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Boris Vasilyev, Sergei Nikolaev, Mikhail Raevskiy, Sergei Belov and Ighor Uzhinsky
Blade damage accounts for a substantial part of all failure events occurring at gas-turbine-engine power plants. Current operation and maintenance (O&M) practices typically use preventive maintenance approaches with fixed intervals, which involve hig...
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Chinedu I. Ossai
Prognosis and remaining useful life (RUL) estimation of components and systems (C&S) are vital for intelligent asset-integrity management. The implementation of the traditional multi-level particle filter (TRMPF) has improved prognosis when compared ...
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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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Syed Safdar Hussain and Syed Sajjad Haider Zaidi
This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect categorizes the machine?s current operational state by analyzin...
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Simone Castelli and Andrea Belleri
In recent years, structural health monitoring, starting from accelerometric data, is a method which has become widely adopted. Among the available techniques, machine learning is one of the most innovative and promising, supported by the continuously inc...
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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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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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David Gerhardinger, Anita Domitrovic, Karolina Krajcek Nikolic and Darko Ivancevic
This paper introduces an expert system approach for predicting the remaining useful life (RUL) of light aircraft structural components by analyzing operational and maintenance records. The expert system consists of four modules: knowledge acquisition, kn...
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Yuan-Jen Chang, He-Kai Hsu, Tzu-Hsuan Hsu, Tsung-Ti Chen and Po-Wen Hwang
With the development of next-generation airplanes, the complexity of equipment has increased rapidly, and traditional maintenance solutions have become cost-intensive and time-consuming. Therefore, the main objective of this study is to adopt predictive ...
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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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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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Feixiang Ren, Jiwang Du and Daofang Chang
To address the challenge of accurate lifespan prediction for bearings in different operating conditions within ship propulsion shaft systems, a two-stage prediction model based on an enhanced domain adversarial neural network (DANN) is proposed. Firstly,...
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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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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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Marie Bieber and Wim J. C. Verhagen
In recent years, there has been an enormous increase in the amount of research in the field of prognostics and predictive maintenance for mechanical and electrical systems. Most of the existing approaches are tailored to one specific system. They do not ...
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Raúl Llasag Rosero, Catarina Silva and Bernardete Ribeiro
Predictive Maintenance (PM) strategies have gained interest in the aviation industry to reduce maintenance costs and Aircraft On Ground (AOG) time. Taking advantage of condition monitoring data from aircraft systems, Prognostics and Health Maintenance (P...
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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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Elena Quatrini, Francesco Costantino, Xiaochuan Li and David Mba
In the industrial panorama, many processes operate under time-varying conditions. Adapting high-performance diagnostic techniques under these relatively more complex situations is urgently needed to mitigate the risk of false alarms. Attention is being p...
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Donghwan Kim, Seungchul Lee and Daeyoung Kim
As technology advances, the equipment becomes more complicated, and the importance of the Prognostics and Health Management (PHM) to monitor the condition of the equipment has risen. In recent years, various methodologies have emerged. With the developme...
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Danpeng Cheng, Wuxin Sha, Linna Wang, Shun Tang, Aijun Ma, Yongwei Chen, Huawei Wang, Ping Lou, Songfeng Lu and Yuan-Cheng Cao
Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring t...
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