Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems

Bermeo-Ayerbe, Miguel Angel; Ocampo‐Martínez, Carlos; Díaz-Rozo, Javier

doi:10.1016/j.energy.2021.121691

Cited by 43 publications

(15 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The precision of a forecast degrades because any system experiences unforeseen changes in machine running conditions. Therefore, an adaptive method is necessary to update the model [34]. According to the literature, concept drift can be detected using active or passive methods, as described in Section 2 [14], [15], [35], [39].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Modeling With Integrated Concept Drift Detection for Predicting Real-Time Energy Consumption of Industrial Machines

2022

View full text Add to dashboard Cite

Industrial machinery is a significant energy consumer, and its CO 2 emissions have increased dramatically in recent years. Therefore, energy efficiency is becoming crucial for businesses, governments, as well as the planet. Estimating the power consumption of industrial machines with greater accuracy assists management and optimizes machine operation parameters. Real-time industrial machine datasets present several challenges, such as changes in the data over time, unknown running conditions, missing data, etc. Most research publications focus on the accuracy of traditional static models of forecasting; however, prediction performance deteriorates over time because data evolves. We implemented deep learning as a prediction model for three distinct real-world industrial datasets. The proposed method, dynamic modeling with memory (DMWM), improved overall prediction performance compared with conventional approaches by identifying concept drifts and optimizing the number of required models in response to industrial datasets' recurring machine energy consumption patterns.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Bermeo et al [34] used industrial testing data with three artificially generated concept drifts. Compared with the nonadaptive model, their proposed adaptive strategy outperformed the conventional approaches in terms of energy prediction performance.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Modeling With Integrated Concept Drift Detection for Predicting Real-Time Energy Consumption of Industrial Machines

2022

View full text Add to dashboard Cite

show abstract

“…They assume that the machinery errors can be inferred through concept drifts. In [142], Barmeo et al utilize the model accuracy to detect data drift in order to adapt the model to the evolving data because of the natural degradation of machinery for energy prediction modeling applications. Compared to the methods without concept drift detection, their system can double the fit rate of the energy estimation.…”

Section: Concept Drift Detectionmentioning

confidence: 99%

A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions

Kovatsch

Mattern

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Artificial intelligence (AI) has been successfully applied in industry for decades, ranging from the emergence of expert systems in the 1960s to the wide popularity of deep learning today. In particular, inexpensive computing and storage infrastructures have moved data-driven AI methods into the spotlight to aid the increasingly complex manufacturing processes. Despite the recent proverbial hype, however, there still exist non-negligible challenges when applying AI to smart manufacturing applications. As far as we know, there exists no work in the literature that summarizes and reviews the related works for these challenges. This paper provides an executive summary on AI techniques for non-experts with a focus on deep learning and then discusses the open issues around data quality, data secrecy, and AI safety that are significant for fully automated industrial AI systems. For each challenge, we present the state-of-the-art techniques that provide promising building blocks for holistic industrial AI solutions and the respective industrial use cases from several domains in order to better provide a concrete view of these techniques. All the examples we reviewed were published in the recent ten years. We hope this paper can provide the readers with a reference for further studying the related problems.

show abstract

“…In recent times, the threat of climate change has prompted significant investment by national and international governments in efforts to mitigate the environmental impacts and emissions associated with the consumption of fossil fuels and raw materials [1][2][3]. In this context, in the integration of state-of-the-art intelligent technologies which allow for real-time monitoring of energy and operational parameters for comprehensive insights into energy-consuming systems [4][5][6][7][8][9][10][11], and the study of innovative processes and technologies for reclaiming organic and inorganic materials from waste streams [12][13][14][15][16][17][18][19], they play a key role. The upgrading of materials derived from different waste sources has been emphasised as a strategic way of achieving sustainability goals and promoting circular economies [10,20,21].…”

Section: Introductionmentioning

confidence: 99%

Mats Made from Recycled Tyre Rubber and Polyurethane for Improving Growth Performance in Buffalo Farms

Masiello,

di Cicco,

Spagnuolo

et al. 2024

AgriEngineering

View full text Add to dashboard Cite

This study focuses on anti-trauma mats designed for buffaloes’ comfort, using as raw materials rubber powder from end-of-life tyres (ELTs) and an isocyanate-based polyurethane resin binder. The first part of the study focused on mat formulation. Whilst it was possible to select a unique combination of raw materials and design features, it was necessary to investigate the relationship between three critical parameters affecting mat consistency and therefore buffalo comfort: binder quantity, mat thickness, and desired final mat density (bulk). In order to quantitatively assess the variation in hardness, various combinations were investigated within well-defined ranges based on the relevant literature. The results obtained from nine selected combinations indicate that increases in the three critical parameters do not induce a real phase transition in the final product consistency, although the hardness suggests an increasing trend. The mats consistently exhibited a moderately soft/hard consistency, offering environmental benefits in terms of increased rubber usage and potentially reduced chemical binder, depending on the desired thickness. The selected mixture showed excellent resistance to heavy chemical loads, suggesting reliability for frequent cleaning operations. The second part of the study involved field trials of the mats with calves. This involved monitoring their weight gain and appetite levels over a 90-day period. The results showed excellent growth performance compared to uncoated grids (i.e., weight gain was approximately 20% higher at the end of the observation period); this was similar to that achieved with the use of straw bedding. However, compared to straw bedding, the mats (i) exhibit long-term durability, with no signs of wear from washing or trampling over the months of the trial, (ii) allow for quick and efficient cleaning, and (iii) enable companies to save on labour, material (straw), and waste disposal costs, while maintaining (or even improving) the same welfare levels associated with the use of straw.

show abstract

Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems

Cited by 43 publications

References 29 publications

Dynamic Modeling With Integrated Concept Drift Detection for Predicting Real-Time Energy Consumption of Industrial Machines

Dynamic Modeling With Integrated Concept Drift Detection for Predicting Real-Time Energy Consumption of Industrial Machines

A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions

Mats Made from Recycled Tyre Rubber and Polyurethane for Improving Growth Performance in Buffalo Farms

Contact Info

Product

Resources

About