
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)11812-9
10.1016/j.heliyon.2024.e35781
e35781
Research Article
Wear prediction of high performance rolling bearing based on 1D-CNN-LSTM hybrid neural network under deep learning
Hu Lai ab
Wang Jian c
Lee Heow Pueh b
Wang Zixi zxwang@tsinghua.edu.cn
a⁎
Wang Yuming a
a State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing, 100084, PR China
b Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore
c Luoyang Bearing Research Institute Co., Ltd., Luoyang, 471039, PR China
⁎ Corresponding author. zxwang@tsinghua.edu.cn
27 8 2024
15 9 2024
27 8 2024
10 17 e357811 4 2024
30 7 2024
2 8 2024
© 2024 Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The finished precision rolling bearings after processing are required to pass the life test before they can be put into the market. The life testing takes a lot of time and expense. Aiming to solve the problem of time and expense, the 1D-CNN and 1D-CNN-LSTM hybrid neural networks are used for deep learning based on the existing rolling bearing life big data results (a total of 791152 date). Taking the wear of bearing as the target, the life prediction of bearing is carried out by using Python. The results show that: (1) 1D-CNN-LSTM algorithm and "all parameters” are selected as the best prediction options. (2) "XYZ direction displacement” and "all parameters” have the best fitting effect on the predicted wear value, and the MAPE is 4.18877, 1.2102, 2.68903 and 1.19981, respectively. The 1D-CNN-LSTM algorithm is slightly better than the 1D-CNN algorithm. (3) Using 1D-CNN-LSTM algorithm and "all parameters” to predict the bearing wear life will obtain good results. Compared with the highest 1D-CNN and "Four Bearing Temperatures” parameters, it is reduced by 14.7 times. (4) The prediction process and results provide a wear prediction method for relevant bearing enterprises in the experimental running-in stage. It can also provide reliable research ideas for subsequent related enterprises and scholars.

Keywords

Rolling bearing
1D-CNN-LSTM hybrid neural network
Mixed prediction of wear
Deep learning
Big data
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pmc1 Introduction

High performance bearings are widely used in high-end equipment in various fields of sea, land and air. Its wear will affect the geometric accuracy and surface integrity, resulting in bearing fatigue strength and service life reduction [1,2]. Meanwhile, related enterprises need to achieve zero failure (bearing accident ≤0) in order to ensure the high-performance bearings sold. Therefore, it is necessary to carry out running-in experiment under actual working conditions to observe the relationship between bearing temperature, environmental temperature, displacement in XYZ direction, load, torque, friction coefficient and wear. However, long-term running-in experiment will cost huge manpower and financial resources. Therefore, it is necessary to predict the wear amount [3,4]. A series of scholars have done a lot of research work on the wear amount or fatigue life of bearings.

Such as, in the field of bearing service life prediction, Huang et al. [5] developed a new comprehensive prediction method of rolling bearing remaining useful life (RUL) based on deep convolution neural network bootstrap. The established prediction code is open source. Zhao et al. [6] proposed a new feature extraction method based on data-driven method. A new RUL prediction model of rolling bearings was established, which can effectively realize the RUL prediction of rolling bearings. In order to accurately predict the RUL of rolling bearings, Qin et al. [7] proposed a new gated recurrent unit neural network with double attention gates, namely gated double attention unit (GDAU). Deng et al. [8] proposed a hybrid transfer learning framework based on calibration to improve data fidelity and model universality. The comparison results show that this method is superior in prediction accuracy and uncertainty quantification. Yang et al. [9] proposed a bearing RUL prediction method based on physical information, that is, multi-state time-frequency network (MSTFN). Singh et al. [10] proposed a basic framework. Several data-driven models are adaptively trained according to different bearing health states, and RUL prediction is carried out with partial data from tested bearings. Gupta et al. [11] proposed a new three-stage structure based on deep learning to train the model of bearing data degradation. Kumar et al. [12] put forward the estimation of RUL, which provides a comprehensive solution for bearing health assessment. Dong et al. [13] put forward a new RUL prediction method with finer transmission, which increases the judgment of fault behavior and improves the prediction accuracy of remaining service life. Zhuang et al. [14] proposed a multi-source antagonistic online regression (MAOR) method considering pseudo-domain expansion, which is used to predict the remaining service life of bearings under online unknown conditions. Wang et al. [15] proposed a new method combining improved deep residual network (IDRN) and wavelet transform (WT) to solve composite faults in gearboxes. The validity of WT-IDRN method was verified by two experiments. Experimental results show that the WT-IDRN method has better performance than the existing intelligent fault diagnosis (FD) method in terms of accuracy and generalization ability. Liang et al. [16] proposed a new end-to-end approach for fault-over diagnosis of rotating machinery (RM) at time-varying speeds. The effectiveness of the proposed method under time-varying speed is verified by two experimental examples. The experimental results show that the proposed method has better performance in terms of diagnostic accuracy and model complexity compared with existing methods. References [[17], [18], [19]] were similar and typical studies.

