Machine learning for predictive maintenance of distributed systems

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 u...

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Main Author: Liu, Yuxuan
Other Authors: Tan Yap Peng
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/155878
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1558782023-07-04T16:14:58Z Machine learning for predictive maintenance of distributed systems Liu, Yuxuan Tan Yap Peng School of Electrical and Electronic Engineering EYPTan@ntu.edu.sg Engineering::Electrical and electronic engineering::Electronic systems 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. Master of Science (Communications Engineering) 2022-03-24T07:09:54Z 2022-03-24T07:09:54Z 2021 Thesis-Master by Coursework Liu, Y. (2021). Machine learning for predictive maintenance of distributed systems. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/155878 https://hdl.handle.net/10356/155878 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering::Electronic systems
spellingShingle Engineering::Electrical and electronic engineering::Electronic systems
Liu, Yuxuan
Machine learning for predictive maintenance of distributed systems
description 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.
author2 Tan Yap Peng
author_facet Tan Yap Peng
Liu, Yuxuan
format Thesis-Master by Coursework
author Liu, Yuxuan
author_sort Liu, Yuxuan
title Machine learning for predictive maintenance of distributed systems
title_short Machine learning for predictive maintenance of distributed systems
title_full Machine learning for predictive maintenance of distributed systems
title_fullStr Machine learning for predictive maintenance of distributed systems
title_full_unstemmed Machine learning for predictive maintenance of distributed systems
title_sort machine learning for predictive maintenance of distributed systems
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/155878
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