用自回归模型增强传感器时序数据，提升故障预测性能。
Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics
- 基于时变自回归模型生成合成数据，解决数据不足问题。
- 在CMAPSS数据集上显著提升故障预测准确率。
- 适合数据稀缺场景下的智能维护系统研发者使用。
本文提出一种针对非平稳多变量时间序列的新型数据增强方法，并应用于故障预测。该方法扩展了作者先前基于时变自回归过程的工作，能够从少量样本中提取关键信息，并生成新的合成样本，从而潜在提升故障预测与健康管理（PHM）方案的性能。在数据稀缺的故障预测场景中尤为有效，这在实际应用中极为常见。所提方法在常用的CMAPSS数据集上进行验证，采用PHM领域中的AutoML方法自动化设计预测解决方案。实验结果表明，该方法可显著提升PHM系统的性能。
原文摘要 · Abstract (English)
This work presents a novel data augmentation solution for non-stationary multivariate time series and its application to failure prognostics. The method extends previous work from the authors which is based on time-varying autoregressive processes. It can be employed to extract key information from a limited number of samples and generate new synthetic samples in a way that potentially improves the performance of PHM solutions. This is especially valuable in situations of data scarcity which are very usual in PHM, especially for failure prognostics. The proposed approach is tested based on the CMAPSS dataset, commonly employed for prognostics experiments and benchmarks. An AutoML approach from PHM literature is employed for automating the design of the prognostics solution. The empirical evaluation provides evidence that the proposed method can substantially improve the performance of PHM solutions.
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