Effective Latent Representation for Prediction of Remaining Useful Life
作者机构:Shanghai Jiaotong UniversityShanghai200240China
出 版 物:《Computer Systems Science & Engineering》 (计算机系统科学与工程(英文))
年 卷 期:2021年第36卷第1期
页 面:225-237页
核心收录:
学科分类:0710[理学-生物学] 08[工学] 0714[理学-统计学(可授理学、经济学学位)] 0701[理学-数学] 0812[工学-计算机科学与技术(可授工学、理学学位)]
主 题:Deep learning predictive maintenance remaining useful life
摘 要:AI approaches have been introduced to predict the remaining useful life(RUL)of a machine in modern industrial *** apply them well,challenges regarding the high dimension of the data space and noisy data should be met to improve model efficiency and *** this study,we propose an end-toend model,termed ACB,for RUL predictions;it combines an autoencoder,convolutional neural network(CNN),and bidirectional long short-term memory.A new penalized root mean square error loss function is included to avoid an overestimation of the *** the CNN-based autoencoder,a high-dimensional data space can be mapped into a lower-dimensional latent space,and the noisy data can be greatly *** compared ACB with five state-of-the-art models on the Commercial Modular Aero-Propulsion System Simulation *** model achieved the lowest score value on all four *** robustness of our model to noise is also supported by the experiments.