A Machine-Learning-Based Framework for Productive Locality Exploitation

Kayraklioglu, Engin; Favry, Erwan; El‐Ghazawi, Tarek

doi:10.1109/tpds.2021.3051348

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…The long-term component friction will cause the gap and position between the different components to be misaligned, resulting in the fact that the machine cannot be used normally. Kayraklioglu E et al pointed out that according to the current actual use of agricultural machinery, the most common failure causes in the current use of common agricultural machinery are: illegal work by operators, wear and tear of mechanical parts, corrosion and aging, and wear of parts, and electrical fault lights of agricultural machinery (Kayraklioglu E et al, 2021). Luz E et al introduced intelligent algorithm into equipment management of production workshop.…”

Section: Introductionmentioning

confidence: 99%

Research on Optimization of Agricultural Machinery Fault Monitoring System Based on Artificial Neural Network Algorithm

Zheng

et al. 2021

INMATEH

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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.

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Section: Introductionmentioning

confidence: 99%