数据集构建方式是影响电气设备地震响应时序预测模型性能的关键因素之一。为建立更加科学合理的地震响应预测数据集构建流程, 该文首先系统研究了地震动选波、调幅策略和样本数量配置3类数据集构建要素对时序预测模型性能的影响; 其次, 以500 kV变压器套管为例, 建立了有限元模型, 并结合振动台试验进行了验证; 再次, 设计了包含谱形匹配与仅考虑场地类型的随机选波、常规与过范围调幅和不同样本数量配置的策略组合, 并采用典型递推长短时记忆神经网络开展了数值试验; 最后, 提出了数据集推荐性构建策略, 并基于典型测试样本对比了经验性构建策略, 验证了所提方法的有效性。结果表明: 谱形匹配选波方式与过范围固定调幅策略组合, 可显著提升模型预测精度; 随机调幅策略虽应用较广, 但效果较差; 训练样本数量对模型性能的提升作用存在边际效应。该文研究结果可为电气设备地震响应时序预测模型的数据集构建和电气设备震后的智能化评估提供参考。

Objective: Electrical equipment is highly vulnerable to seismic hazards. Acquiring accurate seismic response data is crucial for post-earthquake damage assessment and emergency decision-making. Although conventional contact sensors are effective for capturing such data, they cannot be widely deployed on the equipment body due to monitoring constraints. Therefore, seismic response prediction methods based on time-series neural networks must be developed. Most existing studies have emphasized the optimization of neural network architectures, and dataset construction strategies have not been systematically and sufficiently investigated. Dataset construction directly influences the fitting accuracy and generalization ability of predictive models, ultimately determining the overall predictive performance. In this study, the effects of three key elements of dataset construction-ground-motion selection, amplitude scaling of records, and sample-size configuration-on the performance of time-series models were evaluated, and a scientifically grounded dataset construction workflow was proposed. Methods: A 500-kV transformer bushing was selected as the case study. A refined finite element model was developed and validated by shaking-table tests to determine its accuracy in terms of dynamic characteristics and response behavior, and the acceleration at the top oil reservoir was chosen as the prediction target. Two ground-motion selection strategies were adopted for dataset construction: spectrum-matched records and random selection constrained only by site type. Four amplitude scaling strategies were examined: conventional random, conventional fixed, extended-range random, and extended-range fixed scaling. Five sample-size levels of 80, 100, 120, 140, and 160 records were also configured to form multiple strategy combinations. A recursive long short-term memory neural network was used as the representative prediction model. Its performance was assessed based on mean squared error and peak response error, and repeated sampling and multiple independent training runs were performed to mitigate stochastic variability. Results: Spectrum-matched selection outperformed random selection based solely on the site type, yielding lower overall prediction errors in seismic response time series. Fixed scaling was superior to random scaling, and the introduction of extended-range scaling further enhanced the peak prediction accuracy. Although commonly used, random scaling considerably reduced the overall and peak prediction performance of the model and was not recommended for seismic response time-series prediction. Increasing the number of training samples improved the model accuracy, but marginal gains were observed at a sample size of 120-140 records. The combination of spectrum-matched selection and extended-range fixed scaling was the most effective strategy. Comparative tests with representative ground-motion records further confirmed that this strategy surpassed commonly used empirical approaches in terms of fitting accuracy, peak prediction capability, and training stability; it also enabled more accurate capture of abrupt response transitions and reduced phase errors. Conclusions: A recommended dataset construction workflow is proposed for the time-series prediction of electrical equipment modeled as linear elastic systems. The proposed process integrates finite element modeling and validation, spectrum-matched ground-motion selection, extended-range fixed scaling, and balanced sample-size configuration. The findings confirm that this workflow considerably improves both the prediction accuracy and overall stability of the model, offering systematic methodological support and practical engineering guidance for post-earthquake emergency assessment and response monitoring of electrical equipment. This approach can also be extended to other structural systems where dataset construction critically affects the model performance.