Discrete entropy-based health indicator and LSTM for the forecasting of bearing health

Zhou, Yuqing; Kumar, Anil; Gandhi, C.P.; Vashishtha, Govind; Tang, Hesheng; Kundu, Pradeep; Singh, Manpreet; Xiang, Jiawei

doi:10.1007/s40430-023-04042-y

Cited by 8 publications

(7 citation statements)

References 54 publications

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“…Kumar et al [97] discovered a novel non-fuzzy entropy measure that was theoretically proven to meet all Shannon's required conditions and used it to create a novel entropy-based HI for RUL prediction and dynamic deterioration assessment. Zhou et al [98] first discovered a symmetric discrimination information measure and deployed this finding to get another novel non-fuzzy entropy measure needed to construct a reliable HI. The proclaimed entropy-based HI is neither oversensitive nor under-sensitive and its value do not vary too much by the varying machine operating conditions.…”

Section: Ai For Defect Prognosis and Estimation Of Rulmentioning

confidence: 99%

Latest innovations in the field of condition-based maintenance of rotatory machinery: a review

Kumar,

Gandhi,

Tang

et al. 2023

Meas. Sci. Technol.

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Health monitoring in rotatory machinery is a process of developing a mechanism to determine its state of deterioration. It involves analysing the presence of damage, locating the fault, determining the severity of the problem, and calculating the amount of time that the machine can still be used effectively by making use of signal processing methods. The journey started to repair when the machine fails and progressed to the modern era, which involves the use of advanced sensors to capture data and conduct on-line signal processing methods to extract relevant features. By seamlessly integrating advanced smart sensing, data collection, and intelligent algorithms, modern technologies have transformed the landscape of condition-based maintenance for rotary machinery, bridging the gap between fundamental understanding and practical engineering applications. In this review paper, first, the roadmap of the condition-based maintenance (CBM) journey for rotary machinery is briefly introduced. Then, CBM task techniques are reviewed in the context of manual identification of defects, applying artificial intelligence (AI) model to identify the defect in the rotary machinery, and AI to carry out defect prognosis and determine the remaining useful life. Finally, the challenges, and issues of signal processing methods to detect faults in rotary machinery, and remedies to overcome such challenges are deeply discussed and future research directions are identified to ensure safe operation for rotary machinery.

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Section: Ai For Defect Prognosis and Estimation Of Rulmentioning

confidence: 99%

Latest innovations in the field of condition-based maintenance of rotatory machinery: a review

Kumar,

Gandhi,

Tang

et al. 2023

Meas. Sci. Technol.

Self Cite

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“…The results are verified on several data sets and good prediction results are obtained. Zhou et al [40] designed an LSTM based RUL prediction method and validated it on three different types of datasets, with better prediction results than other methods. LSTM can better solve the task in terms of longtime sequences and alleviates the problems of gradient disappearance and explosion that exist in RNN [21].…”

Section: Deep Recursive Dynamic Principal Component Analysis (Rdpca)mentioning

confidence: 99%

A novel method of health indicator construction and remaining useful life prediction based on deep learning

Zhan,

Liu,

Yan

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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The construction of health indicators (HI) for traditional deep learning requires human training labels and poor interpretability. This paper proposes an HI construction method based on Stacked Sparse Autoencoder (SSAE) and combines SSAE with Long short-term memory (LSTM) network to predict the remaining useful life (RUL). Extracting features from a single domain may result in insufficient feature extraction and cannot comprehensively reflect the degradation status information of mechanical equipment. In order to solve the problem, this article extracts features from time domain, frequency domain, and time-frequency domain to construct a comprehensive original feature set. Based on monotonicity, trendiness, and robustness, the most sensitive features from the original feature set are selected and put into the SSAE network to construct HI for state partitioning, and then LSTM is used for RUL prediction. By comparing with the existing methods, it is proved that the prediction effect of the proposed method in this paper is satisfied.

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“…Vashishtha [ 20 ] proposed an optimal selection of hyperparameters (HPs) based on a deep learning model to study worm gearboxes and found that this method is more efficient than conventional methods. Zhou [ 21 ] proposed a health index prediction method based on discrete probabilistic entropy and a bearing health prediction method based on long and short term memory, and the comparison shows that the method is superior to other time series prediction models. Vashishtha [ 22 ] proposed an intelligent defect identification scheme for tapered roller bearings based on the ELM model.…”

Section: Introductionmentioning

confidence: 99%

Study on nonlinear dynamic characteristics of a two-speed transmission system at low speed

Zhihui,

Zhang,

Zhang

et al. 2024

PLoS ONE

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A pure shear mechanical model of low gear of six-degree-of-freedom two-speed transmission system is established by using lumped parameter method. The Runge-Kutta method is used to numerically solve the aforementioned nonlinear system. The variation of transmission error between gears is analyzed by using global bifurcation, time domain diagram, phase diagram and Poincare cross section. Moreover, the transfer error bifurcation characteristics of the solar wheel and the first planetary wheel under different gear moduli are investigated. The results show that: by taking the excitation frequency as the control parameter, the system includes period-1 motion, period-2 motion, quasi-periodic motion, multiperiodic motion, and chaotic motion. With the increase of gear modulus, the system mainly presents chaotic motion in the medium frequency range (0.5<ωh≤2). It shows stable period-1 motion in the high frequency range (2<ωh≤3), and the higher the modulus, the wider the high frequency range of period-1 motion. The research results can provide reference for the design and optimization of this kind of two-speed transmission system in the future.

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Discrete entropy-based health indicator and LSTM for the forecasting of bearing health

Cited by 8 publications

References 54 publications

Latest innovations in the field of condition-based maintenance of rotatory machinery: a review

Latest innovations in the field of condition-based maintenance of rotatory machinery: a review

A novel method of health indicator construction and remaining useful life prediction based on deep learning

Study on nonlinear dynamic characteristics of a two-speed transmission system at low speed

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