Hanna Lo, Alireza Ghasemi, Claver Diallo and John Newhook
Condition-based maintenance (CBM) has become a central maintenance approach because it performs more efficient diagnoses and prognoses based on equipment health condition compared…
Abstract
Purpose
Condition-based maintenance (CBM) has become a central maintenance approach because it performs more efficient diagnoses and prognoses based on equipment health condition compared to time-based methods. CBM models greatly inform maintenance decisions. This research examines three CBM fault prognostics models: logical analysis of data (LAD), artificial neural networks (ANNs) and proportional hazard models (PHM). A methodology, which involves data pre-processing, formulating the models and analyzing model outputs, is developed to apply and compare these models. The methodology is applied on NASA’s Turbofan Engine Degradation data set and the structural health monitoring (SHM) data set from a Nova Scotia Bridge. Results are evaluated using three metrics: error, half-life error and a cost score. This paper concludes that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably, and its predictions show much larger variance than the predictions from the other three methods. Based on these conclusions, the purpose of this paper is to provide recommendations on the appropriate situations in which to apply these three prognostics models.
Design/methodology/approach
LAD, ANNs and PHM methods are adopted to perform prognostics and to calculate the mean residual life (MRL) of eqipment using NASA’s Turbofan Engine Degradation data set and the SHM data set from a Nova Scotia Bridge. Statistical testing was used to evaluate the statistical differences between the approaches based on these metrics. By considering the differences in these metrics between the models, it was possible to draw conclusions about how the models perform in specific cases.
Findings
Results were evaluated using three metrics: error, half-life error and a cost score. It was concluded that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably and its predictions show much larger variance than the predictions from the other three methods. Overall the models predict failure after it has already occurred (negative error) when the residual life is large and vice versa.
Practical implications
It was concluded that a good CBM prognostics model for practical implications can be determined based on three main considerations: accuracy, run time and data type. When accuracy is a main concern, as in the case where impacts of failure are large, LAD and feedforward neural network are preferred. The preference changes when run time is considered. If data can be easily collected and updating the model is performed often, the ANNs and LAD are preferred. On the other hand, if CM data are not easily obtainable and existing data are not representative of the population’s behavior, data type comes into play. In this case, PHM is preferred.
Originality/value
Previous research in the literature performed reviews of multiple independent studies on CBM techniques performed on different data sets. They concluded that it is typically harder to implement artificial intelligence models, because of difficulties in data procurement, but these approaches offer improved performance as compared to more traditional model-based and statistical approaches. In this research, the authors further investigate and compare the performance and results from two major artificial intelligence models, namely, ANNs and LAD, and one pioneer statistical model, PHM over the same two real life prognostics data sets. Such in-depth comparison and review of major CBM techniques was missing in current literature of CBM field.
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Seyed Mohamad Fakhr Mousavi, Alireza Amirteimoori, Sohrab Kordrostami and Mohsen Vaez-Ghasemi
As returns to scale (RTS) describes the long run connection of the changes of outputs relative to increases in the inputs, the purpose of this study is to answer the following…
Abstract
Purpose
As returns to scale (RTS) describes the long run connection of the changes of outputs relative to increases in the inputs, the purpose of this study is to answer the following questions: If the proportionate changes exist in the inputs, what is the rate of changes in outputs with respect to the inputs’ variations in the two-stage networks over the long term? How can the authors investigate quantitative RTS in the two-stage networks? In other words, the purpose of this research is to introduce a different approach to estimate the performance, RTS and scale economies (SE) in network structures.
Design/methodology/approach
This paper proposes a novel non-radial approach based on data envelopment analysis to analyze the performance and to investigate RTS and SE in two-stage processes.
Findings
The findings show that the range adjusted measure (RAM)/RTS approach can identify reference sets for overall systems and each stage. In addition, the models presented in this paper can classify decision-making units and determine the increasing/decreasing trends of RTS.
Originality/value
The majority of previous RTS studies have been examined in black-box structures and have been discussed in a radial framework. Therefore, in this study, RTS and SE in the two-stage networks are dealt with using an extended RAM approach. Actually, the efficiency and RTS for each stage and the overall model are calculated using the proposed technique.
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Alireza Rahimi, Ali Dehghan Saee, Abbas Kasaeipoor and Emad Hasani Malekshah
The purpose of this paper is to carry out a comprehensive review of some latest studies devoted to natural convection phenomenon in the enclosures because of its significant…
Abstract
Purpose
The purpose of this paper is to carry out a comprehensive review of some latest studies devoted to natural convection phenomenon in the enclosures because of its significant industrial applications.
