Abstract
A detection method for predicting the remaining service life of the movement is proposed in this paper. This optimized model is constructed by combining ARIMA and BP neural network models. Based on method of signal feature fusion, a single evaluation index is constructed that can reflect the changing law of the movement performance. The ARIMA model, BP model and combined model are compared in experiments to prove the combined model which has the smallest prediction error among three methods and has a good predictive effect.
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Lu, L., Li, Y., Li, G., Mitrouchev, P. (2022). Prediction of Remaining Life of Massage Chair Movement Based on ARIMA-BP Model. 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_12
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DOI: https://doi.org/10.1007/978-981-19-0572-8_12
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