In the aspect of bearing wear prediction, Shutin et al. [20] proposed a hybrid prediction model method. Tests were carried out on traction motor bearings of high-load locomotives to consider and predict their wear and RUL. The results are in good agreement with the statistical data of traction motor bearing operation qualitatively and quantitatively. Eickhoff et al. [21,22] established the simulation model of air foil thrust bearing (AFTB). Through simulation and experimental comparison, the accuracy of surface analysis and wear prediction was verified. Wang et al. [23] proposed a prediction method of rolling bearing performance degradation based on SAE and TCN attention model. A prediction model of wear performance degradation of rolling bearings was established. This model had practical engineering value for predicting the health status of equipment. Suh et al. [24] proposed a new health partition method for convolution neural networks. This method can detect the early symptoms of bearing wear earlier and more effectively. Literature [[25], [26], [27]] were similar and typical study.

In this study, the running-in experimental parameters (four sets of bearing temperature, environmental temperature, XYZ direction displacement, load, torque and friction coefficient) of rolling bearings in various high-end equipment fields (aerospace, high-speed trains, high-end machine tools, etc.) were deeply studied. 1D-CNN and 1D-CNN-LSTM Hybrid Neural Network models were established, and the two models were studied by using big data parameters. Bearing wear was predicted by different running-in parameters. The evaluation results of the two models (R2, MAE, MAPE and RMSE) were compared and analyzed. The main innovation is that it can conduct multi-dimensional analysis through different parameter signals, and obtain the best solution of the algorithm and parameter signals. Meanwhile, through the research of this study, the prediction process and results provide a wear prediction method for relevant bearing enterprises in the experimental running-in stage. It can also provide reliable research ideas for subsequent related enterprises and scholars. No more wasting time and expense. The simplified flow of the research route in this paper was shown in Fig. 1.Fig. 1 Sketch of research ideas.

Fig. 1

2 Predictive mathematical model

2.1 1D-CNN:One-dimensional convolution neural network

Convolutional neural network (CNN) is one of the most perfect algorithms in the field of deep learning, which can be divided into standard convolution and deep component convolution, as shown in Fig. 2.Fig. 2 Model structure of deep convolution and standard convolution.

Fig. 2

As shown in Fig. 2, for standard convolution, the input image is DF×DF×M, and convolution operation is performed with DK×DK×C convolution kernels of size, and the output is DF×DF×N.

The calculation amount of standard convolution is as follows:(1) DK⋅DK⋅M⋅N⋅DF⋅DF

The Eq. (1) shows that the calculation amount of standard convolution is multiple related to the input channel, output channel, convolution kernel size and characteristic graph size. Depth separable convolution consists of two stages: depth convolution and point-to-point convolution. In the deep convolution stage, the calculation amount is as follows Eq. (2):(2) DK⋅DK⋅M⋅DF⋅DF

The computational complexity of point-to-point convolution is Eq. (3):(3) M⋅N⋅DF⋅DF

The computational complexity of depth separable convolution is as follows Eq. (4):(4) DK⋅DK⋅M⋅DF⋅DF+M⋅N⋅DF⋅DF

Then the ratio of depth separable convolution calculation amount to standard convolution calculation amount is as follows Eq. (5):(5) DK⋅DK⋅M⋅DF⋅DF+M⋅N⋅DF⋅DFDK⋅DK⋅M⋅N⋅DF⋅DF=1N+1DK2

Compared with standard convolution, the depth separable convolution has less parameter calculation, lighter network, and can improve the training speed and real-time performance of the model. Convolution can be divided into one-dimensional, two-dimensional and three-dimensional. Among them, 1D-CNN is good at processing sequence data. In this paper, 1D-CNN network is selected for data advanced processing, and its structure is shown in Fig. 3.Fig. 3 Structure of 1D convolutional neural network.

Fig. 3

Meanwhile, the mathematical model of 1D-CNN is obtained, as shown in Eq. (6).(6) out(Ni,Coutj)=bias(Coutj)+∑k=0Cin−1weight(Coutj,k)⋅input(Ni,k)

where N represents the number of data (samples) transmitted to the program for training in a single time. C represents multiple channels. L represents the length of the signal sequence.

