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1 – 10 of 586Dilbagh Panchal, Sachin Kumar Mangla, Mohit Tyagi and Mangey Ram
The purpose of this paper is to develop a fuzzy methodology approaches based framework for carrying the risk analysis of a real industrial system of a urea fertilizer industry…
Abstract
Purpose
The purpose of this paper is to develop a fuzzy methodology approaches based framework for carrying the risk analysis of a real industrial system of a urea fertilizer industry located in northern part of India.
Design/methodology/approach
Petri Net approach was applied for representing the series-parallel arrangement of the considered system. Various failure causes related to different subsystems or equipment of the considered system were listed under FMEA approach and their Risk Priority Number was tabulated. Further, to overcome the drawbacks of traditional FMEA approach in risk ranking fuzzy FMEA and grey relation analysis (GRA) approaches were applied within traditional FMEA approach and the ranking results were compared for better and effective decision making of risky components.
Findings
The proposed framework has overcome the drawbacks of tradition FMEA approach in an effective and efficient manner. Causes AC7, CL3, ST2, DR3 and NR3 of centrifugal compressor, hot heat exchanger, ammonia convertor reactor, cold condenser and ammonia separator have been identified as the most critical failure causes of the considered system.
Originality/value
The proposed framework has been tested with its application on an ammonia synthesis system of the considered process industry. The risk ranking results would be highly useful in developing a planned maintenance policy for the considered system which further results in improving the system availability.
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The purpose of this paper is to present a technique for analyzing the behavior of an industrial system utilizing vague, imprecise, and uncertain data. The synthesis unit of a urea…
Abstract
Purpose
The purpose of this paper is to present a technique for analyzing the behavior of an industrial system utilizing vague, imprecise, and uncertain data. The synthesis unit of a urea plant situated in northern part of India has been considered to demonstrate the proposed approach. Sensitivity analysis of system's behaviour has also been done in it.
Design/methodology/approach
In the proposed approach, traditional Lambda‐Tau technique is used to analyze the behavior of a complex repairable synthesis unit of a fertilizer industry up to a desired degree of accuracy. This technique has been demonstrated by computing eight well‐known reliability indices which are used for behaviour analysis of the considered system in a more promising way.
Findings
The study of analysis of reliability, availability, maintainability etc can help in increasing the production and quality of synthesis. To ensure the system performance throughout its service life, it is necessary to set up proper maintenance, planning and control which can be done after studying the variation of reliability, availability with respect to time. Thus, it may be a more useful analysis tool to access the current system condition and involved uncertainties. The present paper suggested an approach to improve the system's performance.
Originality/value
The paper suggests a technique for analyzing the stochastic behavior of an industrial subsystem by computing eight well‐known reliability indices in the form of fuzzy membership function. The benefits for the methodology include the ability to model and deal with highly complex system as fuzzy sets can deal easily with approximations and it helps in improving and handling the uncertainties and possibilities.
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The most important component of a coating is the resin: all other components simply modify the resin. Solvents affect the flow, pigments give decorative effects and aid economy;…
Abstract
The most important component of a coating is the resin: all other components simply modify the resin. Solvents affect the flow, pigments give decorative effects and aid economy; the physical and chemical properties of the resins used are the principal differences in various paint systems
Abdul Kareem Abdul Jawwad and Ibrahim AbuNaffa
The purpose of this paper is to help newly established plants with minimal or no historical machine data select best maintenance strategies that suit their specific working setup…
Abstract
Purpose
The purpose of this paper is to help newly established plants with minimal or no historical machine data select best maintenance strategies that suit their specific working setup and at the same time satisfy relevant selection criteria.
Design/methodology/approach
Analytical hierarchy process (AHP) was applied successfully in this study to select the maintenance strategy at a newly established chemical fertilizers plant. Implementation started by identifying main and sub-criteria pertinent to maintenance practice in this particular industry. Pair-wise comparisons and consistency calculations were carried out on the chosen criteria and then were used to assess candidate maintenance strategies through a special scoring process. The methodology included the use of surveys, brainstorming and expert consultation.
Findings
The results have shown that the most important main criteria are cost, resources, failures, management, operations, quality and safety. The final maintenance strategy selected for the plant under consideration included a mix of condition-based predictive maintenance (PDM), time-based preventive maintenance (PM) and corrective maintenance (CM). The best balance between the three maintenance activities, which satisfies the maintenance criteria with technical applicability, was found to be 50, 23 and 19% for PDM, PM and CM, respectively.
Originality/value
The present paper is a novel application of AHP coupled with deterministic application-specific ranking for devising a procedure for selecting viable and applicable comprehensive maintenance strategies for newly established chemical fertilizers plants with no historical data on machine failures.
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Monika Saini, Deepak Sinwar, Alapati Manas Swarith and Ashish Kumar
Reliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of…
Abstract
Purpose
Reliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of the best-fitted probability distribution are also contributing significantly to reliability estimation. In this work, a case study of load haul dump (LHD) machines is illustrated that consider the optimization of failure and repair rate parameters using two well established metaheuristic approaches, namely, genetic algorithm (GA) and particle swarm optimization (PSO). This paper aims to analyze the aforementioned points.
Design/methodology/approach
The data on time between failures (TBF) and time to repairs (TTR) are collected for a LHD machine. The descriptive statistical analysis of TBF & TTR data is performed, trend and serial correlation tested and using Anderson–Darling (AD) value best-fitted distributions are identified for repair and failure times of various subsystems. The traditional methods of estimation like maximum likelihood estimation, method of moments, least-square estimation method help only in finding the local solution. Here, for finding the global solution two well-known metaheuristic approaches are applied.
Findings
The reliability of the LHD machine after 60 days on the real data set is 28.55%, using GA on 250 generations is 17.64%, and using PSO on 100 generations and 100 iterations is 30.25%. The PSO technique gives the global best value of reliability.
Practical implications
The present work will be very convenient for reliability engineers, researchers and maintenance managers to understand the failure and repair pattern of LHD machines. The same methodology can be applied in other process industries also.
Originality/value
In this case study, initially likelihood function of the best-fitted distribution is optimized by GA and PSO. Reliability and maintainability of LHD machines evaluated by the traditional approach, GA and PSO are compared. These results will be very helpful for maintenance engineers to plan new maintenance strategies for better functioning of LHD machines.
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Ajay Kumar, S.P. Sharma and Dinesh Kumar
– The purpose of this paper is to develop a new approach for computing various performance measures such as reliability, availability, MTBF, ENOF, etc. for any industrial system.
Abstract
Purpose
The purpose of this paper is to develop a new approach for computing various performance measures such as reliability, availability, MTBF, ENOF, etc. for any industrial system.
Design/methodology/approach
Pulping system, the main functionary part of paper industry, is the subject of the study. The interactions among the working components are shown using Petri nets (PNs). Failure and repair rates are represented using triangular fuzzy numbers (TFNs), as they allow expert opinion, linguistic variables, operating conditions, uncertainty and imprecision in reliability information, to be incorporated into system model. The failure rates and repair times of all constituent components are obtained using genetic algorithms (GAs) and then various performance measures are computed using fuzzy Lambda-Tau methodology (FLTM).
Findings
The proposed methodology provides a better understanding about the behavior of any repairable system through its graphical representation.
Originality/value
A new approach has been given to compute various performance measures and based on calculated reliability parameters, a structured framework has been developed that may help the maintenance engineers to analyze and predict the system behavior.
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