RUL Prediction of Rolling Bearings Based on a DCAE and CNN

Wang, Chenyang; Jiang, Wanlu; Xukang, Yang; Zhang, Shuqing

doi:10.3390/app112311516

Cited by 39 publications

(17 citation statements)

References 44 publications

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“…The prediction process of the bearing is based on 1-D time-series signals. The training model is affected by an accurate addition of labels to the data [37]. Therefore, after the segmented processing of the HI, RULs are added to the original data as labels by using the constructed HI value.…”

Section: ) Fully Connected Layermentioning

confidence: 99%

Remaining useful life prediction for rolling bearings based on similarity feature fusion and convolutional neural network

Nie

Zhang

et al. 2022

J Braz. Soc. Mech. Sci. Eng.

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As a critical content of condition-based maintenance (CBM) for mechanical systems, remaining useful life (RUL) prediction of rolling bearing attracts extensive attention to this day. Through mining the bearing degradation rule from operating data, the deep learning method is often used to perform RUL prediction. However, due to the complexity of operating data, it is usually difficult to establish a satisfactory deep learning model for accurate RUL prediction. Thus, a novel convolutional neural network (CNN) prediction method based on similarity feature fusion is proposed. In this paper, the similarity features are extracted based on the correlation between statistical features and time series. After sensitive feature screening, eligible features are applied to develop a health indicator (HI), which can be used to define the bearing failure stages and reduces the complexity of the CNN model. Subsequently, a one-dimensional CNN is established to predict the RUL of bearing, and the HI is utilized to train the prediction model. The proposed approach is verified by FEMTO bearing datasets and IMS bearing datasets. And the experimental results reveal the superiority and effectiveness of the feature fusion-based CNN method in constructing HI and accurate RUL prediction.

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Section: ) Fully Connected Layermentioning

confidence: 99%

Remaining useful life prediction for rolling bearings based on similarity feature fusion and convolutional neural network

Nie

Zhang

et al. 2022

J Braz. Soc. Mech. Sci. Eng.

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show abstract

“…Dynamic maintenance scheduling can only work after acquiring the prediction of future states. Generally, there are three types of methods to carry out RUL prediction, including model-based, data-based, and hybrid-based approaches [30][31][32]. Model-based methods use the physical model of the degradation process, which is extracted from the expert knowledge about the life cycle of a given product.…”

Section: Introductionmentioning

confidence: 99%

Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction

et al. 2022

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Remanufacturing is an activity of the circular economy model whose purpose is to keep the high value of products and materials. As opposed to the currently employed linear economic model, remanufacturing targets the extension of products and reduces the unnecessary and wasteful use of resources. Remanufacturing, along with health status monitoring, constitutes a key element for lifetime extension and reuse of large industrial equipment. The major challenge is to determine if a machine is worth remanufacturing and when is the optimal time to perform remanufacturing. The present work proposes a new predictive maintenance framework for the remanufacturing process based on a combination of remaining useful life prediction and condition monitoring methods. A hybrid-driven approach was used to combine the advantages of the knowledge model and historical data. The proposed method has been verified on the realistic run-to-failure rolling bearing degradation dataset. The experimental results combined with visualization analysis have proven the effectiveness of the proposed method.

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“…6 Then, machine learning or time series prediction methods 7 are used to build a model and predict RUL 8 based on these indicators, such as Support Vector Machine (SVM) 9 and Support Vector Data Description (SVDD). 10 Wang et al 11 established a health indicator by combining Deep Convolutional Auto-Encoder (DCAE) and Self-Organizing Map (SOM) networks to perform more advanced characterization against the original vibration data. Yang et al 12 established a health indicator by combining Piecewise Cubic Hermite Interpolating Polynomial Local Characteristic-scale Decomposition (PCHIP-LCD) and Generalized Regression Neural Network (GRNN) to predict the RUL of rolling bearing.…”

Section: Introductionmentioning

confidence: 99%

Local-global cooperative least squares support vector machine and prediction of remaining useful life of rolling bearing

Letian

Gao

et al. 2022

Measurement and Control

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Aiming at the inability to accurately predict the remaining useful life of rolling bearings due to the phased degradation in the bearing degradation process, this paper proposes a local-global cooperative learning strategy to solve the problem that the information cannot be fully utilized due to the characteristics of the data. And the strategy is combined with the least squares support vector machine to build a regression model, which improves the accuracy of the remaining useful life of bearings. The strategy evaluates the health state of bearing’s degradation process based on singular value decomposition and kurtosis criteria to divide each degradation stage of the bearing so that the degradation information of each stage of the bearing can be learned. Then, according to the proposed cooperative learning mechanism, the local learning of each stage of the bearing is extended to the global learning of the total degradation process of the bearing. Finally, the learning strategy is applied to the least squares support vector machine model to predict the remaining useful life of bearings better. The results on the PHM2012 dataset show that our method’s values of root mean square error and mean absolute percentage error are 35.4461 and 0.2041, respectively.

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RUL Prediction of Rolling Bearings Based on a DCAE and CNN

Cited by 39 publications

References 44 publications

Remaining useful life prediction for rolling bearings based on similarity feature fusion and convolutional neural network

Remaining useful life prediction for rolling bearings based on similarity feature fusion and convolutional neural network

Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction

Local-global cooperative least squares support vector machine and prediction of remaining useful life of rolling bearing

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