ABSTRACT
In the existing bearing remaining useful life (RUL)-prediction model based on deep learning, the advantages and disadvantages of the extracted features are evaluated by the prediction accuracy; thus, the analytical ability of the features is poor. At the same time, the change of working conditions has a great influence on prediction accuracy. To overcome these limitations, a prediction method of bearing RUL based on feature evaluation and deep transfer learning is proposed. The proposed model can solve the above problems: (1) a method of feature evaluation and selection for bearing life prediction based on trend consistency index was designed. (2) In this study, a domain adversarial transfer model based on feature condition mapping is proposed to overcome the second limitation. Experimental results show that this method is superior to the existing bearing evaluation and prediction methods.
ABSTRACT
A tissue-equivalent proportional counter (TEPC) is a reference detector to measure microdosimetric quantities. A conventional spherical TEPC and a novel TEPC based on a ceramic thick gas electron multiplier (THGEM) foil were developed to carry out microdosimetric measurements of lineal energy spectra in monoenergetic and 252Cf/241Am-Be neutron radiation fields, and the absorbed dose values had been derived. In order to go further in radiobiology and therapy, the fluence to absorbed dose conversion coefficients in neutron fields were also determined. According to the dose distribution in lineal energy, the neutron relative biological effect (RBE) values were also calculated using an empirical procedure applying biological weighting functions.
