Reliability assessment based on time waveform characteristics with small sample: A practice inspired by few-shot learnings in metric space

Abstract

Time waveform characteristics of electromagnetic transients such as rise and fall time, duration or energy integral are vital criteria for reliability analysis, but it often meets the challenge to make an evaluation for the electronics of interest due to the lack of sufficient information from sampling and testing. Benefiting from the recent achievements on deep learning technique, in this paper a reliability assessment method for electronics is proposed based on neural network. The recurrent neural network (RNN) is involved to approximate the time waveform norm, so that the time-related characteristic can be extracted in metric space for comparison and classification. Inspired by the model-based few-shot learning strategy, a Siamese network architecture of two weights-sharing RNN is trained to avoid possible over-fitting. Artificial data representing various pulse waveforms are generated, with the help of which the approximation ability of RNN to two kinds of time waveform p-norms is analyzed and discussed in depth. To demonstrate the applicability of the proposed model, the lifetime stage of gas discharge tube (GDT) after cumulative discharge is experimentally investigated. The results are also compared with several informed evaluation model, and the proposed model is verified to able to yield the interpretable estimation in metric space and free from extra prior information.