Development of Simulation for Condition Monitoring and Evaluation of Manufacturing Systems

Equipment Condition Management used for predicting the performance parameters required for maintenance decision making was developed. This program predicts the state probabilities and maintenance action recommendation based the predetermined alert levels. The maintenance program software was developed from the derived stable state probability models using algebraic substitution and computation of the breakdown data and operational data of the MTTF, MTTR, λ and µ of these equipment/component(s) at PM and CM states with implementation algorithm. The models were derived using mechanistic modeling technique such that all the relevant variables of the reliability process were accounted for. Validation analysis of this simulation revealed its prediction accuracy of over 99%. Therefore, its use in the monitoring and evaluation of the health conditions of production systems remains very essential. Keywords: Mechanistic model, process parameter, stable state probabilities, prediction algorithm, Equipment Condition Managemen

Development of State Duration and Expectation Model for Evaluating Remaining Life of Manufacturing Systems

The key goal of predicting the state expectation of an engineering system is to predict the remaining life of the system so as to aid in maintenance decision-making activities. In this paper, a maintenance model for predicting the state expectation of industrial machines has been developed. It incorporates various stages of deterioration and maintenance states. Given that a current state has been attained, from inspection and diagnosis, this model is capable of computing the predicted average time before a system failure occurs. This study focuses on using real data of an industrial Bottle filler machine to test the effectiveness of the State Expectation model and its effect on the reliability and maintainability of the machine. The model is tested for various scenarios by changing one of the main parameters during each calculation while others are kept constant. For the state duration sensitivity analysis, as the failure rate continuously increases from 0.0178 to 0.7060, the expected mean sojourning time for each degradation state decreased from 220.97hrs to 1.59hrs. Subsequently at uniform incrementally varied repair/maintenance rate (0.0861 to 0.7663), the state expectation of the equipment increases from 168.11hrs to1189.98hrs. This allowed us to determine the most suitable decision to improve the reliability of the Bottle filler machine. The prediction result identifies the effectiveness of the proposed method in predicting RUL of manufacturing systems. Keywords: Remaining Useful Life; State Expectation Model; Maintenance; Decision making; Prediction

A systematic literature review on the decarbonisation of the building sector—a case for Nigeria

The buildings sector is responsible for over 36% of total global end-use energy utilization and nearly 40% of the total indirect and direct carbon emissions. Low-carbon or zero-energy buildings remain the only option to lessen the sector’s energy consumption and CO2 emissions. The current systematic study examines low-carbon buildings under deep decarbonization scenarios in selected global south regions from 2010 to 2021. The study was channelled by the PRISMA (“Preferred Reporting Items for Systematic reviews and Meta-Analyses”) review process, which identified 29 related articles from Scopus, Web of Science., and Google Scholar databases. The identified critical drivers of emissions were population, gross domestic product, dwelling characteristics, and urbanization. The dwelling characteristics contributed about 12% and 27% to the total CO2 emissions in the selected regions. The population varies between 23% and 27% across the areas. Specific findings were made for inclusion in the Nigeria model while the general results were observed and further studies proposed. Total investment from the private and public sectors was identified as key to achieving the transition process of decarbonization in the building sector

A Perspective Review on Thermal Conductivity of Hybrid Nanofluids and Their Application in Automobile Radiator Cooling

Hybrid nanofluids developed with the fusion or suspension of two or more different nanoparticles in a mixture as a novel heat transfer fluid are currently of interest to researchers due to their proven better measured thermal conductivities. Several reviewed articles exist on the thermal conductivity of hybrid nanofluids, a vital property for which the heat transfer rate is directly dependent. This review aims to understand the current developments in hybrid nanofluids and their applications. An extensive literature survey was carried out of heuristic-based articles published in the last 15 years. The review reiterates topical research on the preparation methods and ways to improve the stability of readied fluid, thermophysical properties of mixture nanofluids, and some empirical correlations developed for estimating thermal conductivity. Hybrid nanofluid studies on heat transfer performance in automobile radiator cooling systems were also obtained and discussed. The review’s significant findings include the following: (1) hybrid nanofluids produce a noticeable thermal conductivity enhancement and a relatively higher heat transfer coefficient than mono nanofluids and regular liquids. Furthermore, through the uniform dispersion and stable suspension of nanoparticles in the host liquids, the maximum possible thermal augmentation can be obtained at the lowest possible concentrations (by <0.1% by volume). (2) An automobile radiator’s overall heat transfer accomplishment can thus be boosted by using a mixture of nanofluids as conventional coolants. Up-to-date literature results on the thermal conductivity enhancement of mixture fluids are also presented in this study. Nonetheless, some of the barriers and challenges acknowledged in this work must be addressed for its complete deployment in modern applications