Investigation on Rolling Bearing Remaining Useful Life Prediction: A Review

Liu, Huiyu; Mo, Zhenling; Zhang, Heng; Zeng, Xiaoming; Wang, Yunlong; Miao, Qiang

doi:10.1109/phm-chongqing.2018.00175

Cited by 17 publications

(4 citation statements)

References 38 publications

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“…Model-based methods describe degradation processes of a system using mathematical or physical models, and adjust model parameters using measured data (Lei, 2016;Yoo & Baek, 2018). In (Liu et al, 2018), physical model based approaches are presented, including Paris Crack Growth Model, Damage Mechanics-based Model, Spall Progression-based Model, and Stress-based Fatigue Model. Generally, modelbased methods are quite reliable, provided that the adopted model is accurate enough to encode the underlying system.…”

Section: Related Workmentioning

confidence: 99%

Predicting RUL of Bearings in the Pumps of a Critical Industrial Air Treatment System

Beirami¹,

Calzà²,

Cimatti³

et al. 2020

PHME_CONF

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Predictive maintenance is emerging as a promising technique to overcome the limitations of periodic maintenance. It integrates automatic condition monitoring to evaluate the health status of a system or device, with the estimation of Remaining Useful Life (RUL) of its components, in order to schedule maintenance only when really needed, minimizing the downtime of a plant. In this paper, we report our results in evaluating the health conditions of the rotating ball bearings of a critical air pump which is part of the Clean Room industrial facility operating in the Micro-Technologies Laboratory of Fondazione Bruno Kessler. We instrumented such component with vibration and acoustic sensors with the aim of identifying a model of the evolving degradation and estimating the RUL of the bearings. The current dataset covers a period of one month before bearing replacement and about six months after the replacement. The first models, based on regression and particle filter processing of critical spectral components extracted from the sensors, indicate an estimated RUL of about 12 months that is in agreement with the average lifetime based on scheduled maintenance. Subsequent model evolutions have been observed in conjunction with scheduled greasing and periods of lower stress, which resulted in remarkable deviations from the initial degradation trends and in a consequent notable increase of the estimated RUL. Results achieved so far are promising and could be used to extend the temporal distance among periodic maintenance interventions according to the estimated RUL.

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Section: Related Workmentioning

confidence: 99%

Predicting RUL of Bearings in the Pumps of a Critical Industrial Air Treatment System

Beirami¹,

Calzà²,

Cimatti³

et al. 2020

PHME_CONF

View full text Add to dashboard Cite

show abstract

“…These diagnosis methods are based on measurements that are recorded and analyzed in conjunction with further information by experts to detect or predict a defect. Statistical methods in general and Artificial Intelligence (AI) in particular have been used to optimize the analysis [ 5 , 6 ]. These methods promise timely diagnosis and predictive maintenance processes to reduce unplanned downtime and thus increase production efficiency and operational reliability [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Transfer-Learning-Based Estimation of the Remaining Useful Life of Heterogeneous Bearing Types Using Low-Frequency Accelerometers

Schwendemann

Sikora

2023

J. Imaging

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Deep learning approaches are becoming increasingly important for the estimation of the Remaining Useful Life (RUL) of mechanical elements such as bearings. This paper proposes and evaluates a novel transfer learning-based approach for RUL estimations of different bearing types with small datasets and low sampling rates. The approach is based on an intermediate domain that abstracts features of the bearings based on their fault frequencies. The features are processed by convolutional layers. Finally, the RUL estimation is performed using a Long Short-Term Memory (LSTM) network. The transfer learning relies on a fixed-feature extraction. This novel deep learning approach successfully uses data of a low-frequency range, which is a precondition to use low-cost sensors. It is validated against the IEEE PHM 2012 Data Challenge, where it outperforms the winning approach. The results show its suitability for low-frequency sensor data and for efficient and effective transfer learning between different bearing types.

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“…Remaining life prediction methods are divided into physical model-based methods, data-driven methods, statistical methods and hybrid methods [2,3]. With the rapid development of the industrial big data era, a large amount of data is generated in various industrial behavioral processes.…”

Section: Introductionmentioning

confidence: 99%

A similarity-based remaining useful life prediction method using multimodal degradation features and adjusted cosine similarity

Kong

Zeng

et al. 2023

Meas. Sci. Technol.

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When a large amount of full life-cycle data are available, similarity-based methods are the preferred method for remaining useful life (RUL) prediction due to their reliability and accuracy. Traditional similarity-based RUL prediction methods use a single model and single-scale degradation features, which are incapable of fully capturing the degradation behavior of the system. Additionally, the similarity of spatial orientation is neglected in the similarity-matching process. To fill these research gaps, a novel method is developed based on multimodal degradation features and adjusted cosine similarity to tackle complex-system remaining useful life prediction in this paper. Complete ensemble empirical mode decomposition with adaptive noise is employed to decouple global degradation and random fluctuations in run-to-failure sensor data. Slow feature analysis is utilized to obtain local degradation features, and residual terms are used as global degradation features. Then, multimodal degradation features are transformed into one-dimensional health degradation indicators by bidirectional gated recurrent unit autoencoder. An adjusted cosine similarity is developed to estimate the matching similarity between the test degradation curve and the training degradation curve. The proposed scheme captures the time-varying multimodal degradation behavior which eliminates the effects of random fluctuations while retaining important degradation differences information. The designed scheme is evaluated on the C-MAPSS dataset and the results illustrate the competitiveness and effectiveness of the proposed method.

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Investigation on Rolling Bearing Remaining Useful Life Prediction: A Review

Cited by 17 publications

References 38 publications

Predicting RUL of Bearings in the Pumps of a Critical Industrial Air Treatment System

Predicting RUL of Bearings in the Pumps of a Critical Industrial Air Treatment System

Transfer-Learning-Based Estimation of the Remaining Useful Life of Heterogeneous Bearing Types Using Low-Frequency Accelerometers

A similarity-based remaining useful life prediction method using multimodal degradation features and adjusted cosine similarity

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