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| Prediction of remaining useful life based on t-SNE and LSTM for rotating machinery |
| GE Yang1,2, GUO Lanzhong1,2, NIU Shuguang1,2, DOU Yan1,2 |
1. School of Mechanical Engineering, Changshu Institute of Technology, Changshu 215500, China;
2. Jiangsu elevator intelligent safety key construction laboratory, Changshu 215500, China |
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Abstract Aiming at the problem of remaining useful life prediction of rotating machinery, a prediction method based on t-Distributed Stochastic Neighbor embedding (t-SNE) and Long Short-Term Memory network (LSTM) was proposed. First of all, the t-SNE dimensionality reduction method was introduced into the feature extraction of rotating machinery vibration signals, and the example verifies that no matter for the time-frequency domain features or energy features obtained by wavelet packet decomposition, the feature differentiation is more obvious after t-SNE dimensionality reduction, and the correct rate of fault mode recognition using the dimensionality reduction features is close to 100%. Secondly, it was proposed to use the divergence between samples as the degradation index of rotating machinery. the experimental results show that the divergence between samples has a more obvious performance on the performance degradation trend of rotating machinery than other indexes. Finally, the LSTM method was used to predict the remaining useful life with different training sample sizes. In order to verify the effectiveness of the LSTM method, it was compared with the BP neural network, grey prediction model, support vector machine and other methods. The results show that the LSTM method can predict the degradation trend of rotating machinery and significantly improve the prediction accuracy of the remaining useful life. It has a certain theoretical guiding significance for the health monitoring and life prediction of rotating machinery.
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Received: 22 November 2018
Published: 28 March 2020
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