Effective Latent Representation for Prediction of Remaining Useful Life

作     者：Qihang Wang Gang Wu 

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.