Optimized Adaptive Local Iterative Filtering Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis

Lv, Yong; Zhang, Yi; Yi, Cancan

doi:10.3390/e20120920

Cited by 18 publications

(13 citation statements)

References 51 publications

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“…There is known a large number of methods for the diagnosis of various mechanisms that is based on the measurement of the parameters of stochastic processes accompanying the operation [10]. The choice of acoustic waves as a source of information is due to the fact that the vibrational waves created by working under the load bearing are directly associated with the force interaction between its parts.…”

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

View full text Add to dashboard Cite

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“…Permutation entropy (PE), as a non-linear dynamic parameter, was introduced by Bandit and Pompe to measure the randomness and dynamic mutation of time sequences [19]. Based on its advantages of intelligibility, low time consumption and strong robustness, PE has achieved many successful applications in the fault diagnosis of rotating machines [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Element Bearings with a Two-Step Scheme Based on Permutation Entropy and Random Forests

Xue

Cao

et al. 2019

Entropy

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This study presents a two-step fault diagnosis scheme combined with statistical classification and random forests-based classification for rolling element bearings. Considering the inequality of features sensitivity in different diagnosis steps, the proposed method utilizes permutation entropy and variational mode decomposition to depict vibration signals under single scale and multiscale. In the first step, the permutation entropy features on the single scale of original signals are extracted and the statistical classification model based on Chebyshev’s inequality is constructed to detect the faults with a preliminary acquaintance of the bearing condition. In the second step, vibration signals with fault conditions are firstly decomposed into a collection of intrinsic mode functions by using variational mode decomposition and then multiscale permutation entropy features derived from each mono-component are extracted to identify the specific fault types. In order to improve the classification ability of the characteristic data, the out-of-bag estimation of random forests is firstly employed to reelect and refine the original multiscale permutation entropy features. Then the refined features are considered as the input data to train the random forests-based classification model. Finally, the condition data of bearings with different fault conditions are employed to evaluate the performance of the proposed method. The results indicate that the proposed method can effectively identify the working conditions and fault types of rolling element bearings.

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“…Pang et al [32] accurately extracted the characteristic band energy entropy that reflects the time-frequency information with the help of singular spectrum decomposition (SSD) [33] and realized the recognition of rotor operation combining with SVM. Using permutation entropy as the evaluation index, Lv et al [34] selected the threshold parameters and the number of components needed to be set in adaptive local iterative filtering (ALIF) [35], and then effectively extracted and highlighted the weak fault characteristics. Zhu et al [36] presented a fault diagnosis approach combining ALIF, MFE, and SVM, which can effectively identify various fault type and severity.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling Bearing

Wan

Peng

2019

Entropy

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Aiming at the problem that the weak faults of rolling bearing are difficult to recognize accurately, an approach on the basis of swarm decomposition (SWD), morphology envelope dispersion entropy (MEDE), and random forest (RF) is proposed to realize effective detection and intelligent recognition of weak faults in rolling bearings. The proposed approach is based on the idea of signal denoising, feature extraction and pattern classification. Firstly, the raw signal is divided into a group of oscillatory components through SWD algorithm. The first component has the richest fault information and perceived as the principal oscillatory component (POC). Secondly, the MEDE value of the POC is calculated and used to describe the characteristics of signal. Ultimately, the obtained MEDE values of various states are trained and recognized by being input as the feature vectors into the RF classifier to achieve the automatic identification of rolling bearing fault under different operation states. The dataset of Case Western Reserve University is conducted, the proposed approach achieves recognition accuracy rate of 100%. In summary, the proposed approach is efficient and robust, which can be used as a supplement to the rolling bearing fault diagnosis methods.

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Optimized Adaptive Local Iterative Filtering Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis

Cited by 18 publications

References 51 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

Fault Diagnosis of Rolling Element Bearings with a Two-Step Scheme Based on Permutation Entropy and Random Forests

An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling Bearing

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