D. Divya, Bhasi Marath and M.B. Santosh Kumar
This study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive…
Abstract
Purpose
This study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive maintenance. Opportunities and challenges in developing anomaly detection algorithms for predictive maintenance and unexplored areas in this context are also discussed.
Design/methodology/approach
For conducting a systematic review on the state-of-the-art algorithms in fault detection for predictive maintenance, review papers from the years 2017–2021 available in the Scopus database were selected. A total of 93 papers were chosen. They are classified under electrical and electronics, civil and constructions, automobile, production and mechanical. In addition to this, the paper provides a detailed discussion of various fault-detection algorithms that can be categorised under supervised, semi-supervised, unsupervised learning and traditional statistical method along with an analysis of various forms of anomalies prevalent across different sectors of industry.
Findings
Based on the literature reviewed, seven propositions with a focus on the following areas are presented: need for a uniform framework while scaling the number of sensors; the need for identification of erroneous parameters; why there is a need for new algorithms based on unsupervised and semi-supervised learning; the importance of ensemble learning and data fusion algorithms; the necessity of automatic fault diagnostic systems; concerns about multiple fault detection; and cost-effective fault detection. These propositions shed light on the unsolved issues of predictive maintenance using fault detection algorithms. A novel architecture based on the methodologies and propositions gives more clarity for the reader to further explore in this area.
Originality/value
Papers for this study were selected from the Scopus database for predictive maintenance in the field of fault detection. Review papers published in this area deal only with methods used to detect anomalies, whereas this paper attempts to establish a link between different industrial domains and the methods used in each industry that uses fault detection for predictive maintenance.
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Hossein Derakhshanfar, J. Jorge Ochoa, Konstantinos Kirytopoulos, Wolfgang Mayer and Craig Langston
The purpose of this research is to identify the most impactful delay risks in Australian construction projects, including the associations amongst those risks as well as the…
Abstract
Purpose
The purpose of this research is to identify the most impactful delay risks in Australian construction projects, including the associations amongst those risks as well as the project phases in which they are most likely present. The correlation between project and organisational characteristics with the impact of delay risks was also studied.
Design/methodology/approach
A questionnaire survey was used to collect data from 118 delayed construction projects in Australia. Data were analysed to rank the most impactful delay risks, their correlation to project and organisational characteristics and project phases where those risks are likely to emerge. Association rule learning was used to capture associations between the delay risks.
Findings
The top five most impactful delay risks in Australia were changes by the owner, slow decisions by the owner, preparation and approval of design drawings, underestimation of project complexity and unrealistic duration imposed to the project, respectively. There is a set of delay risks that are mutually associated with project complexity. In addition, while delay risks associated with resources most likely arise in the execution phase, stakeholder and process-related risks are more smoothly distributed along all the project phases.
Originality/value
This research for the first time investigated the impact of delay risks, associations amongst them and project phases in which they are likely to happen in the Australian context. Also, this research for the first time sheds light on the project phases for the individual project delay risks which aids the project managers to understand where to focus on during each phase of the project.
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Hossein Derakhshanfar, J. Jorge Ochoa, Konstantinos Kirytopoulos, Wolfgang Mayer and Vivian W.Y. Tam
The purpose of this paper is to systematically develop a delay risk terminology and taxonomy. This research also explores two external and internal dimensions of the taxonomy to…
Abstract
Purpose
The purpose of this paper is to systematically develop a delay risk terminology and taxonomy. This research also explores two external and internal dimensions of the taxonomy to determine how much the taxonomy as a whole or combinations of its elements are generalisable.
Design/methodology/approach
Using mixed methods research, this systematic literature review incorporated data from 46 articles to establish delay risk terminology and taxonomy. Qualitative data of the top 10 delay risks identified in each article were coded based on the grounded theory and constant comparative analysis using a three-stage coding approach. Word frequency analysis and cross-tabulation were used to develop the terminology and taxonomy. Association rules within the taxonomy were also explored to define risk paths and to unmask associations among the risks.
Findings
In total, 26 delay risks were identified and grouped into ten categories to form the risk breakdown structure. The universal delay risks and other delay risks that are more or less depending on the project location were determined. Also, it is realized that delays connected to equipment, sub-contractors and design drawings are highly connected to project planning, finance and owner slow decision making, respectively.
Originality/value
The established terminology and taxonomy may be used in manual or automated risk management systems as a baseline for delay risk identification, management and communication. In addition, the association rules assist the risk management process by enabling mitigation of a combination of risks together.
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Sunil Kumar Tiwari, Sarang Pande, Santosh M. Bobade and Santosh Kumar
The purpose of this paper is to propose and develop PA2200-based composite powder containing 0-15 Wt.% magnesium oxide before directly using it in selective laser sintering (SLS…
Abstract
Purpose
The purpose of this paper is to propose and develop PA2200-based composite powder containing 0-15 Wt.% magnesium oxide before directly using it in selective laser sintering (SLS) machine to produce end-use products for low-volume production in the engineering applications with keen focus to meet the functional requirements which rely on material properties.
