自编码
深信不疑网络
人工智能
深度学习
预言
均方误差
计算机科学
特征(语言学)
人工神经网络
数据挖掘
机器学习
工程类
模式识别(心理学)
统计
数学
语言学
哲学
作者
Huthaifa Al-Khazraji,Ahmed R. Nasser,Ahmed Mudheher Hasan,Ammar K. Al Mhdawi,H. S. Al‐Raweshidy,Amjad J. Humaidi
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:10: 82156-82163
被引量:36
标识
DOI:10.1109/access.2022.3188681
摘要
Remaining Useful Life (RUL) is used to provide an early indication of failures that required performing maintenance and/or replacement of the system in advance. Accurate RUL prediction offers cost-effective operation for decision-makers in the industry. The availability of data using intelligence sensors leverages the power of data-driven methods for RUL estimation. Deep Learning is one example of a data-driven method that has a lot of applications in the industry. One of these applications is the RUL prediction where DL algorithms achieved good results. This paper presents an Autoencoder-based Deep Belief Network (AE-DBN) model for Aircraft engines’ RUL estimation. The AE-DBN DL model is utilized the feature extraction characteristic of AE and superiority in learning long-range dependencies of DBN. The efficiency of the proposed DL algorithm is evaluated by comparison between the proposed AE-DBRN and the state-of-the-art related method for RUL perdition for four datasets. Based on the Root Mean Square Error (RMSE) and Score indices, the outcomes reveal that the AE-DBN RUL prediction model is superior to other DL approaches.
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