Toward cognitive predictive maintenance: A survey of graph-based approaches

Xia, Liqiao; Zheng, Pai; Li, Xinyu; Gao, Robert X.; Wang, Lihui

doi:10.1016/j.jmsy.2022.06.002

Cited by 85 publications

(19 citation statements)

References 102 publications

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“…There will be other dimensions affecting the service quality, passengers' satisfaction, and their loyalty to the airlines that are worth further study in the future. The predictive maintenance and preventive maintenance assisted with multiple Internet-of-Things and cloud-based scheduling system could be further considered as one of the paramters affected the service quality (Fan et al, 2022;Li, Feng, Guo, Wang, Li, Liu et al, 2020;Li, Ng, Fan, Yuan, Liu & Bu, 2021;Xia et al, 2021;Xia, Zheng, Li, Gao & Wang, 2022;Zheng et al, 2021). The adoption of a smart product service system could also affected the service quality in future (Fan et al, 2022;; Wang, Chen, Zheng, Li & Khoo, 2019Zhang, Ye, Peng, Peng, Tang & Xiang, 2020;Zheng, Lin, Chen & Xu, 2018;Zheng, Wang, Sang, Zhong, Liu, Liu et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

How do full-service carriers and low-cost carriers passengers perceived service dimensions, passengers’ satisfaction, and loyalty differently? An empirical study

Chow

Li²,

Mak³

et al. 2022

JIEM

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Purpose: In this study, group differences between full-service carriers (FSC) and low-cost carriers (LCC) in loyalty constructs are investigated, revealing the relationship between service quality and loyalty. This work focuses on five dimensions, including tangibility, empathy, assurance, responsiveness, and reliability, constitute service quality. Design/methodology/approach: 248 questionnaires were collected in the first half of 2019. The antecedents of customer loyalty are explored, and the group differences between FSC and LCC are analyzed. For assessing the path model with the consideration of group variance, the Partial Least Squares Multiple Group Analysis (PLS-MGA) was adopted to analyze the differences of the estimated inter-group coefficient.Findings: Our findings suggest that service assurance, service empathy, and service reliability positively impact the value perceived. The impact of service empathy on customer satisfaction in FSC is significantly diverse from LCC. Several suggestions are provided to FSC and LCC on improving their services in view of passengers’ wants and interests.Originality/value: With the data collected at the HKIA, this study examined the relationships among service quality, perceived value, customer satisfaction, and customer loyalty and divided service quality into five dimensions. The findings show that assurance, empathy, and reliability of service quality positively affect the value perceived, and the effects of responsiveness and tangibility of service quality on perceived value are insignificant. Among the five aspects of service quality, assurance, reliability, responsiveness, and tangibility of the service quality are the pre-conditions of customer satisfaction. However, only the reliability of service is the antecedent of customer loyalty. Besides, the value perceived positively affects customers to be satisfactory and loyal. Furthermore, satisfaction degree also significantly influences the degree of customers’ loyalty. As to the role of airline types, the sole effect is on customers' satisfaction is service empathy, with a significant difference between FSC and LCC.

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Section: Discussionmentioning

confidence: 99%

How do full-service carriers and low-cost carriers passengers perceived service dimensions, passengers’ satisfaction, and loyalty differently? An empirical study

Chow

Li²,

Mak³

et al. 2022

JIEM

View full text Add to dashboard Cite

show abstract

“…Various XAI methods are summarized in [68] that could be leveraged for PdM in the SME domain. Similarly Graph based approaches could be leveraged in PdM as described in the survey [69], e.g., Knowledge graph [70] and virtual Graph [71] would be useful from an SME perspective to work with limited computing resources. Another state-of-the-art software architecture for Human-AI teaming [72] for smart factories could also be handy for adopting Human-Center AI-based PdM methods in SMEs.…”

Section: Best Practices (Rq4)mentioning

confidence: 99%

A Systematic Mapping Study of Predictive Maintenance in SMEs

et al. 2022

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The rapid growth of Industry 4.0 and predictive methods fostered a great potential for state-of-the-art techniques in the industrial sector, especially in smart factories. The equipment failure or system breakdowns during run time of a factory creates a severe problems towards impoverishment of the production system and destitution of the business. Predictive Maintenance (PdM) is a cost-saving and data driven technique to predict the maintenance time of in-service equipment or systems to reduce breakdown time and increase productivity. Although PdM is pragmatically adopted in large-scale industries, there is a lack of studies that map the PdM adoption in small and medium-sized enterprises (SMEs). In this systematic mapping study (SMS), we focus on predictive maintenance from an SME perspective to explore the field for researchers, scientists, and developers to comprehend the potential of PdM systems, their challenges, distinctive characteristics, and best practices in SMEs. Our study is based on four research questions comprised of demographic data, key challenges, distinctive characteristics, and best practices of predictive maintenance in SMEs. We found that the current literature on PdM is deficient in the SME domain, especially the financial side is vague. There is a huge potential for PdM in SMEs to design cost models and focus on data availability impediments. Management and monitoring of PdM and skilled personnel are also inadequate. Thus, we present a study that extracts the knowledge from the existing literature about PdM in SMEs, finds the research gap, and can assist in identifying the barriers and challenges of PdM adoption in SMEs.

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“…Meanwhile, data-driven models from multiple participants can combine for ensemble learning [28]. Besides, in a multiagent system, local models can merge into a global optimal model through reinforcement learning [29] or knowledge graph [30][31][32][33]. Moreover, Wang et al [34] applied a blockchain in collaborative fault diagnosis, where the blockchain provided a decentralised platform and claimed the immutable ownership of data and knowledge of each participant.…”

Section: Collaborative Fault Diagnosismentioning

confidence: 99%

“…Meanwhile, data‐driven models from multiple participants can combine for ensemble learning [28]. Besides, in a multi‐agent system, local models can merge into a global optimal model through reinforcement learning [29] or knowledge graph [30–33]. Moreover, Wang et al.…”

Section: Related Workmentioning

confidence: 99%

Privacy‐preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

Xia

Zheng

et al. 2022

IET Collab Intel Manufact

Self Cite

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Data-driven fault diagnosis approaches have been widely adopted due to their persuasive performance. However, data are always insufficient to develop effective fault diagnosis models in real manufacturing scenarios. Despite numerous approaches that have been offered to mitigate the negative effects of insufficient data, the most challenging issue lies in how to break down the data silos to enlarge data volume while preserving data privacy. To address this issue, a vertical federated learning (FL) model, privacy-preserving boosting tree, has been developed for collaborative fault diagnosis of industrial practitioners while maintaining anonymity. Only the model information will be shared under the homomorphic encryption protocol, safeguarding data privacy while retaining high accuracy. Besides, an Autoencoder model is provided to encourage practitioners to contribute and then improve model performance. Two bearing fault case studies are conducted to demonstrate the superiority of the proposed approach by comparing it with typical scenarios. This present study's findings offer industrial practitioners insights into investigating the vertical FL in fault diagnosis.

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Toward cognitive predictive maintenance: A survey of graph-based approaches

Cited by 85 publications

References 102 publications

How do full-service carriers and low-cost carriers passengers perceived service dimensions, passengers’ satisfaction, and loyalty differently? An empirical study

How do full-service carriers and low-cost carriers passengers perceived service dimensions, passengers’ satisfaction, and loyalty differently? An empirical study

A Systematic Mapping Study of Predictive Maintenance in SMEs

Privacy‐preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

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