Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip‐EEMD‐GM(1, 1) Model

Wang, Fengtao; Liu, Xiaofei; Liu, Chenxi; Li, Hongkun; Han, Qingkai

doi:10.1155/2018/3013684

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…Small-scale portable means of technical control may become an option to overcome the corresponding difficulties. Means and devices of this class are not expensive; they do not require changes in the adopted control system and repair technology, but require special training of the service personnel [7]. At the same time, such devices can serve to collect preliminary information about diagnostic signals and how to obtain and process them, which is necessary for the development of integrated systems.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

The development of methods for determining vibration stochastic fields of technological complexes

Marchenko

Monchenko

Martyniuk

2019

EEJET

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22. Enhanced public key security for the McEliece cryptosystem / Baldi M., Bianchi M., Chiaraluce F., Rosenthal J., Schipani D. // arXiv.org. URL: https://arxiv.org/abs/1108.2462 23. Yevseiev S., Rzayev Kh., Tsyhanenko A. Analysis of the software implementation of the direct and inverse transform in non-binary equilibrium coding method // Journal of Information Security. 2016. Vol. 22, Issue 2. P. 196-203. 24. Niederreiter H. Knapsack-type cryptosystems and algebraic coding theory // Problems of Control and Information Theory. 1986. Vol. 15, Issue 2. P. 159-166. 25. Mishchenko V. A., Vilanskiy Yu. V. Ushcherbnye teksty i mnogokanal'naya kriptografiya. Minsk: Enciklopediks, 2007. 292 p. 26. Mishchenko V. A., Vilanskiy Yu. V., Lepin V. V. Kriptograficheskiy algoritm MV 2. Minsk, 2006. 177 p. 27. A statistical test suite for random and pseudorandom number generators for cryptographic applications / Rukhin A., Sota J., Nechvatal J., Smid M., Barker E., Leigh S. et. al. // NIST. 2000. doi: https://doi.org/10.6028/nist.sp.800-22 28. Berlekemp E. R. Algebraicheskaya teoriya kodirovaniya. Moscow: Mir, 1971. 480 p. 29. Teoriya kodirovaniya / Kasami T., Tokura N., Ivadari E., Inagaki Ya. Moscow: Mir, 1978. 576 p. 30. Kuznecov A. A., Korolev R. V., Tomashevskiy B. P. Ocenka stoykosti kripto-kodovyh sredstv zashchity informacii k atakam zloumyshlennika // Systemy upravlinnia, navihatsii ta zviazku. 2010. Issue 2 (14). P. 114-117. 31. Naumenko N. I., Stasev Yu. V., Kuznetsov O. O. Teoretychni osnovy ta metody pobudovy alhebraichnykh blokovykh kodiv. Kharkiv: KhUPS, 2005. 267 p. 32. Yevseiev S. P., Ostapov S., Bilodid I. Research of the properties of hybrid crypto-code constructions // Ukrainian Information Se- На основі аналізу систем технічної діагностики проведено дослідження силових впливів, що виникають в технологічних комплексах. У зв'язку з поділом силових впливів на детерміновані та випадкові, запропоновано різні способи виділення вібрації інформаційних діагностичних характеристик для забезпечення оперативного і достовірного виявлення дефектів, які швидко розвиваються. Достовірна діагностика дозволить перейти з системи планово-попереджувальних ремонтів на організацію ремонтів по поточному стану і зниженню витрат на ремонт і відновлення вузлів технологічних комплексів шляхом раннього виявлення дефектів, що зароджуються у вузлах. Проаналізувавши процес поширення віброакустичних хвиль, зумовлених силовим впливом, створено математичну модель виникнення та поширення пружних хвиль в складних технологічних комплексах від місць їх виникнення до точки спостереження. Наведено кінематичні схеми розповсюдження низькочастотних вібрацій, вібросигналів від щітково-колекторного вузла, а також хвилі від внутрішнього кільця підшипника. Це дало змогу обґрунтувати математичну модель виникнення та поширення віброакустичних хвиль в деталях і вузлах складних технологічних комплексів від різних джерел вібрації. За результатами порівняльного аналізу результатів дослідження реальних вібраційних полів та результатів чисельного моделювання підтве...

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

The development of methods for determining vibration stochastic fields of technological complexes

Marchenko

Monchenko

Martyniuk

2019

EEJET

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“…RUL, which is the service life (remaining life) of a component or system at a certain time in the life cycle, is incredibly important for management integrity at a particular time [7][8][9]. Therefore, the ability to estimate the RUL of components and systems is beneficial for being able to employ different maintenance management strategies to optimize the life cycle phases of a component or system.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Remaining Useful Life for Components in SSC of RSG-Gas Based on Reliability Analysis

Hartini¹,

Hastuti²,

Sunaryo³

et al. 2022

Tri Dasa Mega

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In the maintenance system, efforts are needed to improve the effectiveness of the maintenance system and organization. For effective maintenance planning it is necessary to have a good understanding of the reliability and component availability of the system. For this reason, it is necessary to determine the remaining component life using Remaining Useful Life (RUL), so that maintenance tasks can be planned effectively. The purpose of this study is to determine the remaining life of the safety A component from SSC RSG-GAS based on reliability analysis. The method used in this paper is a statistical approach to estimating RUL. The Weibull hazard model is determined for modeling the hazard function so that it can be integrated in the reliability analysis. The model is verified using data from the safety A component from the SSC RSG-GAS. The results obtained from the analysis are useful for estimating the remaining useful lives of these components which can then be used to plan for effective maintenance and help control unplanned outages. The results obtained can be used for maintenance development and preventive repair planning.

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“…Wu and Zhang [13] proposed a new cascaded fusion convolutional longand short-term memory network for orientation rule prediction because of the limited structure of current deep learning methods and the poor stability of prediction results due to the use of single sensory data. Wang et al [14] used KPCA method to reduce the dimension of the extracted features and used kernel principal component to estimate the parameters of WPHM model and proposed a trend prediction method of rolling bearing residual service life based on Pchip-EEMD-GM(1, 1). ese studies using information fusion technology in the direction of bearing residual life prediction have achieved good results and provide useful reference in information fusion technology, but, using a single type of sensor, the information collected cannot fully reflect the bearing life information, which will affect the prediction of bearing life.…”

Section: Introductionmentioning

confidence: 99%

Residual Life Prediction of Metro Traction Motor Bearing Based on Convolutional Neural Network

Cai

Xie

et al. 2021

Shock and Vibration

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In order to solve the problem that a single type of sensor cannot fully reflect the bearing life information in the process of bearing residual life prediction of metro traction motor, a bearing residual life prediction method based on multi-information fusion and convolutional neural network is proposed. Firstly, the vibration sensor and acoustic emission sensor are used to collect the bearing life signals on the bearing fatigue life test bench. Secondly, wavelet packet decomposition is used to denoise the collected bearing life signal and extract multiple eigenvalues. On this basis, the multiple eigenvalues are normalized, and the bearing degradation trend is analyzed. Finally, the collected bearing life is divided into five stages, and the processed multiple eigenvalues are fused and input into convolutional neural network for training and recognition. The results show that the probability of predicting the stage of bearing life based on multiple eigenvalues and convolutional neural network is more than 98%.

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Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip‐EEMD‐GM(1, 1) Model

Cited by 9 publications

References 13 publications

The development of methods for determining vibration stochastic fields of technological complexes

The development of methods for determining vibration stochastic fields of technological complexes

Prediction of Remaining Useful Life for Components in SSC of RSG-Gas Based on Reliability Analysis

Residual Life Prediction of Metro Traction Motor Bearing Based on Convolutional Neural Network

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