Design/methodology/approach
Geometries of the enclosures have considerable influences on the heat transfer which will be important in energy consumption. The most useful geometries in engineering fields are treated in this literature, and their effects on the fluid flow and heat transfer are presented.
Findings
A great variety of geometries included with different physical and thermal boundary conditions, heat sources and fluid/nanofluid media are analyzed. Moreover, the results of different types of methods including experimental, analytical and numerical are obtained. Different natures of natural convection phenomenon including laminar, steady-state and transient, turbulent are covered. Overall, the present review enhances the insight of researchers into choosing the best geometry for thermal process.
Originality/value
A comprehensive review on the most practical geometries in the industrial application is performed.
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Mohsen Jami, Hamidreza Izadbakhsh and Alireza Arshadi Khamseh
This study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic…
Abstract
Purpose
This study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time.
Design/methodology/approach
This paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN.
Findings
The findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain.
Originality/value
The researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.
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Alireza Rahimi, Abbas Kasaeipoor, Emad Hasani Malekshah, Mohammad Mehdi Rashidi and Abimanyu Purusothaman
This study aims to investigate the three-dimensional natural convection and entropy generation in a cuboid enclosure filled with CuO-water nanofluid.
Abstract
Purpose
This study aims to investigate the three-dimensional natural convection and entropy generation in a cuboid enclosure filled with CuO-water nanofluid.
Design/methodology/approach
The lattice Boltzmann method is used to solve the problem numerically. Two different multiple relaxation time (MRT) models are used to solve the problem. The D3Q7–MRT model is used to solve the temperature field, and the D3Q19 is used to solve the fluid flow of natural convection within the enclosure.
Findings
The influences of different Rayleigh numbers (103 < Ra < 106) and solid volume fractions (0 < f < 0.04) on the fluid flow, heat transfer, total entropy generation, local heat transfer irreversibility and local fluid friction irreversibility are presented comprehensively. To predict thermo–physical properties, dynamic viscosity and thermal conductivity, of CuO–water nanofluid, the Koo–Kleinstreuer–Li (KKL) model is applied to consider the effect of Brownian motion on nanofluid properties.
Originality/value
The originality of this work is to analyze the three-dimensional natural convection and entropy generation using a new numerical approach of dual-MRT-based lattice Boltzmann method.
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Peiman Pilechiha, Alireza Norouziasas, Hoorieh Ghorbani Naeini and Kasmir Jolma
In vernacular buildings, many climatic and passive solutions have been used to create indoor thermal comfort. Seasonal occupant movement is an example of a traditional response to…
Abstract
Purpose
In vernacular buildings, many climatic and passive solutions have been used to create indoor thermal comfort. Seasonal occupant movement is an example of a traditional response to increasing thermal comfort. This article investigates the influence of these user behaviours on thermal comfort in courtyard houses.
Design/methodology/approach
Parametric models of three different scenarios of courtyard houses are simulated. The courtyard houses are located in Shiraz, Iran, and share the same orientation and construction materials. To enhance the accuracy of the study, the indoor adaptive thermal comfort (ATC) analysis is performed with three different window-to-wall ratios (WWR) of 25, 50 and 75%. The ACT analysis is performed on an hourly basis for summer and winter scenarios.
Findings
The results demonstrate that the indoor ATC is 8.3% higher in winter than in the summer in the seasonal zones. During the summer, the amount of ATC is relatively sustained in all zones. Unlike common beliefs, seasonal movement can enhance the ATC, especially during winter, specifically in the northern part of the courtyard. In northern zones, the seasonal movement of occupants improves the indoor ATC from 10.1 to 23.7%, and in southern zones, the improvement is from 2.2 to 4.8%.
Originality/value
This research presents a new numerical investigation into occupants' seasonal movements in courtyard houses during summer and winter. It provides a precise pattern to show how much this seasonal movement can affect the habitant's ATC.
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Alireza Abdolahi, Hossein Soroush and Saeed Khodaygan
Predicting dimensional and geometrical errors in 3D printing parts during the design stage can significantly enhance the product’s quality. This study aims to predict the form…
Abstract
Purpose
Predicting dimensional and geometrical errors in 3D printing parts during the design stage can significantly enhance the product’s quality. This study aims to predict the form deviation and process capability in additive manufacturing (AM) specimens considering layer thickness, laser power and scan speed parameters in the laser powder bed fusion (LPBF) method. Various machine learning (ML) techniques are implemented to estimate the form deviation and process capability with the highest accuracy in 3D-printed cylindrical parts as a case study.
