scholarly journals Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 219
Author(s):  
Margarita Razgon ◽  
Alireza Mousavi
Keyword(s):  
Empirical Investigation ◽  
Rule Learning ◽  
Predictive Maintenance ◽  
Proof Of Concept ◽  
Compression Moulding ◽  
Industrial Case Study ◽  
Rule Based ◽  
Beverage Industry ◽  
Novel Approach

In this paper we propose a novel approach of rule learning called Relaxed Separate-and- Conquer (RSC): a modification of the standard Separate-and-Conquer (SeCo) methodology that does not require elimination of covered rows. This method can be seen as a generalization of the methods of SeCo and weighted covering that does not suffer from fragmentation. We present an empirical investigation of the proposed RSC approach in the area of Predictive Maintenance (PdM) of complex manufacturing machines, to predict forthcoming failures of these machines. In particular, we use for experiments a real industrial case study of a Continuous Compression Moulding (CCM) machine which manufactures the plastic bottle closure (caps) in the beverage industry. We compare the RSC approach with a Decision Tree (DT) based and SeCo algorithms and demonstrate that RSC significantly outperforms both DT based and SeCo rule learners. We conclude that the proposed RSC approach is promising for PdM guided by rule learning.

scholarly journals Correction: Razgon, M., et al. Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept. Algorithms 2020, 13, 219

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 86
Author(s):  
Margarita Razgon ◽  
Alireza Mousavi
Keyword(s):  
Predictive Maintenance ◽  
Proof Of Concept ◽  
Rule Based

The authors wish to make the following corrections to their paper [...]

Deployment of a Smart and Predictive Maintenance System in an Industrial Case Study

2020 ◽  
Author(s):  
Filipe Alves ◽  
Hasmik Badikyan ◽  
H.J. Antonio Moreira ◽  
Joao Azevedo ◽  
Pedro Miguel Moreira ◽  
...  
Keyword(s):  
Predictive Maintenance ◽  
Industrial Case Study ◽  
Maintenance System

Automatic Generation of UML Diagrams From Product Requirements Described by Natural Language

2009 ◽  
Author(s):  
Lei Chen ◽  
Yong Zeng
Keyword(s):  
Natural Language ◽  
Semantic Information ◽  
Automatic Generation ◽  
Proof Of Concept ◽  
Requirements Modeling ◽  
Novel Approach ◽  
Graphic Language ◽  
Uml Diagrams ◽  
Future Work

In this paper, a novel approach is proposed to transform a requirement text described by natural language into two UML diagrams — use case and class diagrams. The transformation consists of two steps: from natural language to an intermediate graphic language called recursive object model (ROM) and from ROM to UML. The ROM diagram corresponding to a text includes the main semantic information implied in the text by modeling the relations between words in a text. Based on the semantics in the ROM diagram, a set of generation rules are proposed to generate UML diagrams from a ROM diagram. A software prototype R2U is presented as a proof of concept for this approach. A case study shows that the proposed approach is feasible. The proposed approach can be applied to requirements modeling in various engineering fields such as software engineering, automotive engineering, and aerospace engineering. The future work is pointed out at the end of this paper.

scholarly journals An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study

2020 ◽  
Vol 12 (5) ◽  
pp. 168781402091920 ◽  
Author(s):  
Ebru Turanoglu Bekar ◽  
Per Nyqvist ◽  
Anders Skoogh
Keyword(s):  
Machine Learning ◽  
Manufacturing Industry ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Machine Learning Techniques ◽  
Time Data ◽  
Industrial Case Study ◽  
Intelligent Approach

Recent development in the predictive maintenance field has focused on incorporating artificial intelligence techniques in the monitoring and prognostics of machine health. The current predictive maintenance applications in manufacturing are now more dependent on data-driven Machine Learning algorithms requiring an intelligent and effective analysis of a large amount of historical and real-time data coming from multiple streams (sensors and computer systems) across multiple machines. Therefore, this article addresses issues of data pre-processing that have a significant impact on generalization performance of a Machine Learning algorithm. We present an intelligent approach using unsupervised Machine Learning techniques for data pre-processing and analysis in predictive maintenance to achieve qualified and structured data. We also demonstrate the applicability of the formulated approach by using an industrial case study in manufacturing. Data sets from the manufacturing industry are analyzed to identify data quality problems and detect interesting subsets for hidden information. With the approach formulated, it is possible to get the useful and diagnostic information in a systematic way about component/machine behavior as the basis for decision support and prognostic model development in predictive maintenance.

A Novel Approach to the Automatic Designation of Predefined Census Enumeration Areas and Population Sampling Frames: A Case Study in Somalia

2019 ◽  
Author(s):  
Sarchil Qader ◽  
Veronique Lefebvre ◽  
Amy Ninneman ◽  
Kristen Himelein ◽  
Utz Pape ◽  
...  
Keyword(s):  
Population Sampling ◽  
Census Enumeration ◽  
Novel Approach

A Novel Approach of Plasma Nitrocarburizing Using a Solid Carbon Active Screen – a Proof of Concept

2017 ◽  
Vol 72 (5) ◽  
pp. 254-259 ◽  
Author(s):  
I. Burlacov ◽  
S. Hamann ◽  
H.-J. Spies ◽  
A. Dalke ◽  
J. Röpcke ◽  
...  
Keyword(s):  
Solid Carbon ◽  
Proof Of Concept ◽  
Novel Approach ◽  
Plasma Nitrocarburizing ◽  
Active Screen

scholarly journals Use of Oxidative Stress Responses to Determine the Efficacy of Inactivation Treatments on Cryptosporidium Oocysts

2021 ◽  
Vol 9 (7) ◽  
pp. 1463
Author(s):  
Tamirat Tefera Temesgen ◽  
Kristoffer Relling Tysnes ◽  
Lucy Jane Robertson
Keyword(s):  
Oxidative Stress ◽  
Stress Responses ◽  
Target Genes ◽  
Cryptosporidium Oocyst ◽  
Drinking Water Treatment ◽  
Proof Of Concept ◽  
Environmental Matrices ◽  
Oocyst Wall ◽  
Novel Approach ◽  
Cryptosporidium Oocysts

Cryptosporidium oocysts are known for being very robust, and their prolonged survival in the environment has resulted in outbreaks of cryptosporidiosis associated with the consumption of contaminated water or food. Although inactivation methods used for drinking water treatment, such as UV irradiation, can inactivate Cryptosporidium oocysts, they are not necessarily suitable for use with other environmental matrices, such as food. In order to identify alternative ways to inactivate Cryptosporidium oocysts, improved methods for viability assessment are needed. Here we describe a proof of concept for a novel approach for determining how effective inactivation treatments are at killing pathogens, such as the parasite Cryptosporidium. RNA sequencing was used to identify potential up-regulated target genes induced by oxidative stress, and a reverse transcription quantitative PCR (RT-qPCR) protocol was developed to assess their up-regulation following exposure to different induction treatments. Accordingly, RT-qPCR protocols targeting thioredoxin and Cryptosporidium oocyst wall protein 7 (COWP7) genes were evaluated on mixtures of viable and inactivated oocysts, and on oocysts subjected to various potential inactivation treatments such as freezing and chlorination. The results from the present proof-of-concept experiments indicate that this could be a useful tool in efforts towards assessing potential technologies for inactivating Cryptosporidium in different environmental matrices. Furthermore, this approach could also be used for similar investigations with other pathogens.

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.

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