Bearing Remaining Useful Life Prediction Based on a Scaled Health Indicator and a LSTM Model with Attention Mechanism

Gao, Songhao; Xiong, Xingchuang; Zhou, Yue; Zhang, Jiashuo

doi:10.3390/machines9100238

Cited by 17 publications

(4 citation statements)

References 39 publications

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“…The major RUL prediction intelligent models of rolling bearings CNN [ 15 , 16 , 17 ] and LSTM [ 18 , 19 , 20 ] were chosen to forecast the RUL of three bearings in the above two datasets to demonstrate the superiority of the prediction method in this study. Figure 11 , Figure 12 and Figure 13 display the experiment results.…”

Section: Experimental Verificationmentioning

confidence: 99%

Remaining Useful Life Prediction of Rolling Bearings Using GRU-DeepAR with Adaptive Failure Threshold

Wang

Liu

et al. 2023

Sensors

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Aiming at the problem that a single neural network model has difficulty in accurately predicting trends of the remaining useful life of a rolling bearing, a method of predicting the remaining useful life of rolling bearings using a gated recurrent unit-deep autoregressive model (GRU-DeepAR) with an adaptive failure threshold was proposed. First, time domain and frequency domain features were extracted from the rolling bearing vibration signal. Second, its operation process was divided into a smooth operation stage and degradation stage according to the trend of the accumulated root mean square of maximum. Then, the failure threshold for different bearings were determined adaptively by the maximum of the smooth operation data. The degradation dataset of a rolling bearing was subsequently obtained. In the meantime, a GRU-DeepAR model was built to obtain predictions of the failure time and failure probability. Appropriate model parameters were determined after a large number of tests to assure the effectiveness and prediction accuracy. Finally, the trend of time series and failure times were predicted by inputting the degradation dataset into the GRU-DeepAR model. Experiments showed that the proposed method can effectively improve the accuracy of the remaining useful life prediction of a rolling bearing with good stability.

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Section: Experimental Verificationmentioning

confidence: 99%

Remaining Useful Life Prediction of Rolling Bearings Using GRU-DeepAR with Adaptive Failure Threshold

Wang

Liu

et al. 2023

Sensors

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“…Table S1: 18 Statistical features extracted in time and frequency domains (X n represents the valid vibration data in temporal and spectral domains). References [2,[21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] are cited in the Supplementary Materials file.…”

Section: Supplementary Materialsmentioning

confidence: 99%

A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump

2022

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An efficient Remaining Useful Life (RUL) prediction method is one of the most important features of a condition-based maintenance system. A running machine’s RUL prognosis in its real-time is a challenging task, especially when there is no historic failure data available for that particular machine. In this paper, an online RUL of an in-operation industrial slurry pump having no historical failure data has been predicted. At first, the available raw vibration datasets were filtered out for valid datasets. The obtained valid datasets were utilized for constructing the Health Degradation Trends (HDTs) using principal component analysis and a moving average method. Then, a novel procedure for automatically selecting the HDT’s data points for initiating the iteration process of prediction was formulated. Afterward, a hybrid deep LSTM model embedded with a smart learning rate mechanism was developed for estimating the online RUL using the selected points of HDTs. The online RUL prediction results produced by the developed model were quite satisfactory when they were compared with other online RUL prediction methods.

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“…Haidong et al [16] proposed an early fault prognosis algorithm based on complex wavelet transform and deep gated recurrent unit networks. Gao et al [17] used PWT to construct an efficient HI and estimated the bearing RUL with an encoder-decoder Long-Short Term Memory (LSTM) neural network. Liu et al [18] used a transformer model-based predictor to perform bearing prognosis under different degradation processes.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for LPRE Bearings Remaining Useful Life Estimation Based on Hidden Markov Models and Fatigue Modelling

Galli,

Weber,

Hoblos

et al. 2024

Machines

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Ball bearings are one of the most critical components of rotating machines. They ensure shaft support and friction reduction, thus their malfunctioning directly affects the machine’s performance. As a consequence, it is necessary to monitor the health conditions of such a component to avoid major degradations which could permanently damage the entire machine. In this context, HMS (Health Monitoring Systems) and PHM (Prognosis and Health Monitoring) methodologies propose a wide range of algorithms for bearing diagnosis and prognosis. The present article proposes an end-to-end PHM approach for ball bearing RUL (Remaining Useful Life) estimation. The proposed methodology is composed of three main steps: HI (Health Indicator) construction, bearing diagnosis and RUL estimation. The HI is obtained by processing non-stationary vibration data with the MODWPT (Maximum Overlap Discrete Wavelet Packet Transform). After that, a degradation profile is defined and coupled with crack initiation and crack propagation fatigue models. Lastly, a MB-HMM (Hidden Markov Model) is trained to capture the bearing degradation dynamics. This latter model is used to estimate the current degradation state as well as the RUL. The obtained results show good RUL prediction capabilities. In particular, the fatigue models allowed a reduction of the ML (Machine Learning) model size, improving the algorithms training phase.

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Bearing Remaining Useful Life Prediction Based on a Scaled Health Indicator and a LSTM Model with Attention Mechanism

Cited by 17 publications

References 39 publications

Remaining Useful Life Prediction of Rolling Bearings Using GRU-DeepAR with Adaptive Failure Threshold

Remaining Useful Life Prediction of Rolling Bearings Using GRU-DeepAR with Adaptive Failure Threshold

A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump

Machine Learning Approach for LPRE Bearings Remaining Useful Life Estimation Based on Hidden Markov Models and Fatigue Modelling

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