Design/methodology/approach
The workflow started by simulating the LPBF AM process using a finite element modeling approach. Then, different ML algorithms like artificial neural networks are used to predict the form deviation. The process capability value is forecasted using some classification ML models and process capability indices (PCIs) for cylindrical parts. Finally, concentricity tolerance classification is performed for cylindrical parts, which can ensure quality control issues in the production stage.
Findings
Results present an accuracy of about 93% for predicting form deviations and 95% accuracy for predicting PCI C_pm in PCI classification based on random forest model as an ML algorithm.
Originality/value
The noteworthy point of the research is accessing the form deviation due to AM and process capability evaluation in the AM process before the production stage, which has not been studied before based on the author’s knowledge. So that the product quality is evaluated based on the shape deviation and its tolerances in the AM process digital chain.
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Alireza Khalili-Fard, Reza Tavakkoli-Moghaddam, Nasser Abdali, Mohammad Alipour-Vaezi and Ali Bozorgi-Amiri
In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal…
Abstract
Purpose
In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.
Design/methodology/approach
In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.
Findings
The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.
Originality/value
This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.
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Mahdi Zarepour, Niloufar Hojat Shemami and Soroush Avakh Darestani
In today’s world, one of the most important factors of the country’s economic development is improving the productivity of manufacturing industries. Identifying factors affecting…
Abstract
Purpose
In today’s world, one of the most important factors of the country’s economic development is improving the productivity of manufacturing industries. Identifying factors affecting the productivity of manufacturing industries and prioritizing them is effective in promoting productivity and can promise to achieve organizational and national productivity. The purpose of this research is to identify the effective factors in improving the productivity of manufacturing industries.
Design/methodology/approach
The present research method is a descriptive-survey and the data collection tool is a questionnaire. In the first step, according to the studies conducted by reviewing the research literature using a comparative method, library studies and asking opinions from experts, potential factors affecting the productivity of industries were identified and analyzed. Then the factors were divided into four main categories, and the selected factors were determined by using a questionnaire and combining the opinions of experts. Then, the importance of the selected factors was determined using the Fuzzy SWARA decision-making method, and the final ranking of the selected industries of the province was done using the MOORA method.
Findings
The results of this research showed that the factors “profit margin,” “The ratio of sales on current assets” and “The ratio of exports to sales,” respectively, have the highest importance, among the pharmaceutical and household appliances industries of the province that are present in the stock exchange, Caspian Tamin Company has the highest productivity with a productivity score of 0.437.
Originality/value
Looking at the background of the research, no comprehensive research has been conducted to identify indicators that affect productivity in the manufacturing industry, and only a few studies have evaluated productivity indicators for small, specialized industries. Therefore, in the current research, considering the uncertainty in experts' opinions, a hybrid model is presented to identify and comprehensively evaluate the productivity indicators of manufacturing industries using the decision-making method of Fuzzy SWARA and MOORA, which is unique in its turn.
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Seyed Alireza Athari, Uju Violet Alola and Andrew Adewale Alola
In this study, as part of an attempt to foster sustainable development, the aim is directed at understanding the perspectives of domestic economic, financial and political risks…
Abstract
Purpose
In this study, as part of an attempt to foster sustainable development, the aim is directed at understanding the perspectives of domestic economic, financial and political risks in tourism development. On the other hand, the role of other agents of sustainable development: innovation, infrastructure, health and primary education and global crisis in tourism development, was illustrated.
Design/methodology/approach
To achieve this objective, the current study explored the (system) SYS-Generalized Method of Moments (GMM) technique for a panel of selected 73 economies over the period 2006–2017. This GMM approached is not undertaken without first establishing the stationarity (a preliminary test) of the employed dataset by utilizing the relevant unit root techniques.
Findings
First, the study found that minimizing risks from economic, financial and political aspects is significant and vital to the attractiveness of the tourism destinations and the eventual development of the tourism sector. Second, the study presents innovation or technological readiness and health and primary education as agents of sustainable development through the growth of international tourism arrivals while global crisis is significantly detrimental to tourism inflow.
Originality/value
Overall, the study presents the contribution of tourism as a pathway to sustainable development from unique dimensions. Investigating a large panel (of 73 countries) is a unique approach. In addition, considering the economic vulnerability of the panel countries from the aspects of risk arising from economic, financial and political aspects is another interesting dimension to the novelty of the study. Thus, this study offers relevant policies for tourism stakeholders.