Notice of Violation of IEEE Publication Principles: 1D CNN based fault diagnosis of switching devices and identification of fault for MMC

Notice of Violation of IEEE Publication Principles

"1D CNN based fault diagnosis of switching devices and identification of fault for MMC,"
by S. S. Gote and P. N. Korde,
in the Proceedings of the Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2020, pp. 454-458

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.

This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

“Real-Time Fault Detection and Identification for MMC using 1D Convolutional Neural Networks”
by S. Kiranyaz, A. Gastli, L. Ben-Brahim, N. Alemadi, and M. Gabbouj,
in IEEE Transactions on Industrial Electronics, May 2, 2018


Automated early acknowledgment and obvious affirmation of switch needs are major in high-voltage applications. For such applications, MMC is another and auspicious analysis situs. MMC is made out of different ill-defined controlled voltage sources called modules or cells. Each cell may have in any event one keys & a key frustration may happen in any one of these cells. The predictable state-run of the mill & blemish lead of a cell voltage will in like manner move according to the modifications in the load current & the inadequacy timing. This makes it an inciting issue to perceive and recognize such accuses. In this evaluation, a consistent and exceptionally careful MMC circuit checking framework for the early insufficiency region and perceiving affirmation utilizing adaptable CNNs. The suggested framework is legitimate to the unpleasant voltage and current information and hence takes out the essential for any part extraction figuring, understanding an inconceivably competent and solid structure. A proliferation result got utilizing a 9-cell, 18-switch MMC analysis situs show that the proposed framework has a highly determined quality to keep up a key decent ways from any phony alert and accomplishes a disclosure likelihood of 0.989, and run of the mill particular affirmation likelihood of 0.997 in less than 100 MS.