscopy.

A vector-dissimilarity-based approach for multi-criteria decision making

Decision making in complex environments is influenced by a large number of conflicting and incommensurable factors. It is multidimensional and the influencing factors affect the decision process to varying degrees. The performance measurement of decision alternatives in multi-criteria decision making (MCDM) may be represented as a multidimensional vector in real space. The technique for order preference by similarity to ideal solution (TOPSIS) is a popular MCDM tool. However, sometimes this technique shows its inability to differentiate between decision alternatives. In this paper the limitations of TOPSIS are examined and an improvement is suggested. The proposed new method uses the vector dissimilarity approach to remove the boundary restrictions and achieve greater sovereignty. The improved method has been demonstrated through a case study of maintenance design for dump trucks deployed in a large surface mine.

Assessment of Human Error Rate in Different Underground Coal Mining Activities Using Rough Set Theory

Assessment and prediction of human error rate plays the vital role in the probabilistic safety and risk analysis. Unfortunately no generalized method for human error classification and quantification exist. Prevailing methods are industry specific and give only a rough estimate. Underground coal mining is inherently hazardous and estimation of human error rate for mining activities is mandatory for analysis and enhancement of safety. Present study embraces the cognitive reliability and error analysis method for assessing the human error rate in reference to the system context. The present study captured the embedded uncertainty in data classification, context assessment and error categorization using fuzzy relational mapping and rough set theory.