Data Analytics Towards Predictive Maintenance for Industrial Ovens

Rousopoulou, Vaia; Nizamis, Alexandros; Giugliano, Luigi; Haigh, Peter; Martins, L. Sousa; Ioannidis, Dimosthenis; Tzovaras, Dimitrios

doi:10.1007/978-3-030-20948-3_8

Cited by 5 publications

(1 citation statement)

References 10 publications

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“…Predictive Analytics are the most used techniques in the practical applications obtained for this SLR. For instance, some studies perform classification techniques (Q. Cao et al, 2020; Kiangala & Wang, 2018; Li, Huang, et al, 2017; Miškuf & Zolotová, 2016; Sellami et al, 2019), while other used regression techniques (Calabrese et al, 2020; Charest et al, 2018; Peralta et al, 2017; Rousopoulou et al, 2019). Simple neural networks are used in several research works such as (Cisotto & Herzallah, 2018; Kabugo et al, 2020; Miškuf & Zolotová, 2016; Soto et al, 2019; Spendla et al, 2017).…”

Section: Literature Review Analysismentioning

confidence: 99%

Business analytics in Industry 4.0: A systematic review

Silva

Cortez

Pereira³

et al. 2021

Expert Systems

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Recently, the term "Industry 4.0" has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modeling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on this Business Analytics usage, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analyzed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modeling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.

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Section: Literature Review Analysismentioning

confidence: 99%

Business analytics in Industry 4.0: A systematic review

Silva

Cortez

Pereira³

et al. 2021

Expert Systems

View full text Add to dashboard Cite

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Application of a Visual and Data Analytics Platform for Industry 4.0 Enabled by the Interoperable Data Spine: A Real-World Paradigm for Anomaly Detection in the Furniture Domain

Nizamis,

Deshmukh,

Vafeiadis

et al. 2024

Proceedings of the I-Esa Conferences

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Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

2022

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When put into practice in the real world, predictive maintenance presents a set of challenges for fault detection and prognosis that are often overlooked in studies validated with data from controlled experiments, or numeric simulations. For this reason, this study aims to review the recent advancements in mechanical fault diagnosis and fault prognosis in the manufacturing industry using machine learning methods. For this systematic review, we searched Web of Science, ACM Digital Library, Science Direct, Wiley Online Library, and IEEE Xplore between January 2015 and October 2021. Full-length studies that employed machine learning algorithms to perform mechanical fault detection or fault prognosis in manufacturing equipment and presented empirical results obtained from industrial case-studies were included, except for studies not written in English or published in sources other than peer-reviewed journals with JCR Impact Factor, conference proceedings and book chapters/sections. Of 4549 records, 44 primary studies were selected. In 37 of those studies, fault diagnosis and prognosis were performed using artificial neural networks (n=12), decision tree methods (n=11), hybrid models (n=8), or latent variable models (n=6), with one of the studies employing two different types of techniques independently. The remaining studies employed a variety of machine learning techniques, ranging from rule-based models to partition-based algorithms, and only two studies approached the problem using online learning methods. The main advantages of these algorithms include high performance, the ability to uncover complex nonlinear relationships and computational efficiency, while the most important limitation is the reduction in model performance in the presence of concept drift. This review shows that, although the number of studies performed in the manufacturing industry has been increasing in recent years, additional research is necessary to address the challenges presented by real-world scenarios.

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Data Analytics Towards Predictive Maintenance for Industrial Ovens

Cited by 5 publications

References 10 publications

Business analytics in Industry 4.0: A systematic review

Business analytics in Industry 4.0: A systematic review

Application of a Visual and Data Analytics Platform for Industry 4.0 Enabled by the Interoperable Data Spine: A Real-World Paradigm for Anomaly Detection in the Furniture Domain

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

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