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

Schwendemann, Sebastian; Sikora, Axel

doi:10.3390/jimaging9020034

Cited by 8 publications

(2 citation statements)

References 46 publications

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“…It uses CNN to extract tool wear features, and then mines the temporal features of tool wear by LSTM, to achieve effective prediction of tool wear. Schwendemann et al [26] propose a deep learning method based on transfer learning. This method uses the windowed envelope, de-noising, and normalization processing of low-frequency sensor data to form the intermediate domain image and uses CNN and LSTM to perform the estimation of remaining useful life and realize effective transfer learning between different bearing types.…”

Section: Introductionmentioning

confidence: 99%

ConvLSTM-Att: An Attention-Based Composite Deep Neural Network for Tool Wear Prediction

et al. 2023

Machines

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In order to improve the accuracy of tool wear prediction, an attention-based composite neural network, referred to as the ConvLSTM-Att model (1DCNN-LSTM-Attention), is proposed. Firstly, local multidimensional feature vectors are extracted with the help of a one-dimensional convolutional neural network (1D-CNN), which avoids the loss of wear features caused by manual feature extraction. Then the temporal relationship learning between multidimensional feature vectors is performed by introducing a long short-term memory (LSTM) network to make up for the lack of long-short distance dependence of the captured sequence of the CNN network. Finally, an attention mechanism is applied to strengthen the ability to extract key information from tool-wearing temporal features. The proposed ConvLSTM-Att model is trained with the measured tool wear data and then performs as a tool wear predictor. The model is compared with several state-of-the-art models on the PHM tool wear data sets. It significantly outperforms the other models in terms of prediction accuracy, but with similar computational complexity.

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Section: Introductionmentioning

confidence: 99%

ConvLSTM-Att: An Attention-Based Composite Deep Neural Network for Tool Wear Prediction

et al. 2023

Machines

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“…The issue also explores the critical aspect of predictive maintenance through a transfer learning-based approach for estimating machinery's Remaining Useful Life (RUL) [3]. By leveraging limited datasets and low sampling rates, the proposed method showcases the power of abstracting features and combining convolutional layers with Long Short-Term Memory networks.…”

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confidence: 99%

Editorial for the Special Issue on Industrial Machine Learning Applications

Rota,

Lopez,

Setti

2023

J. Imaging

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A Hybrid Strategy Enhanced Sub-Sampled Recursive Second Order Algorithm for Chemical Process Intelligent Identification

Wang

2023

Int J Comput Intell Syst

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The accurate dynamic model of the chemical process is an important condition for the successful implementation of advanced control in the plant. In this paper, an efficient second order algorithm for long short-term memory (LSTM) network training is proposed for chemical process intelligent identification. A novel Hessian inverse recursion method is adopted to achieve fast convergence and avoid the high-cost operation of the classic second order optimization method. Besides, more information is back propagated since the proposed method retains the real curvature information of the neural network. Considering the large amount of chemical process data, a sub-sampled recursive second order-stochastic gradient descent (SRSO-SGD) algorithm which uses sub-sampling method and hybrid strategy is proposed. The identification experiment on a delayed coker fractionator shows that the proposed sub-sampled neural network second order training algorithm has better performance than other learning algorithms in terms of model identification accuracy and convergence speed. By adopting a hybrid strategy that performing Hessian inverse estimation every 3 training epochs, the expensive Hessian inverse calculation cost in the identification process is further reduced while low training and testing errors are maintained.

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

Cited by 8 publications

References 46 publications

ConvLSTM-Att: An Attention-Based Composite Deep Neural Network for Tool Wear Prediction

ConvLSTM-Att: An Attention-Based Composite Deep Neural Network for Tool Wear Prediction

Editorial for the Special Issue on Industrial Machine Learning Applications

A Hybrid Strategy Enhanced Sub-Sampled Recursive Second Order Algorithm for Chemical Process Intelligent Identification

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