2.2 1D-CNN-LSTM hybrid neural network model

Because of the diversity of data, a 1D-CNN-LSTM Hybrid Neural Network model was constructed in this study. The main idea of the model was to combine the feature extraction ability of 1D-CNN network and LSTM network in spatial and temporal dimensions. 1D-CNN network can extract short-term features of time series, while LSTM network was adept at capturing long-term dependent information of time series. The model structure was shown in Fig. 4.Fig. 4 1D-CNN-LSTM model structure.

Fig. 4

In Fig. 4, the original parameter signal was collected by 1D-CNN, and its description method was single. After adding LSTM, the obtained features include local features and global features. By mapping the feature learning of two networks for different types of data at the same time to the same feature space, a joint feature for prediction was obtained. Finally, it was sent to the output layer for final prediction.

3 1D-CNN-LSTM Hybrid prediction algorithm.

In Fig. 5, fatigue test data of bearing model GE90TXG3A were extracted, including 791152 pieces of data, including four sets of bearing temperature, environmental temperature, displacement in XYZ direction, load, torque, friction coefficient and wear. Taking wear as the prediction target, "four sets of bearing temperature”, "displacement in XYZ direction” and "environmental temperature, load, torque and friction coefficient” were used as boundary conditions for training. Meanwhile, 1D-CNN and 1D-CNN-LSTM were used as research methods, and Python was used to realize the hybrid prediction algorithm.Fig. 5 GE90TXG3A bearing samples before and after the experiment.

Fig. 5

The wear amount was normalized to 5000 as an average point, and a total of 158 groups of data were obtained. Take 70 % of the total data (791152) as the training set and 30 % as the test set. Meanwhile, the learning rate is 0.00003, the number of iterations is 200, the number of features for each step pair is 11, and the hidden layer size is 16. Convert the torch input format as follows.

# Read data

data, label, label count = load data()

# Generate a training set test set, 70 % for training and 30 % for testing.

train data, train label, val data, val label = create train data(data, label, 0.7).

Number of categories: 158.

Training set shape(110.5000, 4).

Training set category: 110.

Test set shape (48, 5000, 4).

Test set category: 48.

# To the input format of torch.

train datas =torch.from numpy(train data).to(torch. float32). cuda(0).

train labels= torch. from numpy(train label).to(torch. float32).cuda(0).

val datas=torch.from numpy(val data).to(torch.float32).cuda(0).

val labels = torch.from numpy(val label).to(torch. float32).cuda(0).

train labels.type()

‘torch.cuda.FloatTensor’.

A set of 1D-CNN methods were randomly extracted for analysis. Training was carried out through data iteration, model loading, loss and optimizer definition, progress bar formation and model verification.

4 Analysis of prediction results of bearing fatigue life experimental data.

According to the above extracted fatigue test data of bearing model GE90TXG3A and combined with two prediction algorithms (1D-CNN and 1D-CNN-LSTM), the bearing wear amount was predicted. Eight calculation results were shown in Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10, Fig. 11, Fig. 12, Fig. 13.Fig. 6 1D-CNN (displacement in XYZ direction).

Fig. 6

Fig. 7 1D-CNN (environmental Temperature, load, torque, friction coefficient).

Fig. 7

Fig. 8 1D-CNN (four sets of bearing temperature).

Fig. 8

Fig. 9 1D-CNN (all parameters).

Fig. 9

Fig. 10 1D-CNN-LSTM (displacement in XYZ direction).

Fig. 10

Fig. 11 1D-CNN-LSTM (environmental temperature, load, torque, friction coefficient).

Fig. 11

Fig. 12 1D-CNN-LSTM (four sets of bearing temperature).

Fig. 12

Fig. 13 1D-CNN-LSTM (all parameters).

Fig. 13

In Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10, Fig. 11, Fig. 12, Fig. 13, the Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10, Fig. 11, Fig. 12, Fig. 13 are the fitting of a training wear values with an actual wear value. The Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10, Fig. 11, Fig. 12, Fig. 13 are the fitting of a test wear values with an actual wear value. The Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10, Fig. 11, Fig. 12, Fig. 13 are the fitting of a predicted wear values with an actual wear value.

According to Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10, Fig. 11, Fig. 12, Fig. 13, both 1D-CNN algorithm and 1D-CNN-LSTM algorithm had poor training results with wear values of parameters (environmental temperature, load, torque, friction coefficient) and (four sets of bearing temperature), and better training results with wear values of parameters (displacement in XYZ direction) and (all parameters). The same conclusion was reached by feeding back to the eight final predicted wear values (Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10, Fig. 11, Fig. 12, Fig. 13).

This study also extracted the evaluation results (R2, MAE, MAPE and RMSE) from Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10, Fig. 11, Fig. 12, Fig. 13, as shown in Fig. 14.Fig. 14 Evaluation results of predicted wear.

Fig. 14

According to the evaluation results, when the predicted value of R2 is closer to 1, the predicted value of MAE is closer to 0, the predicted value of MAPE is smaller and the predicted value of RMSE is closer to 0, the fitting effect is best.

