scholarly journals Modeling System Based on Machine Learning Approaches for Predictive Maintenance Applications

2020 ◽  
Author(s):  
João Pedro Serrasqueiro Martins ◽  
Filipe Martins Rodrigues ◽  
Nuno Paulo Ferreira Henriques
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Test Bench ◽  
Industrial Sector ◽  
Predictive Maintenance ◽  
Learning Approaches ◽  
Learning Capacity ◽  
Overall Performance ◽  
The Evolution Of Industry ◽  
Self Learning

Industry 4.0 must respond to some challenges such as the flexibility and robustness of unexpected conditions, as well as the degree of system autonomy, something that is still lacking. The evolution of Industry 4.0 aims at converting purely mechanical machines into machines with self-learning capacity in order to improve overall performance  and contribute to the optimization of maintenance. An important contribution of Industry 4.0 in the industrial sector is predictive maintenance and prescriptive maintenance. This article should be analysed as a methodology proposal to implement an automatic forecasting model in a test bench for the recognition of a machine’s failure and contribute to the development of algorithms for preventive and descriptive maintenance. Keywords: Industry 4.0, Artificial intelligence, Machine learning, Predictive maintenance, Prescriptive maintenance

scholarly journals The Role of Industry 4.0 and BPMN in the Arise of Condition-Based and Predictive Maintenance: A Case Study in the Automotive Industry

2021 ◽  
Vol 11 (8) ◽  
pp. 3438
Author(s):  
Jorge Fernandes ◽  
João Reis ◽  
Nuno Melão ◽  
Leonor Teixeira ◽  
Marlene Amorim
Keyword(s):  
Business Process ◽  
Automotive Industry ◽  
Industry 4.0 ◽  
Process Model ◽  
Business Process Management ◽  
Process Management ◽  
Predictive Maintenance ◽  
The Evolution Of Industry

This article addresses the evolution of Industry 4.0 (I4.0) in the automotive industry, exploring its contribution to a shift in the maintenance paradigm. To this end, we firstly present the concepts of predictive maintenance (PdM), condition-based maintenance (CBM), and their applications to increase awareness of why and how these concepts are revolutionizing the automotive industry. Then, we introduce the business process management (BPM) and business process model and notation (BPMN) methodologies, as well as their relationship with maintenance. Finally, we present the case study of the Renault Cacia, which is developing and implementing the concepts mentioned above.

scholarly journals IDS for Industrial Applications: A Federated Learning Approach with Active Personalization

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6743
Author(s):  
Vasiliki Kelli ◽  
Vasileios Argyriou ◽  
Thomas Lagkas ◽  
George Fragulis ◽  
Elisavet Grigoriou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Network Flow ◽  
Detection System ◽  
Human Life ◽  
Industrial Sector ◽  
Machine Learning Techniques ◽  
Sensitive Information ◽  
Learning Approaches ◽  
Monitoring And Control

Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. Such global integration of IoT solutions has led to an expanded attack surface against IoT-enabled infrastructures. Artificial intelligence and machine learning have demonstrated their ability to resolve issues that would have been impossible or difficult to address otherwise; thus, such solutions are closely associated with securing IoT. Classical collaborative and distributed machine learning approaches are known to compromise sensitive information. In our paper, we demonstrate the creation of a network flow-based Intrusion Detection System (IDS) aiming to protecting critical infrastructures, stemming from the pairing of two machine learning techniques, namely, federated learning and active learning. The former is utilized for privately training models in federation, while the latter is a semi-supervised approach applied for global model adaptation to each of the participant’s traffic. Experimental results indicate that global models perform significantly better for each participant, when locally personalized with just a few active learning queries. Specifically, we demonstrate how the accuracy increase can reach 7.07% in only 10 queries.

Machine learning techniques applied to industrial engineering: a multi criteria approach

2019 ◽  
Author(s):  
Fabio De Felice ◽  
Marta Travaglioni ◽  
Giuseppina Piscitelli ◽  
Raffaele Cioffi ◽  
Antonella Petrillo
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Technological Development ◽  
Predictive Maintenance ◽  
Industrial Engineering ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The World ◽  
The Subject ◽  
To Receive

With the Industry 4.0 (I4.0) beginning, the world is witnessing an important technological development. The success of I4.0 is linked to the implementation of enabling technologies, including Machine Learning, which focuses on the machines’ ability to receive a series of data and learn on their own. The present research aims to systematically analyze the existing literature on the subject in various aspects, including publication year, authors, scientific sector, country, institution and keywords. Understanding and analyzing the existing literature on Machine Learning applied to predictive maintenance is preparatory to recommend policy on the subject.

scholarly journals Identifying Benchmarks for Failure Prediction in Industry 4.0

Informatics ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 68
Author(s):  
Mouhamadou Saliou Diallo ◽  
Sid Ahmed Mokeddem ◽  
Agnès Braud ◽  
Gabriel Frey ◽  
Nicolas Lachiche
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Model ◽  
Supervised Learning ◽  
Industry 4.0 ◽  
Learning Algorithms ◽  
Failure Prediction ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Selection Of

Industry 4.0 is characterized by the availability of sensors to operate the so-called intelligent factory. Predictive maintenance, in particular, failure prediction, is an important issue to cut the costs associated with production breaks. We studied more than 40 publications on predictive maintenance. We point out that they focus on various machine learning algorithms rather than on the selection of suitable datasets. In fact, most publications consider a single, usually non-public, benchmark. More benchmarks are needed to design and test the generality of the proposed approaches. This paper is the first to define the requirements on these benchmarks. It highlights that there are only two benchmarks that can be used for supervised learning among the six publicly available ones we found in the literature. We also illustrate how such a benchmark can be used with deep learning to successfully train and evaluate a failure prediction model. We raise several perspectives for research.

scholarly journals An In-Depth Analysis of the Various Factors Influencing Industry 4.0 and its Impact on the Overall Performance of the Industrial Sector to Achieve Sustainable Development

2021 ◽  
Vol 17 (11) ◽  
pp. 34
Author(s):  
Pritam Kumar
Keyword(s):  
Industry 4.0 ◽  
Industrial Sector ◽  
Innovative Technology ◽  
Research Approach ◽  
Exploratory Research ◽  
Depth Analysis ◽  
Overall Performance ◽  
New Research ◽  
The Impact ◽  
Smart Machine

<p class="0abstract">In the last two years, the entire world has been confronted with the emergence of pandemic Covid 19, which poses several challenges to the entire human race. Covid 19 has been forced to adopt new innovative technology. To survive in this precarious situation; most of the organization has adopted new innovative technology to meet the expectations of the market and the demands of consumers. In this study, the primary goal is to identify the various factors that have an impact on Industry 4.0 while also evaluating the impact of Industry 4.0 on the overall performance of the industrial sector. An exploratory research approach is used by researchers to discover the characteristics and relationships of new research phenomena. This approach is particularly useful when researchers are looking for answers to the questions of what and how they came to be discovered.  This study demonstrates that the use of Industry 4.0 technologies will increase the effectiveness and efficiency of organizations and industries. This is beneficial because organizations and industries need to develop and survive in this competitive era. Adoption of smart machine and smart product applications will become more common with the widespread use of information and communication technology for all aspects of business operations and activities.</p>

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