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Data Mining Applications in Industry 4.0
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Data Mining Applications in Industry 4.0

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 January 2022) | Viewed by 14305

Special Issue Editors

Dr. Radu Godina

E-Mail Website1 Website2
Guest Editor
Faculty of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
Interests: industrial engineering; industrial symbiosis; energy management; sustainability; circular economy; additive manufacturing; lean manufacturing; quality management systems; sustainable energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Pedro Espadinha da Cruz

E-Mail Website1 Website2 Website3
Guest Editor
UNIDEMI - Department of Mechanical and Industrial Engineering, Faculty of Science and Technology (FCT), Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
Interests: business interoperability; supply chain management; knowledge management; information systems; business process modelling; multiple-criteria decision-making; computer simulation; axiomatic design

Special Issue Information

Dear Colleagues,

In the current economic and political environment, being able to gain a competitive edge is a priority for the industrial sector. Companies of all sizes are extremely interested in the possibilities and opportunities that Industry 4.0 offers. New tools available at industry’s disposal such as data mining, Internet of Things (IoT) and digital twins open up the possibility of production being more intelligently structured in the future, with more information at its disposal. The Internet of Things will find its way into all areas of demand in an intelligent and networked world. However, this means that enormous amounts of data will accumulate in all the production areas of the manufacturing industry. The challenge lies in identifying suitable technologies and then being able to smartly employ them to optimize production processes.

Large amounts of data can be utilized efficiently for production control. The predictive management and evaluation of production data and their analysis requires the implementation of suitable tools. The traceability allows saving valuable time and effort when analysing the production process. Big data and machine learning tools can also be used to optimize machine settings. Implementing them in the production process brings many advantages.

There is still much to explore regarding this new set of tools in the proactive and predictive use of the existing production data. This Special Issue intends to deepen the knowledge of data mining applications, one of the main aspects of Industry 4.0. The potential benefits that data mining applications can bring are diverse; therefore, contributions from different areas of research are welcome. Researchers are encouraged to submit contributions that touch on several aspects of data mining applications in the Industry 4.0 context and its relationship with several contiguous topics.

Dr. Radu Godina
Dr. Pedro Espadinha da Cruz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Industry 4.0
  • Data mining
  • Big data
  • Smart factories
  • Machine learning
  • Industrial Internet of Things
  • Smart manufacturing
  • Cyber-physical systems
  • Virtual reality
  • Decision support systems
  • Digital twins
  • Augmented reality
  • 3D printing
  • Cloud computing

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Published Papers (5 papers)

