Predictive Analysis of a Lift Motor Using Autoregressive Integrated Moving Average (ARIMA) Model for Vibration-Based Condition Monitoring-Reference-Cited by-同舟云学术

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Predictive Analysis of a Lift Motor Using Autoregressive Integrated Moving Average (ARIMA) Model for Vibration-Based Condition Monitoring

Published:2024 Issue: Volume: Page:15-23
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:en
Short-container-title:

Author:

Rahim Sharafiz^ORCID,Dehghani Adnan,Md Rezali Khairil Anas^ORCID,Abidin Abdul Murad Zainal,Rashid Zamil Hisham Bin Abdul,Khalid Siti Nor Azila,bin Mohd Azahar,Yunus Mohamad Fikri Bin Mohamad

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-97-3851-9_2

Reference9 articles.

1. Yin S, Li X, Gao H, Kaynak O (2015) Data-based techniques focused on modern industry: an overview. IEEE Trans Ind Electron 62(1):657–667. https://doi.org/10.1109/TIE.2014.2308133

2. Law Jing, S., Jacqueline, L., Lau Chee, Y.: Predictive maintenance on an elevator system using machine learning. J Appl Technol Innov 5(1). e-ISSN 2600–7304

3. Rahim SA, Manson G (2021) Kernel principal component analysis for structural health monitoring and damage detection of an engineering structure under operational loading variations. J Fail Anal Prev 21:1981–1990. https://doi.org/10.1007/s11668-021-01260-1

4. Li J, Xie J, Yang Z, Li J (2018) Fault diagnosis method for a mine hoist in the internet of things environment. Sensors 18:1920. https://doi.org/10.3390/s18061920

5. He W, Chen J, Zhou Y, Liu X, Chen B, Guo B (2022) An intelligent machinery fault diagnosis method based on GAN and transfer learning under variable working conditions. Sensors 22:9175. https://doi.org/10.3390/s22239175

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