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Review
. 2021 Feb 20;21(4):1470.
doi: 10.3390/s21041470.

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

Martin Pech  1 , Jaroslav Vrchota  1 , Jiří Bednář  1
Affiliations
Review

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

Martin Pech et al. Sensors (Basel). .

Abstract

With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems' decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper's main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories.

Keywords: Industry 4.0; intelligent sensors; maintenance; smart factory.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Organisation of the article.
Figure 2
Figure 2
Experience- and data-driven predictive maintenance [38].
Figure 3
Figure 3
Research design and procedure.
Figure 4
Figure 4
Flow diagram based on PRISMA [48] and QUORUM [49] flowchart.
Figure 5
Figure 5
The development of the total number of Scopus and Web of Science publications. Note: square on the line (Web of Science), circle on the bold line (Scopus).
Figure 6
Figure 6
The summary of burst detection analysis for main topics. The results are divided into three periods of time (1970–1990, 1990–2010, and 2010–2020). The terms in each period are sorted according to the burst weights.
Figure 7
Figure 7
The top terms in analysed areas based on the burst detection. Note: The top ten used terms are highlighted “bold” and top twenty terms are depicted “italic”.
Figure 8
Figure 8
Keywords of co-occurrence analysis.
Figure 9
Figure 9
Analysis of keywords related to Industry 4.0 technologies.
Figure 10
Figure 10
Main characteristics of intelligent sensors.
Figure 11
Figure 11
Analysis of keywords “smart” and “intelligent”.
See this image and copyright information in PMC

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