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Guanwen Zhang and Dongnian Jiang
Rolling bearings are one of the most important and indispensable components of a mechanical system, and an accurate prediction of their remaining life is essential to ensuring the reliable operation of a mechanical system. In order to effectively utilize...
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Xiaofeng Liu, Liuqi Xiong, Yiming Zhang and Chenshuang Luo
Turbofan engines are known as the heart of the aircraft. The turbofan?s health state determines the aircraft?s operational status. Therefore, the equipment monitoring and maintenance of the engine is an important part of ensuring the healthy and stable o...
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Mingxian Wang, Hongyan Wang, Langfu Cui, Gang Xiang, Xiaoxuan Han, Qingzhen Zhang and Juan Chen
The aero-engine is the heart of an aircraft; its performance deteriorates rapidly due to the high temperature and high-pressure environment during flights. It is necessary to predict the remaining useful life (RUL) to improve the reliability of aero-engi...
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Wenliao Du, Xukun Hou and Hongchao Wang
It is difficult to accurately extract the health index of non-stationary signals of rolling bearings under variable rotational speed, which also leads to greater prediction error for bearing degradation models with fixed parameters. For this reason, an a...
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Vahid Safavi, Arash Mohammadi Vaniar, Najmeh Bazmohammadi, Juan C. Vasquez and Josep M. Guerrero
Predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is crucial to preventing system failures and enhancing operational performance. Knowing the RUL of a battery enables one to perform preventative maintenance or replace the batte...
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Xin Wang, Yi Li, Yaxi Xu, Xiaodong Liu, Tao Zheng and Bo Zheng
Data-driven Remaining Useful Life (RUL) prediction is one of the core technologies of Prognostics and Health Management (PHM). Committed to improving the accuracy of RUL prediction for aero-engines, this paper proposes a model that is entirely based on t...
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Genane Youness and Adam Aalah
Prognosis and health management depend on sufficient prior knowledge of the degradation process of critical components to predict the remaining useful life. This task is composed of two phases: learning and prediction. The first phase uses the available ...
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Leonardo Lucio Custode, Hyunho Mo, Andrea Ferigo and Giovanni Iacca
Remaining useful life (RUL) prediction is a key enabler for predictive maintenance. In fact, the possibility of accurately and reliably predicting the RUL of a system, based on a record of its monitoring data, can allow users to schedule maintenance inte...
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Wei Ming Tan and T. Hui Teo
Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form compl...
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Tarek Berghout, Leïla-Hayet Mouss, Ouahab Kadri, Lotfi Saïdi and Mohamed Benbouzid
The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be c...
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Hung-Cuong Trinh and Yung-Keun Kwon
We propose a data-independent framework based on an ensemble of genetic algorithms for fault-type classification and remaining useful life prediction.
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Hung-Cuong Trinh and Yung-Keun Kwon
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Minghu Wu, Chengpeng Yue, Fan Zhang, Rui Sun, Jing Tang, Sheng Hu, Nan Zhao and Juan Wang
The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are critical indicators for assessing battery reliability and safety management. However, these two indicators are difficult to measure directly, posing a challenge to ens...
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Bing Long, Kunping Wu, Pengcheng Li and Meng Li
The remaining useful life (RUL) prediction for hydrogen fuel cells is an important part of its prognostics and health management (PHM). Artificial neural networks (ANNs) are proven to be very effective in RUL prediction, as they do not need to understand...
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Dong Zhou, Long Xue, Yijia Song and Jiayu Chen
Lithium-ion battery on-line remaining useful life (RUL) prediction has become increasingly popular. The capacity and internal resistance are often used as the batteries? health indicator (HI) for quantifying degradation and predicting the RUL. However, t...
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Haobin Wen, Long Zhang and Jyoti K. Sinha
On top of the condition-based maintenance (CBM) practice for rotating machinery, the robust estimation of remaining useful life (RUL) for rolling-element bearings (REB) is of particular interest. The failure of a single bearing often results in secondary...
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Haochen Qin, Xuexin Fan, Yaxiang Fan, Ruitian Wang, Qianyi Shang and Dong Zhang
Predicting the remaining useful life (RUL) of batteries can help users optimize battery management strategies for better usage planning. However, the RUL prediction accuracy of lithium-ion batteries will face challenges due to fewer data samples availabl...
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Wang Xiao, Yifan Chen, Huisheng Zhang and Denghai Shen
Turbine blades are crucial components exposed to harsh conditions, such as high temperatures, high pressures, and high rotational speeds. It is of great significance to accurately predict the life of blades for reducing maintenance cost and improving the...
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Wahyu Caesarendra, Tegoeh Tjahjowidodo, Buyung Kosasih and Anh Kiet Tieu
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Yi Chen, Qiang Miao, Bin Zheng, Shaomin Wu and Michael Pecht
Batteries are one of the most important components in many mechatronics systems, as they supply power to the systems and their failures may lead to reduced performance or even catastrophic results. Therefore, the prediction analysis of remaining useful l...
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