Abstract: A device and a method for nondestructively detecting a transient characteristic of a conductive screw of a turbo-generator rotor are provided. The device includes a personal computer (PC), an extremely-steep pulse generator, an ultra-high-frequency double-isolation transformer, and a pulse emitting and coupling module, which are connected in sequence. The pulse emitting and coupling module is connected to a load. A synchronous pulse receiving non-inductive divider circuit synchronously receives a characteristic waveform from the load, and the synchronous pulse receiving non-inductive divider circuit is connected to an ultra-high-speed analog/digital (A/D) module through a nonlinear saturation amplifying circuit that amplifies a signal. The PC receives a signal from the ultra-high-speed A/D module. The load includes a positive or negative excitation lead loop that is in a 180° symmetrical and instantaneous short-circuit state and a rotor shaft.

Abstract: An output layer is removed from a pre-trained neural network model and a neural capacitance probe unit with multiple layers is incorporated on top of one or more bottom layers of the pre-trained neural network model. The neural capacitance probe unit is randomly initialized and a modified neural network model is trained by fine-tuning the one or more bottom layers on a target dataset for a maximum number of epochs, the modified neural network model comprising the neural capacitance probe unit incorporated with multiple layers on top of the one or more bottom layers of the pre-trained neural network model. An adjacency matrix is obtained from the initialized neural capacitance probe unit and a neural capacitance metric is computed using the adjacency matrix. An active model is selected using the neural capacitance metric and a machine learning system is configured using the active model.