Remaining useful life prediction for rolling bearings using correlation coefficient and Kullback–Leibler divergence feature selection

Multi-feature fusion has been widely used to enhance recognition accuracy for different health stages of rail, which may lead to high-dimensionality and information redundancy of signal. In addition, conventional supervised methods require plenty of labeled samples with class information, which can lead to significant time and economic costs. In order to improve the effectiveness of the electromagnetic acoustic emission (EMAE) technique in rail crack defect recognition, a novel method including multi-feature fusion based on weakly supervised learning and recognition threshold construction, is proposed in this paper. First, a mechanism contains of multi-feature extraction and feature selection, is developed to fully reflect the information of different health stages of rail and avoid interference caused by the ineffective features. Then, the effective features and a novel weakly unsupervised label are input into the self-normalizing convolutional neural network and long short-term memory (SCNN-LSTM) model to construct the rail health indicator (RHI). Finally, the recognition threshold is calculated by the characteristics of RHI, to achieve crack recognition automatically. Furthermore, the experimental results under different working conditions demonstrate that the proposed method achieves higher recognition performance than other existing methods in rail crack defect recognition.

Section: Comparison and Discussionmentioning

confidence: 99%

Rail crack defect recognition based on a multi-feature fusion algorithm using electromagnetic acoustic emission technique

Chang

Zhang

Song

et al. 2023

“…Simple correlation analysis generally measures the degree of correlation by calculating the correlation coefficient between the variables. For two reservoir state variables x 1 (t) and x 2 (t), their correlation can be measured by the Pearson correlation coefficient [22] ρ (x…”

Section: Partial Correlation Analysis Of Reservoir Neuronsmentioning

confidence: 99%

“…Compared with correlation coefficient [22], partial correlation analysis [23] is a more accurate means of measurement. It can measure the degree of association between two variables among multiple variables while controlling for the influence of other variables.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Echo State Network with Partial Correlation Pruning for Time-Series Variables Prediction in Industrial Processes

Huang¹,

Wang²,

Yang³

et al. 2023

For an ordinary echo state network (ESN), redundant information in the huge reservoir will lead to degradation of the prediction performance of the network, especially when the labels of the samples are limited. To solve this problem, a semi-supervised ESN with partial correlation pruning (PCP-S2ESN) is proposed in this paper to scientifically capture the essential association between two reservoir variables while controlling for the influence of other factors. In this way, redundant neurons and their connection weights in the reservoir are eliminated, so that the prediction accuracy is significantly enhanced by optimizing the network structure. Moreover, anunsupervised pre-training procedure is introduced to modify the input weight matrix and reservoir connection weight matrix of the ESN, which successfully achieves precise prediction of time-series variables with limited labels. The superiority of the PCP-S2ESN model is demonstrated through two benchmark prediction tasks and the fed-batch penicillin cultivation (FBPC) process.

“…By using the self-attention model before or after BiGRU, the ability of the self-attention model to deal with original features and abstract features can be comprehensively analyzed. Finally, the outputs of different self-attention models {D (1) , D (2) , D (3) , D (4) } are weighted and combined to obtain the output of the multi-head self-attention model. The error between the model output and multistep RUL values is calculated as a loss function, which is used for reverse fine-tuning of model parameters.…”

Section: Model Fusion For Bigru With Multi-head Self-attentionmentioning

confidence: 99%

Multi-head self-attention bidirectional gated recurrent unit for end-to-end remaining useful life prediction of mechanical equipment

Che

Wang

et al. 2022

In order to reduce error accumulation caused by multistep modeling and achieve general accurate model, this paper proposes an end-to-end remaining useful life (RUL) prediction model based on multi-head self-attention bidirectional gated recurrent unit (BiGRU). Taking multivariable samples with long time series as model input and multistep RUL values as model output, the BiGRU model is constructed for continuous prediction of RUL. In addition, single-head self-attention models are applied for time series and variables of samples before or after the BiGRU, which can be fused into multi-head attention BiGRU. Aeroengine and rolling bearing are selected to testify the effectiveness of the proposed method from the system level and component level respectively. The results show that the proposed method can achieve end-to-end RUL prediction efficiently and accurately. Compared with single-head models and individual deep learning models, the prediction mean square error of the proposed method is reduced by 20%-70%.