Condition-Based Maintenance is pivotal in enabling the early detection of potential failures in engineering systems, where precise prediction of the Remaining Useful Life is essential for effective maintenance and operation. However, a predominant focus in the field centers on predicting the Remaining Useful Life using unprocessed or minimally processed data, frequently neglecting the intricate dynamics inherent in the dataset. In this work we introduce a novel methodology grounded in the analysis of statistical similarity within latent data from system components. Leveraging a specifically designed architecture based on a Vector Quantized Variational Autoencoder, we create a sequence of discrete vectors which is used to estimate system-specific priors. We infer the similarity between systems by evaluating the divergence of these priors, offering a nuanced understanding of individual system behaviors. The efficacy of our approach is demonstrated through experiments on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. Our validation not only underscores the potential of our method in advancing the study of latent statistical divergence but also demonstrates its superiority over existing techniques.

基于条件的维护是在工程系统中实现对潜在故障的早期检测至关重要的，而准确预测剩余寿命是有效维护和操作的关键。本研究引入了一种基于统计相似性分析潜在数据的新方法，借助基于向量量化变分自动编码器的特定设计架构，创建了一系列离散向量，用于估计系统特定的先验知识。通过评估这些先验知识的差异来推断系统之间的相似性，从而提供对个体系统行为的细致理解。借助对NASA商用模块化航空推进系统模拟(C-MAPSS)数据集的实验验证，我们不仅突出了该方法在推进潜在统计差异研究方面的潜力，还证明了其优于现有技术的优越性。

预测系统退化中潜在数据差异的意义