Results 61 to 70 of about 20,775 (227)

An Improved Method for Predicting the Remaining Useful Life Using a Spatial–Temporal Feature Extraction Network With Attention Mechanism

IEEE Access
The prediction of the remaining useful life (RUL) of mechanical equipment is of vital importance to its operation and maintenance. Deep learning methods can effectively extract degradation information closely related to equipment RUL from extensive ...
Xiaojia Yan, Weige Liang, Shiyan Sun
doaj   +1 more source

RUL Prognostics: Recursive Bayesian Ensemble Prediction with Combining Artificial Degradation Patterns

International Journal of Prognostics and Health Management, 2023
With the rising complexity of manufacturing processes, resulting from rapid industrial development, the utilization of remaining useful lifecycle (RUL) prediction, based on failure physics and traditional reliability, has remained limited.
Junhyun Byun, Suhong Min, Jihoon Kang
doaj   +1 more source

Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

, 2018
In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability
Cheng, Cheng, Ding, Han, Ma, Guijun, Sun, Mingyang, Teng, Fei, Yuan, Ye, Zhang, Yong   +6 more
core   +1 more source

Remaining Useful Life Estimation of MoSi₂ Heating Element in a Pusher Kiln Process

Sensors
The critical challenge of estimating the Remaining Useful Life (RUL) of MoSi2 heating elements utilized in pusher kiln processes is to enhance operational efficiency and minimize downtime in industrial applications.
Hafiz M. Irfan, Po-Hsuan Liao, Muhammad Ikhsan Taipabu, Wei Wu   +3 more
doaj   +1 more source

Accelerated Aging System for Prognostics of Power Semiconductor Devices [PDF]

, 2010
Prognostics is an engineering discipline that focuses on estimation of the health state of a component and the prediction of its remaining useful life (RUL) before failure.
Celaya, Jose R., Saha, Sankalita, Vashchenko, Vladislav, Wysocki, Philip   +3 more
core   +1 more source

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

IEEE Access, 2019
Remaining useful life (RUL) is the premise and basis of the equipment health management plan. As accurate as possible life prediction is of great significance to reliability and economy of equipment maintenance.
Yang Ge, Lining Sun, Jiaxin Ma
doaj   +1 more source

Particle Filter-Based Prognostics for an Electrolytic Capacitor Working in Variable Operating Conditions

, 2015
International audiencePrognostic models should properly take into account the effects of operating conditions on the degradation process and on the signal measurements used for monitoring.
Astigarraga, Daniel, Baraldi, Piero, Galarza, Ainhoa, Rigamonti, Marco, Zio, Enrico   +4 more
core   +4 more sources

Predictive Maintenance on the Machining Process and Machine Tool [PDF]

, 2019
This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing.
Box, Breiman, Djeziri, Extrapolation, Farokhzad, Galar, Guyon, Jin, Maronna, Mills, Mobley, Tsymbal, Tyagi, Winkler   +13 more
core   +2 more sources

Stage-Based Remaining Useful Life Prediction for Bearings Using GNN and Correlation-Driven Feature Extraction

Machines
Bearings are critical components in mechanical systems, and their degradation process typically exhibits distinct stages, making stage-based remaining useful life (RUL) prediction highly valuable.
Guangzhong Huang, Wenping Lei, Xinmin Dong, Dongliang Zou, Shijin Chen, Xing Dong   +5 more
doaj   +1 more source

A review of physics-based models in prognostics: application to gears and bearings of rotating machinery [PDF]

, 2016
Health condition monitoring for rotating machinery has been developed for many years due to its potential to reduce the cost of the maintenance operations and increase availability.
Cubillo, Adrian, Esperon Miguez, Manuel, Perinpanayagam, Suresh   +2 more
core   +2 more sources