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.

翻译：本研究提出了一种针对非平稳多变量时间序列的新型数据增强方法及其在故障预测中的应用。该方法扩展了作者先前基于时变自回归过程的研究工作，能够从有限样本中提取关键信息，并以可能提升PHM解决方案性能的方式生成新的合成样本。这对于PHM中常见的数据稀缺场景（尤其在故障预测领域）具有重要价值。所提方法基于CMAPSS数据集进行验证，该数据集是预测实验与基准测试的常用数据集。研究采用PHM文献中的AutoML方法实现预测解决方案的自动化设计。实证评估表明，所提方法能够显著提升PHM解决方案的性能。