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.

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 Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes

Healthcare ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1393
Author(s):  
Muhammad Waqas Nadeem ◽  
Hock Guan Goh ◽  
Vasaki Ponnusamy ◽  
Ivan Andonovic ◽  
Muhammad Adnan Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Learning Approach ◽  
Health Providers ◽  
Machine Learning Approach ◽  
Patients With Diabetes ◽  
Diagnosis And Classification ◽  
Onset Of Diabetes ◽  
The Cost

A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date.

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.

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