Transfer of learningmethod for industrial equipment diagnostics

Research of industrial rotating equipment failure diagnostics using transfer learning methods is conducted. An overview of modern neural network architectures with features of their application, advantages and disadvantages is presented. The problem of constant information deficitabout industrial equipment state and its negative impact on the results of diagnostic models is designated. Keywords transfer learning, predictive diagnostics, data sets, domain adaptation, domain generalization, rotating equipment, neural network architectures