Identification method of abnormal working condition of equipment based on big data operation

Chao Chai; Shaokun Jia; Songyang Liu; Ying Li; Aishan Hao

doi:10.1117/12.3050717

12 December 2024 Identification method of abnormal working condition of equipment based on big data operation

Chao Chai, Shaokun Jia, Songyang Liu, Ying Li, Aishan Hao

Author Affiliations +

Proceedings Volume 13419, Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024); 134193X (2024) https://doi.org/10.1117/12.3050717
Event: Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024), 2024, Lhasa, China

Abstract

In order to reduce the frequency of equipment failure during operation, the decision tree algorithm is introduced to carry out research on the design of recognizing abnormal working conditions of equipment operation based on big data. Sensors, instruments and monitoring systems are used to perceive the operating conditions of equipment, and based on this, the collection and pre-processing of equipment operating data are realized: data in different formats and from different sources are converted and integrated to realize metadata feature extraction and category classification based on the decision tree; spatial clustering is performed on data of the same category to realize data clustering and anomaly data identification. Anomaly identification is important for reducing the frequency of equipment failure.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chao Chai, Shaokun Jia, Songyang Liu, Ying Li, and Aishan Hao "Identification method of abnormal working condition of equipment based on big data operation", Proc. SPIE 13419, Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024), 134193X (12 December 2024); https://doi.org/10.1117/12.3050717

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE