T present, a variety of investigation on the RUL prediction of parts have reported [6], and approaches of RUL prediction is usually roughly grouped into three categories. The very first category is definitely the prediction technique determined by physical models, which estimates the RUL of parts in line with the 5′-O-DMT-2′-O-TBDMS-Ac-rC manufacturer degradation mechanism. Leser et al. [9] validated the crack growth modeling system applying harm diagnosis data determined by structural well being monitoring, in addition to a probabilistic prediction of RUL is formed for a metallic, singleedge notch tension specimen with a fatigue crack growing below mixedmode situations. Habib et al. [10] evaluated the stress of A310 aircraft wings throughout each loading cycle via a finite element evaluation, and they predicted the RUL of A310 wings working with the Paris Law method according to linear elastic fracture mechanics. Chen et al. [11] created a novel computational modelling technique for the prediction of crack growth in load bearing orthopaedic alloys subjected to fatigue loading, which can predict the RUL of components via the crack path. The second category is the prediction process determined by probability statistics, which match the failure data of components to receive the characteristic distribution of life through a statistical distribution model. Wang et al. [12] proposed a novel technique determined by the threeparameterPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Copyright: 2021 by the authors. Licensee MDPI, Basel, Switzerland. This short article is an open access short article distributed beneath the terms and situations from the Inventive Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).Appl. Sci. 2021, 11, 8482. https://doi.org/10.3390/apphttps://www.mdpi.com/journal/applsciAppl. Sci. 2021, 11,2 ofWeibull distribution proportional hazards model to predict the RUL of rolling bearings, the model is able to make accurate RUL predictions for the tested bearings and outperforms the popular twoparameter model. Pan et al. [13] proposed a remanufacturability evaluation scheme determined by the average RUL in the structural arm, and made a comprehensive evaluation by establishing the reliability parameter model with the structural arm. Xu et al. [14] discussed the influence of unique distribution function values on the prediction outcomes by BSc5371 Biological Activity analyzing unique parameter estimation techniques, and established the RUL prediction model depending on the failure data of components. Rong et al. [15] determined the average helpful life of the pump truck boom determined by the Weibull distribution function by utilizing the failure data, and predicted the RUL of the boom by utilizing the utilized time. The third category may be the datadriven prediction strategy. Ren et al. [16] analyzed the timedomain and frequencydomain traits of rolling bearing vibration signals, and established the RUL prediction model of rolling bearing determined by deep neural networks. Liu et al. [17] proposed an RUL prediction framework based on multiple wellness state assessments that divide the whole bearing life into various health states, exactly where a nearby regression model is often built individually. Zio et al. [18] proposed a methodology for the estimation in the RUL of components determined by particle filtering. Sun et al. [19] applied assistance vector machines to develop degradation models for bearing RUL prediction. Maio et al. [20] proposed a combination of a relevance vector machine and model fitting as a prognostic procedure for estimati.
