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The Significance of Latent Data Divergence in Predicting System Degradation

Miguel Fernandes, Catarina Silva, Alberto Cardoso, Bernardete Ribeiro

TL;DR

This work tackles RUL prediction within Condition-Based Maintenance by shifting focus from raw-data forecasts to latent-data divergence. It introduces a Transformer-based encoder paired with a Vector Quantized VAE to produce a discrete latent space, from which system-specific priors are inferred via transition matrices and their steady-state distributions. By measuring the Jensen–Shannon divergence between these latent priors, the method identifies data-similar systems and predicts RUL through a nearest-neighbor-like aggregation of known RULs, achieving notable improvements on larger C-MAPSS subsets. The approach demonstrates the value of latent statistical dynamics for health assessment and degradation understanding, with practical implications for proactive maintenance in complex engineered systems.

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.

The Significance of Latent Data Divergence in Predicting System Degradation

TL;DR

This work tackles RUL prediction within Condition-Based Maintenance by shifting focus from raw-data forecasts to latent-data divergence. It introduces a Transformer-based encoder paired with a Vector Quantized VAE to produce a discrete latent space, from which system-specific priors are inferred via transition matrices and their steady-state distributions. By measuring the Jensen–Shannon divergence between these latent priors, the method identifies data-similar systems and predicts RUL through a nearest-neighbor-like aggregation of known RULs, achieving notable improvements on larger C-MAPSS subsets. The approach demonstrates the value of latent statistical dynamics for health assessment and degradation understanding, with practical implications for proactive maintenance in complex engineered systems.

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.
Paper Structure (24 sections, 19 equations, 2 figures, 1 table)

This paper contains 24 sections, 19 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem Statement
  3. Latent Variables
  4. Statistical Divergence
  5. Objective
  6. Preliminaries
  7. Transformers
  8. Positional Encodings
  9. Multi-Head Attention
  10. VQ-VAE
  11. Proposed Approach
  12. Model Architecture
  13. Encoder
  14. Latent Space
  15. Decoder
  16. ...and 9 more sections

Figures (2)

  • Figure 1: The architecture of the proposed model, consisting of three key components: 1) the encoder, responsible for processing the input data; 2) the vector quantization model, which discretizes the data into a latent representation; and 3) the decoder, tasked with reconstructing the output from the quantized data.
  • Figure 2: The results of the RUL predictions for the C-MAPSS testing dataset. (a) Prediction for the 100 testing systems in FD001. (b) Prediction for the 256 testing systems in FD002. (c) Prediction for the 100 testing systems in FD003. (d) Prediction for the 248 testing systems in FD004. In each subplot, the actual RULs are depicted by orange lines, while the estimated RULs are shown in blue.