关键词: 时序信号分类, 神经架构搜索, 跨域特征增强, 廉价评估, 帕累托搜索

Abstract: Time series classification is a critical technology for intelligent decision-making systems, significantly enhancing system reliability and diagnostic accuracy. However, the task faces two major challenges when dealing with low-value-density time series signals: the inherent sparsity of discriminative features and low signal-to-noise ratio, and the prohibitive computational cost of traditional Neural Architecture Search (NAS) which requires full training of massive candidate models. To address these issues, a low-cost architecture search method was proposed. The discriminative power and robustness of input features were first enhanced through a cross-domain feature enhancement and dynamic selection mechanism. Then, a separable multi-scale search space and a Lightweight Pareto Architecture Search Strategy (LPAS) were designed to automatically balance model accuracy and efficiency. Furthermore, a Multi-dimensional Low-cost Proxy Evaluation Strategy (MLP-ES) was introduced, which rapidly assesses candidate models without complete training by quantifying their feature expressiveness, scalability, and trainability, thereby dramatically reducing computational overhead. Extensive experimental results on the Taiyuan Intelligent Detection and Information Processing (TIDIP) dataset of welding defects and fault diagnosis dataset from the Korea Advanced Institute of Science and Technology (KAIST) demonstrate that high-performance classification models are achieved with an average search time of only 1.5 hours, attaining accuracy rates exceeding 98%. The resulting models also exhibit significantly reduced parameter counts and inference times compared to MnasNet, Quant-NAS, WDD-NAS (Weld Defect Detection Neural Architecture Search) and other comparison methods. The number of parameters is reduced by an average of 19.9%, and the inference time is shortened by an average of 30.8%. These results validate the effectiveness of the approach in providing an efficient and automated architecture search solution for low-value-density time series classification tasks.

Key words: time series signal classification, Neural Architecture Search (NAS), cross-domain feature enhancement, low-cost evaluation, Pareto search