Results 91 to 100 of about 9,526,985 (202)

Bearing Remaining Useful Life Prediction Based on NAP-HSMM [PDF]

, 2023
The prediction of remaining useful life (RUL) is the most challenge task in the area of reliability and safety of rolling bearing. Nuisance attribute projection (NAP) is used in this paper to eliminate the impact of operating conditions in vibration ...
Wang, Sh.
core   +1 more source

Anomaly-informed remaining useful life estimation (AIRULE) of bearing machinery using deep learning framework

MethodsX
A rolling bearing is a crucial element within rotating machinery, and its smooth operation profoundly influences the overall well-being of the equipment.
Pooja Kamat, Satish Kumar, Shruti Patil, Ketan Kotecha   +3 more
doaj   +1 more source

A Novel RUL-Centric Data Augmentation Method for Predicting the Remaining Useful Life of Bearings

Machines
Maintaining the reliability of rotating machinery in industrial environments entails significant challenges. The objective of this paper is to develop a methodology that can accurately predict the condition of rotating machinery in order to facilitate ...
Miao He, Zhonghua Li, Fangchao Hu
doaj   +1 more source

Correction: Prediction of remaining useful life (RUL) of Komatsu excavator under reliability analysis in the Weibull-frailty model. [PDF]

PLoS One, 2021
Ghomghaleh A, Khaloukakaie R, Ataei M, Barabadi A, Qarahasanlou AN, Rahmani O, Pour AB.   +6 more
europepmc   +1 more source

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

Prediction of Remaining Useful Life (RUL) in Refinery using Deep Learning

, 2019
The Remaining Useful Life (RUL) is typically used as the process of predicting the life span of machine before its failure. The purpose of this project is to develop a predictive analysis system of Remaining Useful Life (RUL) in refinery.
Baharadin, Hazirah
core  

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

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

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