An Event Based Machine Learning Framework for Predictive Maintenance in Industry 4.0

2019 ◽  
Author(s):  
Matteo Calabrese ◽  
Martin Cimmino ◽  
Martina Manfrin ◽  
Francesca Fiume ◽  
Dimos Kapetis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Rules ◽  
Predictive Maintenance ◽  
Learning Approach ◽  
Combined Approach ◽  
Learning Framework ◽  
Cutting Machine ◽  
Machine Learning Approach ◽  
Industrial Group ◽  
Event Based

Abstract Predictive Maintenance concerns the smart monitoring of machine to avoid possible future failures, since because it is better to intervene before the damage occurs, saving time and money. In this paper, a Predictive Maintenance methodology based on Machine learning approach is presented and it is applied to a real cutting machine, a woodworking machinery in a real industrial group, producing accurate estimations. This kind of strategy is important to deal with maintenance problems given the ever increasing need to reduce downtime and associated costs. The Predictive Maintenance methodology implemented allows dynamical decision rules that have to be considered for maintenance prediction using a combined approach on Azure Machine Learning Studio. The Three models (RF, GBM and XGBM) allowed the accurately predict machine down ever gripped bearing thanks to the pre-processing phases.

Effective Predictive Maintenance to Overcome System Failures—A Machine Learning Approach

2021 ◽  
pp. 341-357
Author(s):  
Sai Kumar Chilukuri ◽  
Nagendra Panini Challa ◽  
J. S. Shyam Mohan ◽  
S. Gokulakrishnan ◽  
R. Vasanth Kumar Mehta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Maintenance ◽  
Learning Approach ◽  
System Failures ◽  
Machine Learning Approach

Improving rail network velocity: A machine learning approach to predictive maintenance

2014 ◽  
Vol 45 ◽  
pp. 17-26 ◽  
Author(s):  
Hongfei Li ◽  
Dhaivat Parikh ◽  
Qing He ◽  
Buyue Qian ◽  
Zhiguo Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Maintenance ◽  
Learning Approach ◽  
Rail Network ◽  
Machine Learning Approach

scholarly journals A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Absalom E. Ezugwu ◽  
Ibrahim Abaker Targio Hashem ◽  
Olaide N. Oyelade ◽  
Mubarak Almutari ◽  
Mohammed A. Al-Garadi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Cities ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Learning Framework ◽  
National Healthcare ◽  
Machine Learning Approach ◽  
The Internet Of Things ◽  
Analyze Data

The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.

scholarly journals Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinchan Qu ◽  
Albert Steppi ◽  
Dongrui Zhong ◽  
Jie Hao ◽  
Jian Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Protein Interactions ◽  
Learning Approach ◽  
Protein Protein Interactions ◽  
Final Model ◽  
Protein Protein Interaction ◽  
Learning Framework ◽  
Machine Learning Approach ◽  
Document Triage

Abstract Background Information on protein-protein interactions affected by mutations is very useful for understanding the biological effect of mutations and for developing treatments targeting the interactions. In this study, we developed a natural language processing (NLP) based machine learning approach for extracting such information from literature. Our aim is to identify journal abstracts or paragraphs in full-text articles that contain at least one occurrence of a protein-protein interaction (PPI) affected by a mutation. Results Our system makes use of latest NLP methods with a large number of engineered features including some based on pre-trained word embedding. Our final model achieved satisfactory performance in the Document Triage Task of the BioCreative VI Precision Medicine Track with highest recall and comparable F1-score. Conclusions The performance of our method indicates that it is ideally suited for being combined with manual annotations. Our machine learning framework and engineered features will also be very helpful for other researchers to further improve this and other related biological text mining tasks using either traditional machine learning or deep learning based methods.

scholarly journals Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 208
Author(s):  
Sofia Fernandes ◽  
Mário Antunes ◽  
Ana Rita Santiago ◽  
João Paulo Barraca ◽  
Diogo Gomes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Acquisition ◽  
Failure Detection ◽  
Predictive Maintenance ◽  
Learning Approach ◽  
Household Appliances ◽  
Machine Learning Approach ◽  
The Cost

Heating appliances consume approximately 48 % of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data.

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