Abstract
Remaining useful life prediction (RUL) is an important prerequisite for preventive maintenance. The feature fluctuation problem of multi working condition equipment brings challenges to the accuracy of RUL prediction. In this paper, a RUL prediction method for multi working condition equipment is proposed. Firstly, the standardized method of multi working condition equipment health features is proposed to reduce the fluctuation of the features. Secondly, a multi-source data fusion method of equipment health indicators is proposed, which fuses multi-dimensional health features into one-dimensional health indicators, which can reflect the deterioration situation of equipment. Then the sliding window method is used to construct the training and testing samples by time series. In order to reduce the over fitting problem caused by unbalanced training samples, a KNN sample weight assignment method is proposed. Finally, a multilayer BLSTM RUL pre-diction network is established. In order to enhance the prediction accuracy of the network, an improved Huber loss function is added into the prediction network. The experimental results of C-MAPSS data show that the RUL prediction method proposed in this paper has good prediction effect.
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This work was supported in part by the Suzhou Science and Technology Foundation of China under Grant SYG202021.
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Ge, Y., Wu, J., Qin, J., Ma, L., Ding, J. (2022). Remaining Useful Life Prediction Based on Multi-source Sensor Data Fusion Under Multi Working Conditions. In: Wang, Y., Martinsen, K., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation XI. IWAMA 2021. Lecture Notes in Electrical Engineering, vol 880. Springer, Singapore. https://doi.org/10.1007/978-981-19-0572-8_92
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DOI: https://doi.org/10.1007/978-981-19-0572-8_92
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