Maximal overlap discrete wavelet packet transforms and variants of neutrosophic cubic cross-entropy-based identification of rotor defects

Abstract

The underlying study proposes a novel procedure for automated testimony of rotor defects through maximal overlap discrete wavelet packet transforms (MODWPT) and the proposed neutrosophic cubic cross measure, fuzzy cross entropy and single valued neutrosophic cross entropy measures consecutively. Discrete wavelet transform is an efficient data acquisition technique, but the technical barrier with this technique is that it can decompose only low frequency (approximate) signals and does not possess shift invariance property. MODWPT possesses shift invariance property and is highly capable of decomposing both approximate and detail signals in time frequency analysis. After the acquisition of raw signals through MODWPT, the lower and upper bounds of normalized energy readings of various rotor defect condition (familiar and unfamiliar) are extracted and converted into the forms of fuzzy sets, single valued neutrosophic sets and neutrosophic cubic sets respectively. The minimum neutrosophic cubic cross-entropy measure as well as fuzzy and single valued neutrosophic cross entropy measure values between each rotor defect condition (familiar and unfamiliar) are computed and then utilized to identify various rotor defect conditions such as parallel and angular misalignment of 7 Mils, rub, unbalance and defect free respectively. The proposed variants of cross entropy measure are intelligent in identifying the desired rotor defect conditions in comparison with the existing cosine similarity measure, which exhibits unjustified and meaningless results during mathematical treatments.

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