Symmetric Neutrosophic Cross Entropy Based Fault Recognition of Turbine

Abstract

This study introduces a novel fault recognition methodology for turbine faults through symmetric trigonometric fuzzy and neutrosophic cross entropy measures (FCEM and NCEM) consequently. After knowing the nethermost (lowest) and uppermost (highest) energy bounds of each real fault conditions, the energy interval ranges are constructed and then transformed into the form of single valued neutrosophic (SN) sets. Thereafter, the proposed symmetric trigonometric cross -entropy measures are deployed to recognize faults of turbine. The nethermost FCEM and NCEM values between familiar and unfamiliar fault conditions indicates that the unfamiliar fault condition is closer to the familiar one. The applicability of the proposed methodology is validated by
taking into consideration the example of fault diagnosis of turbine. The repercussions of this study yield that the proposed symmetric trigonometric FCEM and NCEM cannot only recognize optimal fault, they can also provide meaningful and remarkable fault information. A comparison of the underlying FCEM and NCEM (based on SN sets) with the enduring cosine measures (based on vague sets) conclude that the latter sets may hide some fruitful fault information., when experimented under sensitive and intuitive criteria and thus resulting an incomplete fault evaluation criterion.