A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications

Bousdekis, Alexandros; Lepenioti, Katerina; Apostolou, Dimitris; Mentzas, Gregoris

doi:10.3390/electronics10070828

Cited by 141 publications

(57 citation statements)

References 90 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This calculator computes the value of prospects under CPT. First step involves calculating three outcomes (variants) on the basis of the parameters given by Kahneman and Tversky [103] as presented in Equations ( 1) and (2).…”

Section: Analysis Of the Results And Discussionmentioning

confidence: 99%

“…Challenges faced by stakeholders and the awareness of social and environmental issues and uncertainty are factors that change the way business is done in economic activity. These challenges are the impulse for making decisions under uncertain conditions in Industry 4.0 (I4.0) [1,2]. I4.0 refers to many IT issues combining engineering, information technology and management knowledge.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Use of Prospect Theory for Energy Sustainable Industry 4.0

2021

View full text Add to dashboard Cite

Industry 4.0 challenges facilities entrepreneurs to be competitive in the market in terms of energy by rational decision making. The goal of the paper is aimed at introducing Prospect Theory (PT) in Industry 4.0 for making decisions in order to select an optimal energy technology. To reach this goal, an approach for decision making on energy investment has been developed. In this paper, the authors have also provided a new opportunity to apply the new decision making method for strengthening Industry 4.0 by addressing energy concerns based on which rational decisions have been made. The study uses a fuzzy analytical hierarchy process for weighting the evaluation sub-criteria of energy technologies and a modified PT for making decisions related to the selection of one of the investigated technologies. The results show that it is possible to implement PT in Industry 4.0 via a decision making model for energy sustainability. Decision probability was achieved using a behavioral approach akin to Cumulative Prospect Theory (CPT) for the considered technology options. More specifically, the probability has created the same threshold-based decision possibilities. The authors used the case study method based on a company located in North America which produces hardwood lumber. The company uses a heating system containing natural gas-fired boilers. This study has also contributed to the literature on energy sustainable Industry 4.0 by demonstrating a new phenomenon/paradigm for energy sustainability-based Industry 4.0 through using PT. In this context, the main motivation of writing the article has been to promote energy sustainability via complex mechanisms and systems that involve interrelated functions.

show abstract

Section: Analysis Of the Results And Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Use of Prospect Theory for Energy Sustainable Industry 4.0

2021

View full text Add to dashboard Cite

show abstract

“…Modern algorithms, data storage, and computing power make it possible to not only analyze past behavior, but to anticipate future behavior of industrial equipment with reasonable confidence [1][2][3]. Anticipating future failures is, therefore, a topic that has been receiving increasingly more attention from researchers.…”

Section: Introductionmentioning

confidence: 99%

Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press

et al. 2021

View full text Add to dashboard Cite

The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options.

show abstract

“…The term big data mainly refers to the huge amount of data being continuously generated [1]. The technological developments society is facing, such as IoT, cloud computing, social networks, and mobile devices, among others, are the main reasons for this growth, leading to Industry 4.0 [2,3]. However, big data includes not only the volume but also the variety and velocity, among other aspects, involved in concepts that are still being defined [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

FDR2-BD: A Fast Data Reduction Recommendation Tool for Tabular Big Data Classification Problems

2021

View full text Add to dashboard Cite

In this paper, a methodological data condensation approach for reducing tabular big datasets in classification problems is presented, named FDR2-BD. The key of our proposal is to analyze data in a dual way (vertical and horizontal), so as to provide a smart combination between feature selection to generate dense clusters of data and uniform sampling reduction to keep only a few representative samples from each problem area. Its main advantage is allowing the model’s predictive quality to be kept in a range determined by a user’s threshold. Its robustness is built on a hyper-parametrization process, in which all data are taken into consideration by following a k-fold procedure. Another significant capability is being fast and scalable by using fully optimized parallel operations provided by Apache Spark. An extensive experimental study is performed over 25 big datasets with different characteristics. In most cases, the obtained reduction percentages are above 95%, thus outperforming state-of-the-art solutions such as FCNN_MR that barely reach 70%. The most promising outcome is maintaining the representativeness of the original data information, with quality prediction values around 1% of the baseline.

show abstract

A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications

Cited by 141 publications

References 90 publications

The Use of Prospect Theory for Energy Sustainable Industry 4.0

The Use of Prospect Theory for Energy Sustainable Industry 4.0

Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press

FDR2-BD: A Fast Data Reduction Recommendation Tool for Tabular Big Data Classification Problems

Contact Info

Product

Resources

About