Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155878
Title: Machine learning for predictive maintenance of distributed systems
Authors: Liu, Yuxuan
Keywords: Engineering::Electrical and electronic engineering::Electronic systems
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Liu, Y. (2021). Machine learning for predictive maintenance of distributed systems. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/155878
Abstract: Evidently, accurate remaining useful life estimation provides numerous benefits in a wide range of real-world applications and industrial settings. Using multi-channel sensing signals, this project investigated a new deep convolutional neural network-based architecture for estimating the remaining useful life of systems. In the proposed deep architecture, an innovative convolutional neural network with 2-dimensional input and 1-dimensional convolution and pooling layer structure is used to better capture notable patterns of sensor inputs at multiple time scales. All detected degrading patterns are methodically integrated in the estimate model and eventually help to estimate the remaining time. The proposed algorithm's performance on the Commercial Modular Aero Propulsion System Simulation (C-MAPSS) dataset is evaluated, and the outcome predictions show that it can achieve high accuracy when compared to some state-of-the-art regression-based models.
URI: https://hdl.handle.net/10356/155878
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
Dissertation_Yuxuan Liu (Final).pdf
  Restricted Access
1.05 MBAdobe PDFView/Open

Page view(s)

42
Updated on May 19, 2022

Download(s)

6
Updated on May 19, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.