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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Arunkumar O.N., Divya D. and Jikku Susan Kurian
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Originality/value
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.
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The primary objective of this research paper is to investigate the fundamental dimensions of conflict management, namely cooperation (concern for self and others) and competition…
Abstract
Purpose
The primary objective of this research paper is to investigate the fundamental dimensions of conflict management, namely cooperation (concern for self and others) and competition (concern only for self), and to establish a theoretical connection between these dimensions and reflective and intuitive thinking. Drawing upon dual process theory and Deutsch’s conceptualization of cooperation and competition, the study delves into the systematic impact of intuition and reflection on individuals’ preferred conflict management styles.
Design/methodology/approach
Theoretically driven hypotheses established links between reflective and intuitive thinking and cooperative and competitive conflict management styles. Two studies were conducted to empirically validate these hypotheses, designed to scrutinize the influence of intuition and reflection on individuals’ inclinations toward competitive or cooperative conflict management styles. Study 1 was based on self-reported measures, and Study 2 was an experimental design method.
Findings
The study outcomes affirm the hypotheses, revealing that reflective thinking aligns with a preference for cooperation, whereas intuitive thinking corresponds to a preference for competition.
Research limitations/implications
Recognizing the significance of cognitive styles in shaping preferences for competitive and cooperative conflict management, this research offers valuable insights for both parties involved, leading to more favorable outcomes and providing practical guidance for conflict management practitioners. The paper concludes by discussing implications and acknowledging limitations.
Originality/value
This exploration represents a novel avenue in the conflict management research domain, shedding light on the antecedents of thinking styles in the context of conflict resolution.
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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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Originality/value
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.
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Piyali Ghosh, Alka Rai, Ragini Chauhan, Gargi Baranwal and Divya Srivastava
The purpose of this study is to examine the potential mediating role of employee engagement between rewards and recognition and normative commitment.
Abstract
Purpose
The purpose of this study is to examine the potential mediating role of employee engagement between rewards and recognition and normative commitment.
Design/methodology/approach
Responses of a sample of 176 private bank employees in India were used to examine the proposed mediated model.
Findings
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.
Originality/value
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.
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Dominik Rozkrut, Malgorzata Tarczynska-Luniewska, Guru Asish Singh and Mateusz Piwowarski
Purpose: Sustainable and responsible business is strongly associated with activities that minimise negative environmental or social impacts. As a result, the utility of big data…
Abstract
Purpose: Sustainable and responsible business is strongly associated with activities that minimise negative environmental or social impacts. As a result, the utility of big data is becoming a reality, opening up exciting possibilities for ESG monitoring and assessment. This study systematises existing knowledge and provides recommendations for big data in ESG monitoring and assessment.
Methodology/approach: Theoretical and exploratory focusing on a literature review.
Conclusions: Results indicate different levels of progress and challenges related to ESG and big data. Awareness and adoption of ESG and big data practices is growing, accompanied by regulatory pressure.
Significance: Understanding the relationship between big data and ESG is critical to properly conducting sustainable and responsible business practices. The urgency and necessity of developing standards for constructing big data cannot be overstated for ensuring consistency between existing policies and the SDGs and for the effective use of big data in ESG monitoring and assessment.
Limitations: A lack of data quality and standardisation in reporting for ESG assessments. Standardisation efforts are growing as data challenges, especially data availability, are major constraints. Large data sets offer exciting opportunities, analysed mainly from the perspective of existing applications for measuring sustainability goals.
Future research: An in-depth analysis of case studies that combine ESG issues with big data infrastructure. Fundamental is knowledge and understanding of companies’ ESG practices and understanding big data issues. We can standardise approaches to using new data sources and move towards deepening our measurable dimension of sustainability assessment.
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Klaus Jürgen Folz and Herbert Martins Gomes
The objective of this article is to evaluate and compare the performance of two machine learning (ML) algorithms, i.e. support vector machines (SVMs) and random forests (RFs)…
Abstract
Purpose
The objective of this article is to evaluate and compare the performance of two machine learning (ML) algorithms, i.e. support vector machines (SVMs) and random forests (RFs), when classifying seven states of operation of an electric motor using the Mel-frequency cepstral coefficients (MFCCs) as extracted representative features.
Design/methodology/approach
The extracted MFCCs are calculated using the motor’s vibration and audio signals separately.
Findings
After the training, the SVM model obtained a mean accuracy of 100% for the MFCCs obtained from database vibration signals and 69.6% for the database of audio signals.
Research limitations/implications
The ML strategies and results reported are limited to the well-known data for industrial electric motors used in the evaluations, although it was performed tests and cross-validations with unseen data and the information from the confusion matrix.
Practical implications
The success of these methodologies in defect classification, where the RF presented a mean accuracy of 99.15% for the vibration signals and 63.82% for the audio signal, enables the use of this ML and extracted features as a predictive tool for failure and anomaly detection, lifetime predictions and online real-time monitoring.
Originality/value
It is the first time that the MFCCs are being used for anomaly detection in vibration and audio signals for electrical motors, as this extracted feature is usually used for human speech identification in the literature.
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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.