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

Information

卷:11

Sofia Fernandes¹  Mário Antunes¹  AnaRita Santiago¹  Diogo Gomes¹  RuiL. Aguiar¹  JoãoPaulo Barraca¹ 

[1] DETI, Universidade de Aveiro, 3810-193 Aveiro, Portugal;

关键词: big data applications;    big data services;    infrastructure;    data processing;    data analysis;    predictive maintenance;   

DOI  :  10.3390/info11040208

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown