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Sensors and Materials, Volume 31, Number 11(2) (2019)
Copyright(C) MYU K.K.
pp. 3599-3608
S&M2034 Research Paper of Special Issue
Published: November 15, 2019

Applications of Expert Diagnosis Learning Defense System with Technology of Cloud Sensors to Enhance the Reliabilities of Machines [PDF]

Wen-Jie Zheng, Tzu-Hung Chang, Ming Li, and Cheng-Fu Yang

(Received April 25, 2019; Accepted October 18, 2019)

Keywords: expert diagnosis learning defense (EDLD) system, microcontroller unit (MCU), cloud sensors (CS)

The expert diagnosis learning defense (EDLD) system has self-learning capability; thus it can be used to enhance the fault defense capabilities and improve the reliabilities of machines and equipment. In this study, we designed and fabricated a group of the EDLD system modules, that could apply the technology of cloud sensors to achieve the function of automatic defense of failure. If the failure problems of machines and equipment were found by a multifunction sensor and noted immediately, all the operation reliabilities of machines and equipment could be controlled. Even if the numbers of machines and equipment were more than the numbers of engineers, the machines and equipment could also be handled easily. The EDLD system would collect much data (or so-called as big data), which were detected by numerous microsystems. Each of the microsystems was composed of several cloud sensors, one microcontroller unit (MCU) card, and one wireless communication card to achieve the function of multidetection. Each of the microsystems had their own monitoring regions, and if it was found that the detective values of monitoring regions were beyond the warning or admonished ones, the messages were sent automatically and immediately by the EDLD system, and the engineers would be notified to troubleshoot the problems. The monitoring process is convenient and simple because if the equipment or machines suddenly lost their functions, the EDLD system with the technology of cloud sensors can greatly reduce the loads of the engineers.

Corresponding author: Tzu-Hung Chang and Cheng-Fu Yang

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Cite this article
Wen-Jie Zheng, Tzu-Hung Chang, Ming Li, and Cheng-Fu Yang, Applications of Expert Diagnosis Learning Defense System with Technology of Cloud Sensors to Enhance the Reliabilities of Machines, Sens. Mater., Vol. 31, No. 11, 2019, p. 3599-3608.

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