RESEARCH ON OPTIMIZATION OF AGRICULTURAL MACHINERY FAULT MONITORING SYSTEM BASED ON ARTIFICIAL NEURAL NETWORK ALGORITHM-Reference-Cited by-同舟云学术

RESEARCH ON OPTIMIZATION OF AGRICULTURAL MACHINERY FAULT MONITORING SYSTEM BASED ON ARTIFICIAL NEURAL NETWORK ALGORITHM

Published:2021-08-31 Issue: Volume: Page:297-306
ISSN:2068-2239
Container-title:INMATEH Agricultural Engineering
language:en
Short-container-title:INMATEH

Author:

Zheng Jiaxin¹,Li Mei²,Hu Shikang³,Xiao Xuwen²,Li Hao²,Li Wenfeng³

Affiliation:

1. Engineering Centre of Yunnan Colleges and Universities for Plateau Characteristic Modern Agricultural Equipment, Yunnan Agricultural University, Kunming / China;The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Yunnan Agricultural University, Kunming / China; Yunnan Plateau Characteristic Agricultural Industry Research Institute, Kunming, 650201 / China

2. Engineering Centre of Yunnan Colleges and Universities for Plateau Characteristic Modern Agricultural Equipment, Yunnan Agricultural University, Kunming / China

3. Engineering Centre of Yunnan Colleges and Universities for Plateau Characteristic Modern Agricultural Equipment, Yunnan Agricultural University, Kunming / China; The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Yunnan Agricultural University, Kunming / China

Abstract

Aiming at the demand of mileage statistics, work area statistics, fault site return and related data automatic retention in the current agricultural machinery reliability appraisal process, the optimization of agricultural machinery video monitoring system based on artificial neural network algorithm was studied. Together with the new video monitoring technology, the agricultural machinery GPS, GSM and fuel consumption recorder technology are combined to realize the functions of real-time data transmission, monitoring, analysis and statistics. Aiming at intelligent fault analysis, a real-time online detection mechanism is proposed, and a cloud collaborative detection mechanism is proposed to solve the problem of inaccurate offline model detection. Use plane map or satellite map to browse. Thus, an online monitoring and visual testing platform for agricultural machinery faults without real-time monitoring records is established. Finally, the test platform is tested and applied. Test results show that the algorithm can greatly shorten the training time and improve the accuracy of training model detection. With the increase of online training iterations, it is helpful to improve the detection accuracy of the generated model. In a word, the system service platform can provide scientific and transparent data for agricultural machinery fault identification, ensure the scientific, open and fair principles of agricultural machinery fault identification, and greatly improve the efficiency of agricultural machinery management.

Publisher

INMA Bucharest-Romania

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering,Food Science

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