The predicted wear values for the parameters (displacement in XYZ direction) and (all parameters) fit best with R2 of 0.97306, 0.96259, 0.97197 and 0.95607, respectively, and the 1D-CNN algorithm was slightly better than the 1D-CNN-LSTM algorithm, as shown in Fig. 14 (a). As shown in Fig. 14 (b), the predicted wear values of the parameters (displacement in XYZ direction) and (all parameters) fit best, with MAE of 0.01457, 0.01334, 0.01513 and 0.0193, respectively, and the 1D-CNN-LSTM algorithm was slightly better than the 1D-CNN algorithm. As shown in Fig. 14 (c), the predicted wear values of the parameters (displacement in XYZ direction) and (all parameters) fit best, with MAPE of 4.18877, 1.2102, 2.68903 and 1.19981, respectively, and the 1D-CNN-LSTM algorithm was slightly better than the 1D-CNN algorithm. The parameters (displacement in XYZ direction) and (all parameters) fit best with RMSE of 0.03032, 0.03572, 0.03093 and 0.03871, respectively, and the 1D-CNN algorithm was slightly better than the 1D-CNN-LSTM algorithm, as shown in Fig. 14 (d).

Among the four evaluation indexes, MAPE is the most important. In addition, 1D-CNN-LSTM algorithm and parameters (all parameters) were selected as the best prediction options. Therefore, when combining the big data bearing model fatigue wear life prediction, the 1D-CNN-LSTM algorithm (all parameters) was selected to predict the bearing wear life effect. Compared with the highest 1D-CNN (four bearing temperatures), it is reduced by 14.7 times. Based on the above conclusions, this paper also compares the MAPE results of other mainstream algorithms (BP, GA, DBO-1DCNN, SSA-1DCNN) with the same parameters (all parameters). As shown in Fig. 15.Fig. 15 MAPE results under different algorithms.

Fig. 15

According to Fig. 15, the MAPE result of 1D-CNN-LSTM proposed in this study was the best among all parameter optimizations. The MAPE result of traditional GA (4.15785) was the worst. Through the analysis of the above cases, it can be concluded that the advantage is that it can directly guide scholars and bearing enterprises to select the most important signal source parameters in the life experiment of rolling bearings. Subsequent research can also remove irrelevant signal parameters, avoid excessive calculation data, and save time. The disadvantage is that a large number of rolling bearing experimental signals are needed for basic training in the early stage to ensure the reliability of subsequent bearing life prediction.

3 Conclusion

In this paper, the bearing experimental temperature, environmental temperature, XYZ direction displacement, load, torque and friction coefficient under the big data of rolling shaft running-in experiment are used to predict the wear. In the prediction process, 1D-CNN and 1D-CNN-LSTM hybrid algorithm models are established. The prediction results of running-in parameter combination under the two algorithms are compared and analyzed. The following conclusions are obtained.(1) No matter 1D-CNN algorithm or 1D-CNN-LSTM algorithm, the training results with the wear values of parameters (environmental temperature, load, torque, friction coefficient) and (four sets of bearing temperature) are poor, while the training results with the wear values of parameters (XYZ direction displacement) and (all parameters) are better.

(2) Among the four evaluation indexes, Mean Absolute Percentage Error is the most important. In addition, 1D-CNN-LSTM algorithm and parameters (all parameters) are selected as the best prediction options. Compared with the highest 1D-CNN (four sets of bearing temperature), it is reduced by 14.7 times.

(3) Through the prediction results, the wear prediction method is provided for related bearing enterprises in the running-in stage of experiment. Reduce unnecessary manpower and financial resources for enterprises. To provide research scholars with wear prediction methods and experimental support.

(4) This study mainly provides research methods for multi-signal parameter processing in rolling bearing life experiment. This method may not be suitable for a single signal parameter. However, the wear characteristics of bearings can be analyzed synthetically with the results of single signal parameters and the life-tested bearings. The following work will also characterize the wear microcosmic of rolling bearing after the life test. Synthetically evaluate the relationship between multi-signal parameters and microscopic wear characterization.

Data availability statement

Data will be made available on request.

CRediT authorship contribution statement

Lai Hu: Writing – original draft, Methodology, Formal analysis, Conceptualization. Jian Wang: Investigation. Heow Pueh Lee: Supervision. Zixi Wang: Writing – review & editing, Conceptualization. Yuming Wang: Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is the supplementary data to this article:Multimedia component 1

Multimedia component 1

Acknowledgements

This research was funded by the National Key R&D Program of Manufacturing Basic Technology and Key Components (2023YFB2406400 ).

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e35781.
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