Predictive Maintenance of Air Booster Compressor (ABC) Motor Failure using Artificial Neural Network trained by Particle Swarm Optimization

Rosli, Nurfatihah Syalwiah; Burhani, Nurul Rawaida Ain; Ibrahim, Rosdiazli

doi:10.1109/scored.2019.8896330

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Other studies used more advanced Deep Learning Neural Networks (Choi et al, 2017; Essien & Giannetti, 2020; H. Kuo & Faricha, 2016; W. J. Lee et al, 2019; Maggipinto et al, 2018). Furthermore, some of the surveyed Preditive Analytics used optimization techniques (e.g., Genetic Algorithm, Particle Swarm Optimization) (Rosli et al, 2019; Saldivar, Goh, Li, Chen, et al, 2016; Saldivar, Goh, Li, Yu, et al, 2016), while other works focused on outliers detection and statistical analysis (Albers et al, 2017; Stein et al, 2016).…”

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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