The Significance of Latent Data Divergence in Predicting System Degradation

TL;DR

This paper introduces a novel approach using vector quantized variational autoencoders to analyze latent data divergence for predicting system degradation, demonstrating improved accuracy over existing methods on aerospace datasets.

Contribution

The study presents a new methodology that leverages latent data divergence analysis with VQ-VAE architecture to enhance system failure prediction accuracy.

Findings

Outperforms existing techniques on NASA C-MAPSS dataset

Provides nuanced understanding of system-specific behaviors

Demonstrates effectiveness of latent divergence in failure prediction

Abstract

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.