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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.

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.

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.

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.

The purpose of this paper is to understand the dark side of blockchain technology (BCT) adoption in small and mid-size enterprises. The focus of the authors is to decode the intricate relationship among the selected variables missing in the existing literature.

A focused group approach is initiated by the authors to identify the barriers. Total interpretive structural modeling, Matrice d'impacts croisés multiplication appliquée á un classment, that is, matrix multiplication applied to classification and decision-making trial and evaluation laboratory are used to analyze the complex relationships among identified barriers.

This study finds that implementation of BCT reduces maintenance cost by withdrawing manual effort, as BCT has better capability to quantify the internal status of the system (observability characteristic). The observability characteristic of BCT provides high compatibility to the system. This study also finds that the compatibility of BCT with the organization reduces implementation cost and facilitates project management. The findings of this study recommend analyzing maintenance cost and compatibility of BCT before implementing it. Small and mid-size enterprises can select complex BCT depending on the sophistication level of IT usage and IT project management capabilities.

This study comes with various limitations, where the model developed by the authors may not be conclusive, as it is based exclusively on expert opinion. The samples collected may not help in validating the model statistically. Though the model has its limitations, it can still be considered as a nascent initiative for further investigation using structural equation modeling.

The outcomes of the theoretical and managerial contributions of the study can be categorized into three levels. This study can be used both by the industrialists and researchers to understand the barriers and the recovery methods thereafter. Suggestions that serve as future directives are also discussed by the authors.

This study aims to investigate the healthcare sector of the United Arab Emirates (UAE) to explore the significance of servant leadership and collaborative culture in fostering social sustainability. The primary objective of this paper is to investigate how servant leadership and a collaborative culture contribute to social sustainability in health care in the UAE. With a focus on promoting well-being within healthcare organizations, the paper aims to uncover the synergies between servant leadership, collaborative culture, and social sustainability.

This paper conducted a multilayer literature review of existing literature on servant leadership, collaborative culture and social sustainability in health care, both globally and specifically in the UAE context, and a conceptual model was proposed.

Servant leadership proves to be a culturally pertinent and effective leadership model within the UAE due to its alignment with cultural values, emphasis on community support, and the robust health-care system that contributes to individual well-being. This combination establishes a solid foundation for fostering a healthy and sustainable society.

Limitations and implications are discussed. The current research has not identified the boundary conditions under which servant leadership and collaborative culture may be more or less effective. This could involve exploring industry-specific influences or contextual factors. Theoretical and practical implications are discussed.

The research seeks to unravel the interconnections between servant leadership, collaborative culture and social sustainability. To the best of the author’s knowledge, none of the studies have explored the interrelationships of these constructs, particularly in the UAE context.

The purpose of this study is to examine the potential mediating role of employee engagement between rewards and recognition and normative commitment.

Responses of a sample of 176 private bank employees in India were used to examine the proposed mediated model.

The variable rewards and recognition is found to be significantly correlated to both employee engagement and normative commitment. Results of regression have been analyzed in line with the four conditions of mediation laid down by Baron and Kenny (1986). Further, SPSS macro developed by Preacher and Hayes (2004) is used to test the proposed mediation model. The relationship between rewards and recognition and normative commitment is found to become smaller after controlling the variable employee engagement. The results provide partial support to the mediation hypothesis.

Normative commitment has been less researched relative to the attention paid to affective commitment. Further, no research has yet focused on the impact of rewards and recognition on normative commitment, with the mediating impact of employee engagement. This study hence provides the first empirical test of the established relationship between rewards and recognition and employee engagement by introducing normative commitment as an outcome variable.

This research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.

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.

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%.

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.

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.

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.

Lubricating oil leakage is a common issue in thermal power plant operation sites, requiring prompt equipment maintenance. The real-time detection of leakage occurrences could avoid disruptive consequences caused by the lack of timely maintenance. Currently, inspection operations are mostly carried out manually, resulting in time-consuming processes prone to health and safety hazards. To overcome such issues, this paper proposes a machine vision-based inspection system aimed at automating the oil leakage detection for improving the maintenance procedures.

The approach aims at developing a novel modular-structured automatic inspection system. The image acquisition module collects digital images along a predefined inspection path using a dual-light (i.e. ultraviolet and blue light) illumination system, deploying the fluorescence of the lubricating oil while suppressing unwanted background noise. The image processing module is designed to detect the oil leakage within the digital images minimizing detection errors. A case study is reported to validate the industrial suitability of the proposed inspection system.

On-site experimental results demonstrate the capabilities to complete the automatic inspection procedures of the tested industrial equipment by achieving an oil leakage detection accuracy up to 99.13%.

The proposed inspection system can be adopted in industrial context to detect lubricant leakage ensuring the equipment and the operators safety.

The proposed inspection system adopts a computer vision approach, which deploys the combination of two separate sources of light, to boost the detection capabilities, enabling the application for a variety of particularly hard-to-inspect industrial contexts.

This study aims to characterize the properties of natural cellulose fiber from the pseudo-stems of the curcuma zedoaria plant.

The fiber was extracted using the biological retting process (cold-water retting). The intrinsic fiber properties obtained were used to evaluate the possibility of using fiber for textile applications.

The average length of a curcuma zedoaria fiber was 34.77 cm with a fineness value of 6.72 Tex. A bundle of curcuma zedoaria fibers was comprised of many elementary fibers. Curcuma zedoaria had an irregular cross-section, with the lumen having a varied oval shape. Curcuma zedoaria fibers had tenacity and elongation value of 3.32 gf/denier and 6.95%, respectively. Curcuma zedoaria fibers had a coefficient of friction value of 0.46. Curcuma zedoaria fibers belong to a hygroscopic fiber type with a moisture regain value of 10.29%.

Extraction and Characterization of Curcuma zedoaria Pseudo-stems Fibers for Textile Application.