Research on Fault Prediction and Diagnosis of Mechanical and Electrical Equipment in Construction Machinery based on Big Data Technology
DOI:
https://doi.org/10.54097/gnt59b95Keywords:
Big Data Technology; Mechanical and Electrical Equipment; Fault Prediction; Fault Diagnosis.Abstract
This article aims to explore how to use big data technology for fault prediction and diagnosis of mechanical and electrical equipment in construction machinery. Through steps such as data collection and storage, data feature extraction and selection, and data analysis and processing, a fault prediction and diagnosis model is constructed to achieve real-time monitoring and early warning. The accuracy and efficiency of fault prediction are improved through decision feedback and algorithm optimization.
Downloads
References
[1] Tang Xiufang. Prediction and Diagnosis of Mechanical and Electrical Equipment Operation Faults Based on Big Data Analysis [J] New technologies and products in China. 2021 (2):86-88.
[2] Zhang Jianpeng, Zhang Dongsheng, Zhong Hua. Fault diagnosis method for electromechanical equipment based on deep learning theory [J] Mechanical and Electrical Information. 2020, 617(11): 58-60.
[3] Zhang Zhongchao, Jiang Zhimin, Cheng Mengcheng. Research on the Analysis of Power Equipment Operation Data Based on Big Data [J]. China Equipment Engineering.2024(02):8-10.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Academic Journal of Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.