Results 81 to 90 of about 9,187,359 (195)

A Novel Multimodal RUL Framework for Remaining Useful Life Estimation with Layer-wise Explanations

CoRR
Estimating the Remaining Useful Life (RUL) of mechanical systems is pivotal in Prognostics and Health Management (PHM). Rolling-element bearings are among the most frequent causes of machinery failure, highlighting the need for robust RUL estimation methods.
Waleed Razzaq, Yun-Bo Zhao
openaire   +2 more sources

A noval RUL prediction method for rolling bearing: TcLstmNet-CBAM

Scientific Reports
Rolling bearings are pivotal components within rotating mechanical systems, and accurately predicting their remaining service life holds significant practical importance.
Qiang Liu, Zhengwei Dai, Hongxi Lai, Minghao Chen, Huiyuan Huang, Jiahui Fu, Mingxin Hou, Xiaoming Xu, Guangbin Wang, Jin Yan   +9 more
doaj   +1 more source

A New Strategy: Remaining Useful Life Prediction of Wind Power Bearings Based on Deep Learning under Data Missing Conditions

Mathematics
With its formidable nonlinear mapping capabilities, deep learning has been widely applied in bearing remaining useful life (RUL) prediction. Given that equipment in actual work is subject to numerous disturbances, the collected data tends to exhibit ...
Xuejun Li, Xu Lei, Lingli Jiang, Tongguang Yang, Zhenyu Ge   +4 more
doaj   +1 more source

A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings

Applied Sciences, 2019
The goal of this work is to improve the generalization of remaining useful life (RUL) prognostics for wheel hub bearings. The traditional life prognostics methods assume that the data used in RUL prognostics is composed of one specific fatigue damage ...
Shixi Tang, Jinan Gu, Keming Tang, Rong Zou, Xiaohong Sun, Saad Uddin   +5 more
doaj   +1 more source

DFL-RUL: Decentralised Federated Learning for Battery Remaining Useful Life Estimation on Heterogeneous Edge-to-cloud

Accurate remaining useful life (RUL) prediction of lithium-ion batteries is essential for reliable and cost-effective electric vehicle operation, yet existing approaches largely rely on centralised training or overlook deployment constraints and data ...
Niri, Mona Faraji, El-Dalahmeh, Mo’ath, Marco, James, Pournazari, Jaber, Park, Dong-Hwan, Dinh, Truong Quang, Ahn, Jung-Hoon   +6 more
core   +1 more source

Predictive Maintenance of Manufacturing Industry Machines Using Remaining Useful Life (RUL) Prediction

International Journal of Computer Science and Mobile Computing
In this paper, a lightweight Autoencoder-LSTM is provided to predict Remaining Useful Life (RUL) of bearings in real-life circumstances with noisy sensors and a small amount of labeled information. The method involves unsupervised health indicator (HI) construction and time modelling that is supervised.
Vishwa Kiran K H, Mohan S H, Rohit M N, Divyashri C R   +3 more
openaire   +1 more source

Attentive Bidirectional Long Short-Term Memory Model to Predict the Remaining Useful Life of Aircraft Engines

Accurate Remaining Useful Life (RUL) prediction for turbofan engines remains a challenging issue for the aviation industry due to operational condition variability, sensor noise, and the relatively complex degradation patterns of turbofans.
Deo, Ravinesh C., Seixas Leal, Andreia, Ali, Mumtaz, Prasad, Salvin Sanjesh, Berton, Lilian, De Castro Santos, Luis Carlos   +5 more
core   +1 more source

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

Cyclic monitoring of the Remaining Useful Life RUL for the Bearing fault prognosis

, 2023
Over the last decades, Prognostics has played a dominant role in preventive maintenance inmanufacturing. It usually involves estimating the Remaining Useful Life (RUL) or the Time to Failure (TTF)of mechanical systems. In Prognosis, the analysis could often be purely data-driven (Trend Analysis). Itrequires a vast data set and offers the double benefit
openaire   +1 more source

Assessment of overall remaining useful life of lubricants by integrating oil quality and performance

Heliyon
Effective lubricant health monitoring programs are essential for extending the lifespan of both the lubricant and machinery. An accurate and reliable remaining useful life (RUL) prediction is necessary for maintenance decision support. The degradation of
Wasan Chokelarb, Pongsert Sriprom, Lasuardi Permana, Pornsawan Assawasaengrat   +3 more
doaj   +1 more source