Nishit Kumar Srivastava

Development of Predictive Maintenance Model for N-Component Repairable System Using NHPP Models and System Availability Concept

The technologically intensive nature of the predictive maintenance (PdM) method restricts its use to companies with higher turnover. This research is aimed to propose a PdM model for an N-component repairable system by integrating non-homogeneous Poisson process (NHPP) models and a system availability concept such that the use of technology is minimized, thereby extending its applicability to companies with lower turnover. It is known that manufacturing systems show reliability degradation with repeated overhauls and component replacements. This has the effect that the mean time between failures (MTBF) is non-identically distributed. Hence, the failure pattern of each component is analyzed using NHPP models and the mean system availability is calculated, which is now compared with the threshold system availability deciding the overall maintenance of the system. Further, the developed model is validated on a wheat flour mill.

Predictive maintenance using FMECA method and NHPP models

Most of predictive maintenance technologies are inaccessible to small scale and medium scale industries due to their demanding cost. This paper proposes a predictive maintenance policy using failure mode effect and criticality analysis (FMECA) and non-homogeneous Poisson process (NHPP) models which require minimal use of advanced monitoring technologies and sophisticated data acquisition systems. Most of the repairable systems show long term reliability degradation with repeated overhauls. Here, critical component of a system or machinery exhibiting sad (deteriorating) trend is used as an indicator to predict overall maintenance time of a system. Firstly, the component to be used as an indicator for predictive maintenance is chosen using FMECA method, in which the most critical component is chosen. Secondly, the failure data of the chosen component is analysed using NHPP models and based on analysis of the data, relevant NHPP model is selected and finally, the Mean Time Between Failure (MTBF) of the component is compared with the threshold mean time between failure [MTBF(Th)] of the component to decide the overall maintenance time for the system. The developed methodology is validated on an overhead crane in a steel manufacturing company.

Assessment of Environmental Risk from the Project Team’s Perspective in Electrical Transmission Line Installation Projects

This article represents a methodology in which the overall environmental risks can be assessed without much need of secondary data. The method exemplifies on collection of risk data on the basis of pre-decided risk factors in which the project team members are asked to give opinion about various risk factors. Since the main risk management team has already given various pre-defined risk sectors the collective opinion derived gives true value of risk, and the involvement of local workers give an edge over others since they have vast knowledge of geographical factors and this is how their knowledge can be scientifically used to determine environmental risk which is a very true estimation of real risk value. This article has successfully identified factors and scaled them up with the help of a questionnaire-based study. Moreover, different levels of management also pose different environment risk priorities which is also analysed in this article.

Development of framework for predictive maintenance in Indian manufacturing sector

Every machine degrades with time and requires maintenance. Among all types of maintenance policies, predictive maintenance is established as the best form of maintenance policy as numerous benefits are associated with it. Despite all the benefits, it finds restrictive usage in manufacturing companies. A literature survey reveals that limited funds is the major reason for restrictive usage of predictive maintenance, as predictive maintenance is capital intensive. In this paper, a predictive maintenance framework using predictive maintenance models with no investment on technology component is proposed for Indian manufacturing sector.

A Project Risk Management Methodology Based on Probabilistic and Non-probabilistic Approach: A Study on Transmission Line Installation Projects

The article deals with the project risk analysis for electrical transmission line installation projects, where both probabilistic decision tree (DT) and non-probabilistic analytical hierarchy process (AHP) methods are used simultaneously on the same risks. The study gives the complete analysis for each risk type and finally they are analyzed by both AHP and DT; both of these techniques are selected for project’s risk minimization while designing the risk mitigation plan. A model is generated for minimizing the project risk and the risks selected after being analyzed through AHP and DT are then treated with this model for risk mitigation. Overall the study minimizes project risk by a three-step procedure, that is, AHP, DT, and finally through the risk minimization model for electrical transmission line installation projects.