Design/methodology/approach
The methodology reported emphasises PA2200-based composite powder containing 0-15 Wt.% magnesium oxide development for SLS process which starts with preparation and characterisation of composite material, thermal and rheological study of composite material to decide optimum process parameters for SLS process machine to get optimal part properties. Further, to verify composite material properties, a conventional casting methodology is used. The composition of composite materials those possessing good properties are further selected for processing in SLS process under optimal processing parameters.
Findings
The process parameters of SLS machine are material-dependent. The effect of temperature in X-ray diffraction profile is negligible in the case of magnesium oxide reinforced PA2200 composite material. The cyclic heating of material increases melting point temperature, this grounds to modify part bed temperature of material every time before processing on SLS machine to uphold build part properties, as well as material. With the rise in temperature, the Melt flow index and rheological property of materials change. The magnesium oxide reinforced PA2200 composite material has high thermal stability than pure PA2200 material. By the addition of small quantity of magnesium oxide, most of the mechanical property and flammability property improves while elongation at break (percentage) decreases significantly.
Practical implications
The proposed PA2200-based composite powder containing 0-15 Wt.% magnesium oxide material development system and casting metrology to verify developed material properties will be very useful to develop new composite material for SLS process with use of less material. The developed methodology has proven, especially in the case where non-experts or student need to develop composite material for SLS process according to the property requirement of applications.
Originality/value
Unlike earlier composite material development methodology, the projected methodology of polymer-based composite material and confirmation of material properties instead of commencing SLS process provides straight forward means for SLS process composite materials development with less use of the material and period of time.
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Pradeep Kumar Tarei and Santosh Kumar
This paper proposes a decision-making framework for assessing various dimensions and barriers that have affected the admission process in management educational institutions…
Abstract
Purpose
This paper proposes a decision-making framework for assessing various dimensions and barriers that have affected the admission process in management educational institutions during the ongoing pandemic. The framework considers the interrelationship between the obstacles and highlights the importance of each barrier.
Design/methodology/approach
An integrated method based on decision-making trial and evaluation laboratory and analytical network process is proposed to structure the barrier assessment framework. Results obtained from the study are validated by comparing them against the conventional analytical hierarchy process.
Findings
The results obtained from this study indicate four significant dimensions that hinder admission in Indian management institutes, namely, governmental, financial, sectoral, institutional and market. The top five barriers are demand shift towards technical (alternative) skills, acceptance of the graduated students, lack of industry–institute collaboration, lack of long-term vision and opening new Indian Institute of Technologies (IITs) and Indian Institute of Managements (IIMs).
Research limitations/implications
During this ongoing pandemic, many educational institutes have been forced to shift from the traditional classroom to a virtual teaching model. In this regard, this study helps identify and assess the barriers to admission in Indian management institutes during this epidemic and thus, contribute to the literature. The findings will assist all stakeholders and policymakers of management institutions design and develop appropriate managerial strategies. The study is conducted in the Indian management educational institute context and can be extended to technical education institutions for deeper insights.
Originality/value
The paper develops an assessment framework for analysing the barriers to admission in Indian management institutes during the ongoing COVID-19 pandemic. Research implications are discussed in the context of a developing country.
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Anup Kumar, Santosh Shrivastav, Amit Adlakha and Niraj K. Vishwakarma
The authors develop a methodology to select appropriate sustainable supply chain indicators (SSCIs) to measure Sustainable Development Goals (SDGs) in the global supply chain.
Abstract
Purpose
The authors develop a methodology to select appropriate sustainable supply chain indicators (SSCIs) to measure Sustainable Development Goals (SDGs) in the global supply chain.
Design/methodology/approach
SSCIs are identified by reviewing the extant literature and topic modeling. Further, they are evaluated based on existing SDGs and ranked using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. Notably, the evaluation of indicators is a multi-criteria decision-making (MCDM) process within a fuzzy environment. The methodology has been explained using a case study from the automobile industry.
Findings
The case study identifies appropriate SSCIs and differentiates them among peer suppliers for gaining a competitive advantage. The results reveal that top-ranked sustainability indicators include the management of natural resources, energy, greenhouse gas (GHG) emissions and social investment.
Practical implications
The study outcome will enable suppliers, specialists and decision makers to understand the criteria that improve supply chain sustainability in the automobile industry. The analysis provides a comprehensive understanding of the competitive package of indicators for gaining strategic advantage. This proactive sustainability indicator selection promotes and enhances sustainability reporting while fulfilling regulatory requirements and increasing collaboration potential with trustworthy downstream partners. This study sets the stage for further research in SSCIs’ competitive strategy in the automobile industry along with its supply chains.