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Research

12 pages, 2674 KiB  
Article
Model-Free Data Mining of Families of Rotating Machinery
by Elizabeth Hofer and Martin v. Mohrenschildt
Appl. Sci. 2022, 12(6), 3178; https://doi.org/10.3390/app12063178 - 21 Mar 2022
Cited by 2 | Viewed by 1724
Abstract
Machines designed to perform the same tasks using different technologies can be organized into families based on their similarities or differences. We are interested in identifying common properties and differences of such machines from raw sensor data for analysis and fault diagnostics. The [...] Read more.
Machines designed to perform the same tasks using different technologies can be organized into families based on their similarities or differences. We are interested in identifying common properties and differences of such machines from raw sensor data for analysis and fault diagnostics. The usual first step is a feature extraction process that requires an understanding of the machine’s harmonics, bearing frequencies, etc. In this paper, we present a model-free path from the raw sensor data to statistically meaningful feature vectors. This is accomplished by defining a transform independent of the operating frequency and performing statistical reductions to identify the components with the largest variances, resulting in a low dimensional statistically meaningful feature space. To obtain an insight into the family relationships we perform a clustering. As the data set has some labeled characteristics we define an entropy-based measure to evaluate a clustering using the a priori-known labels, resulting in a symmetric measurement uniquely defining the clustering goal. Applying this hierarchically we obtain the family tree. The methods are presented can be applied in general situations. As a case study we apply them to a real data set of vibrating screens. Full article
(This article belongs to the Special Issue Data Mining Applications in Industry 4.0)
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21 pages, 2933 KiB  
Article
Classifying Multivariate Signals in Rolling Bearing Fault Detection Using Adaptive Wide-Kernel CNNs
by Jurgen van den Hoogen, Stefan Bloemheuvel and Martin Atzmueller
Appl. Sci. 2021, 11(23), 11429; https://doi.org/10.3390/app112311429 - 2 Dec 2021
Cited by 11 | Viewed by 2881
Abstract
With the developments in improved computation power and the vast amount of (automatic) data collection, industry has become more data-driven. These data-driven approaches for monitoring processes and machinery require different modeling methods focusing on automated learning and deployment. In this context, deep learning [...] Read more.
With the developments in improved computation power and the vast amount of (automatic) data collection, industry has become more data-driven. These data-driven approaches for monitoring processes and machinery require different modeling methods focusing on automated learning and deployment. In this context, deep learning provides possibilities for industrial diagnostics to achieve improved performance and efficiency. These deep learning applications can be used to automatically extract features during training, eliminating time-consuming feature engineering and prior understanding of sophisticated (signal) processing techniques. This paper extends on previous work, introducing one-dimensional (1D) CNN architectures that utilize an adaptive wide-kernel layer to improve classification of multivariate signals, e.g., time series classification in fault detection and condition monitoring context. We used multiple prominent benchmark datasets for rolling bearing fault detection to determine the performance of the proposed wide-kernel CNN architectures in different settings. For example, distinctive experimental conditions were tested with deviating amounts of training data. We shed light on the performance of these models compared to traditional machine learning applications and explain different approaches to handle multivariate signals with deep learning. Our proposed models show promising results for classifying different fault conditions of rolling bearing elements and their respective machine condition, while using a fairly straightforward 1D CNN architecture with minimal data preprocessing. Thus, using a 1D CNN with an adaptive wide-kernel layer seems well-suited for fault detection and condition monitoring. In addition, this paper clearly indicates the high potential performance of deep learning compared to traditional machine learning, particularly in complex multivariate and multi-class classification tasks. Full article
(This article belongs to the Special Issue Data Mining Applications in Industry 4.0)
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18 pages, 4875 KiB  
Article
Classification of Diabetic Walking for Senior Citizens and Personal Home Training System Using Single RGB Camera through Machine Learning
by Yeoungju Woo, Seoyeong Ko, Sohyun Ahn, Hang Thi Phuong Nguyen, Choonsung Shin, Hieyong Jeong, Byungjoo Noh, Myeounggon Lee, Hwayoung Park and Changhong Youm
Appl. Sci. 2021, 11(19), 9029; https://doi.org/10.3390/app11199029 - 28 Sep 2021
Cited by 9 | Viewed by 2570
Abstract
Senior citizens have increased plasma glucose and a higher risk of diabetes-related complications than young people. However, it is difficult to diagnose and manage elderly diabetics because there is no clear symptom according to current diagnostic criteria. They also dislike the invasive blood [...] Read more.
Senior citizens have increased plasma glucose and a higher risk of diabetes-related complications than young people. However, it is difficult to diagnose and manage elderly diabetics because there is no clear symptom according to current diagnostic criteria. They also dislike the invasive blood sample test. This study aimed to classify a difference in gait and physical fitness characteristics between senior citizens with and without diabetes for a non-invasive method and propose a machine-learning-based personal home-training system for training abnormal gait motions by oneself. We used a dataset for classification with 200 over 65-year-old elders who walked a flat and straight 15 m route in 3 different walking speed conditions using an inertial measurement unit and physical fitness test. Then, questionnaires for participants were included to identify life patterns. Through results, it was found that there were abnormalities in gait and physical fitness characteristics related to balance ability and walking speed. Using a single RGB camera, the developed training system for improving abnormalities enabled us to correct the exercise posture and speed in real-time. It was discussed that there are risks and errors in the training system based on human pose estimation for future works. Full article
(This article belongs to the Special Issue Data Mining Applications in Industry 4.0)
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13 pages, 20923 KiB  
Article
An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems
by Arpad Gellert, Stefan-Alexandru Precup, Bogdan-Constantin Pirvu, Ugo Fiore, Constantin-Bala Zamfirescu and Francesco Palmieri
Appl. Sci. 2021, 11(7), 3278; https://doi.org/10.3390/app11073278 - 6 Apr 2021
Cited by 10 | Viewed by 2570
Abstract
Industrial assistive systems result from a multidisciplinary effort that integrates IoT (and Industrial IoT), Cognetics, and Artificial Intelligence. This paper evaluates the Prediction by Partial Matching algorithm as a component of an assembly assistance system that supports factory workers, by providing choices for [...] Read more.
Industrial assistive systems result from a multidisciplinary effort that integrates IoT (and Industrial IoT), Cognetics, and Artificial Intelligence. This paper evaluates the Prediction by Partial Matching algorithm as a component of an assembly assistance system that supports factory workers, by providing choices for the next manufacturing step. The evaluation of the proposed method was performed on datasets collected within an experiment involving trainees and experienced workers. The goal is to find out which method best suits the datasets in order to be integrated afterwards into our context-aware assistance system. The obtained results show that the Prediction by Partial Matching method presents a significant improvement with respect to the existing Markov predictors. Full article
(This article belongs to the Special Issue Data Mining Applications in Industry 4.0)
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11 pages, 8805 KiB  
Communication
Generative Adversarial Networks for Data Augmentation in Structural Adhesive Inspection
by Ricardo Silva Peres, Miguel Azevedo, Sara Oleiro Araújo, Magno Guedes, Fábio Miranda and José Barata
Appl. Sci. 2021, 11(7), 3086; https://doi.org/10.3390/app11073086 - 30 Mar 2021
Cited by 12 | Viewed by 2599
Abstract
The technological advances brought forth by the Industry 4.0 paradigm have renewed the disruptive potential of artificial intelligence in the manufacturing sector, building the data-driven era on top of concepts such as Cyber–Physical Systems and the Internet of Things. However, data availability remains [...] Read more.
The technological advances brought forth by the Industry 4.0 paradigm have renewed the disruptive potential of artificial intelligence in the manufacturing sector, building the data-driven era on top of concepts such as Cyber–Physical Systems and the Internet of Things. However, data availability remains a major challenge for the success of these solutions, particularly concerning those based on deep learning approaches. Specifically in the quality inspection of structural adhesive applications, found commonly in the automotive domain, defect data with sufficient variety, volume and quality is generally costly, time-consuming and inefficient to obtain, jeopardizing the viability of such approaches due to data scarcity. To mitigate this, we propose a novel approach to generate synthetic training data for this application, leveraging recent breakthroughs in training generative adversarial networks with limited data to improve the performance of automated inspection methods based on deep learning, especially for imbalanced datasets. Preliminary results in a real automotive pilot cell show promise in this direction, with the approach being able to generate realistic adhesive bead images and consequently object detection models showing improved mean average precision at different thresholds when trained on the augmented dataset. For reproducibility purposes, the model weights, configurations and data encompassed in this study are made publicly available. Full article
(This article belongs to the Special Issue Data Mining Applications in Industry 4.0)
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