Remaining useful life prediction of rolling bearing under limited data based on adaptive time-series feature window and multi-step ahead strategy

Kong, Weili; Li, Hai

doi:10.1016/j.asoc.2022.109630

Cited by 21 publications

(8 citation statements)

References 60 publications

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“…For the space structure of the linear model, the degradation process at time t k is expressed as equation (1). According to equation (1), the expression at time t k−1 can be obtained:…”

Section: Construction Of the Joint State Space Based On Sliding Sir F...mentioning

confidence: 99%

“…With the passage of running time, the bearing will continue to accumulate damage until failure. However, any failure of the bearing may lead to an accidental shutdown of the entire unit, and may even lead to catastrophic consequences [1,2]. Therefore, it is necessary to monitor the operating condition of the bearing in real-time and perform necessary maintenance before the bearing fails through the prognostic method [3], which can ensure the reliable and stable operation of the mechanical equipment with minimal maintenance expense [4].…”

Section: Introductionmentioning

confidence: 99%

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A sliding sequence importance resample filtering method for rolling bearings remaining useful life prediction based on two Wiener-process models

Song,

Xu,

2023

Meas. Sci. Technol.

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The remaining useful life (RUL) prediction of rolling bearings is an important part of prognostic and health management of mechanical systems. The model based on Wiener process can describe the time variability in the degradation process of bearings. However, in practical engineering, the degradation trends of bearings are often inconsistent, and it is difficult to fit the actual degradation trends of bearings with a single Wiener process model-based filtering method. Therefore, to improve the generalization ability, this paper uses linear model and exponential model based on Wiener process to predict bearing RUL. A sliding sequence importance resample filtering algorithm is proposed to track the degradation state of the bearing and reduce the prediction error by combining the two degradation models. Last, the superiority and effectiveness of the proposed method are illustrated by comparing with other commonly used RUL prediction methods on the basis of PRONOSTIA dataset.

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“…For the space structure of the linear model, the degradation process at time t k is expressed as equation (1). According to equation (1), the expression at time t k−1 can be obtained:…”

Section: Construction Of the Joint State Space Based On Sliding Sir F...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A sliding sequence importance resample filtering method for rolling bearings remaining useful life prediction based on two Wiener-process models

Song,

Xu,

2023

Meas. Sci. Technol.

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show abstract

“…Similarly, A * t is the hidden layer state of the inverse LSTM network at moment t. Then, the computational equation is displayed in Equation (8).…”

Section: Bilstmmentioning

confidence: 99%

“…In situations characterized by a scarcity of available data. Kong et al [8] proposed a rolling bearing remaining service life prediction based on the utilization of an adaptive time series feature window in conjunction with a multi-step forward method. Zhuang et al [9] adopted a multi-source confrontational online regression in the presence of online unknown conditions for residual bearing life prediction.…”

Section: Introductionmentioning

confidence: 99%

Residual Life Prediction of Rolling Bearings Based on a CEEMDAN Algorithm Fused with CNN–Attention-Based Bidirectional LSTM Modeling

Zhang,

Yang,

Yang

2023

Processes

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This paper presents a methodology for predicting the remaining usability of rolling bearings. The method combines a fully adaptive ensemble empirical modal decomposition of noise (CEEMDAN), convolutional neural network (CNN), and attention bidirectional long short-term memory network (ABiLSTM). Firstly, a finite number of intrinsic mode functions (IMFs) are obtained from breaking down the initial vibration signals using CEEMDAN. The IMFs are further screened by combining the correlation criterion and the craggy criterion. Then, time-frequency domain features, which are extracted from the screened IMFs, are reconstructed into a feature set. The SPT is recognized through some features, like the root mean square (RMS), variance, and kurtosis. Secondly, the deterioration character of rolling bearings was extracted using CNN and used to train the ABiLSTM network. Based on the output of the ABiLSTM network, it forecasts how long rolling bearings will last during use. Finally, the XJTU-SY rolling bearing dataset validated the validity of the suggested rolling bearing remaining life prediction method. We compare our algorithm with other algorithms, such as GRU, LSTM, and CNN–BiLSTM, in which the accuracy of MAE, MSE, RMSE, MAPE, and R2_score is significantly improved. Thus, the results of the validation experiments demonstrate that our proposed algorithm has excellent prediction accuracy.

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“…The state space equation constructed by it can well describe the performance degradation evolution process of bearings. Kong W [ 15 ] et al first determined the starting time of prediction and achieved good results in bearing RUL prediction. Liu S et al [ 16 ] established a degradation model that integrates multiple degradation stages of bearings to predict the RUL distribution of different degradation stages.…”

Section: Introductionmentioning

confidence: 99%

A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model

Jiang,

Sheng,

Yang

et al. 2023

Sensors

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Bearing is the critical basic component of rotating machinery and its remaining life prediction is very important for mechanical equipment’s smooth and healthy operation. However, fast and accurate bearing life prediction has always been a difficult point in industry and academia. This paper proposes a new strategy for bearing health assessment based on a model-driven dynamic interval prediction model. Firstly, the mapping proportion algorithm is used to determine whether the measured data are in the degradation stage. After finding the starting point of prediction, the improved annealing algorithm is used to determine the shortest data interval that can be used for accurate prediction. Then, based on the bearing degradation curve and the information fusion inverse health index, the health index is obtained from 36 general indexes in the time domain and frequency domain through screening, fusion, and inversion. Finally, the state space equation is constructed based on the Paris-DSSM formula and the particle filter is used to iterate the state space equation parameters with the minimum interval data to construct the life prediction model. The proposed method is verified by XJTU-SY rolling bearing life data. The results show that the prediction accuracy of the proposed strategy for the remaining life of the bearing can reach more than 90%. It is verified that the improved simulated annealing algorithm selects limited interval data, reconstructs health indicators based on bearing degradation curve and information fusion, and updates the Paris-DSSM state space equation through the particle filter algorithm. The bearing life prediction model constructed on this basis is accurate and effective.

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Remaining useful life prediction of rolling bearing under limited data based on adaptive time-series feature window and multi-step ahead strategy

Cited by 21 publications

References 60 publications

A sliding sequence importance resample filtering method for rolling bearings remaining useful life prediction based on two Wiener-process models

A sliding sequence importance resample filtering method for rolling bearings remaining useful life prediction based on two Wiener-process models

Residual Life Prediction of Rolling Bearings Based on a CEEMDAN Algorithm Fused with CNN–Attention-Based Bidirectional LSTM Modeling

A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model

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