Originality/value
This study is unique as it provides a framework for determining relevant SSCIs, which can be distinguished from peer suppliers, while also matching economic, environmental and social metrics to achieve a competitive advantage.
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Santosh Chaudhary and Mohan Kumar Choudhary
The purpose of this paper is to investigate two-dimensional viscous incompressible magnetohydrodynamic boundary layer flow and heat transfer of an electrically conducting fluid…
Abstract
Purpose
The purpose of this paper is to investigate two-dimensional viscous incompressible magnetohydrodynamic boundary layer flow and heat transfer of an electrically conducting fluid over a continuous moving flat surface considering the viscous dissipation and Joule heating.
Design/methodology/approach
Suitable similarity variables are introduced to reduce the governing nonlinear boundary layer partial differential equations to ordinary differential equations. A numerical solution of the resulting two-point boundary value problem is carried out by using the finite element method with the help of Gauss elimination technique.
Findings
A comparison of obtained results is made with the previous work under the limiting cases. Behavior of flow and thermal fields against various governing parameters like mass transfer parameter, moving flat surface parameter, magnetic parameter, Prandtl number and Eckert number are analyzed and demonstrated graphically. Moreover, shear stress and heat flux at the moving surface for various values of the physical parameters are presented numerically in tabular form and discussed in detail.
Originality/value
The work is relatively original, as very little work has been reported on magnetohydrodynamic flow and heat transfer over a continuous moving flat surface. Viscous dissipation and Joule heating are neglected in most of the previous studies. The numerical method applied to solve governing equations is finite element method which is new and efficient.
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Sunil Kumar Yadav, Shiwangi Singh and Santosh Kumar Prusty
This study develops a model for understanding the relationships and interactions between the antecedents influencing inter-organisational collaboration in the healthcare sector.
Abstract
Purpose
This study develops a model for understanding the relationships and interactions between the antecedents influencing inter-organisational collaboration in the healthcare sector.
Design/methodology/approach
A comprehensive and systematized search was conducted on Scopus to identify all relevant studies investigating the antecedents of inter-organisational collaboration in the healthcare sector. Antecedents were identified based on insights from experts and a systematised search method. A modified total interpretive structural model (m-TISM) was used to determine the hierarchical relationships between the identified antecedents. Finally, the Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis was employed to categorise the identified antecedents into clusters based on their driving or dependence influence.
Findings
Eight antecedents of inter-organisational collaboration in the healthcare sector were identified. The results revealed that having a shared vision and goals, digital infrastructure and proximity are the most crucial antecedents of inter-organisational collaboration in healthcare, along with leadership, shared resources and communication.
Research limitations/implications
Future research on inter-organisational collaboration in the healthcare sector can include additional factors that may influence collaboration beyond those currently studied. Structural equation modelling can be employed to validate the proposed model.
Originality/value
The study proposes a hierarchical model for inter-organisational collaboration in the healthcare sector. The framework will help healthcare executives and academicians identify key antecedents that are most critical to enabling effective collaboration.
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Ahmed M. Attia, Ahmad O. Alatwi, Ahmad Al Hanbali and Omar G. Alsawafy
This research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.
Abstract
Purpose
This research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.
Design/methodology/approach
A mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy conditions.
Findings
It was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%.
Research limitations/implications
The proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop scheduling.
Practical implications
Maintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 footprint.
Originality/value
This work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.
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This study aims to introduce an innovative approach to predictive maintenance by integrating time-series sensor data with event logs, leveraging the synergistic potential of deep…
Abstract
Purpose
This study aims to introduce an innovative approach to predictive maintenance by integrating time-series sensor data with event logs, leveraging the synergistic potential of deep learning models. The primary goal is to enhance the accuracy of equipment failure predictions, thereby minimizing operational downtime.
Design/methodology/approach
The methodology uses a dual-model architecture, combining the patch time series transformer (PatchTST) model for analyzing time-series sensor data and bidirectional encoder representations from transformers for processing textual event log data. Two distinct fusion strategies, namely, early and late fusion, are explored to integrate these data sources effectively. The early fusion approach merges data at the initial stages of processing, while late fusion combines model outputs toward the end. This research conducts thorough experiments using real-world data from wind turbines to validate the approach.
Findings
The results demonstrate a significant improvement in fault prediction accuracy, with early fusion strategies outperforming traditional methods by 2.6% to 16.9%. Late fusion strategies, while more stable, underscore the benefit of integrating diverse data types for predictive maintenance. The study provides empirical evidence of the superiority of the fusion-based methodology over singular data source approaches.
Originality/value
This research is distinguished by its novel fusion-based approach to predictive maintenance, marking a departure from conventional single-source data analysis methods. By incorporating both time-series sensor data and textual event logs, the study unveils a comprehensive and effective strategy for fault prediction, paving the way for future advancements in the field.