# Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder

arXiv: https://arxiv.org/abs/1608.06154 · HTML: https://mlsanity.com/paper/1608.06154/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2016.

## Built on by (most cited first, 11 of 11)

- [A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies](https://mlsanity.com/paper/2208.14197/) (2022)
- [Dual Aspect Self-Attention based on Transformer for Remaining Useful Life Prediction](https://mlsanity.com/paper/2106.15842/) (2021)
- [A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic](https://mlsanity.com/paper/2204.06362/) (2022)
- [Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management](https://mlsanity.com/paper/2110.04256/) (2021)
- [Learning Informative Health Indicators Through Unsupervised Contrastive Learning](https://mlsanity.com/paper/2208.13288/) (2022)
- [Health Index Estimation Through Integration of General Knowledge with Unsupervised Learning](https://mlsanity.com/paper/2405.04990/) (2024)
- [Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes](https://mlsanity.com/paper/2405.06068/) (2024)
- [Anomaly Detection in Beehives: An Algorithm Comparison](https://mlsanity.com/paper/2110.03945/) (2021)
- [Uncertainty-Aware Deep Learning Framework for Remaining Useful Life Prediction in Turbofan Engines with Learned Aleatoric Uncertainty](https://mlsanity.com/paper/2511.19124/) (2025)
- [Interaction models for remaining useful life estimation](https://mlsanity.com/paper/2301.05029/) (2023)
- [INCLAIR: Inception-Based Longitudinal Clinical Anomaly Detection with Informed Reasoning](https://mlsanity.com/paper/2607.27487/) (2026)

## Abstract (arXiv)

Many approaches for estimation of Remaining Useful Life (RUL) of a machine, using its operational sensor data, make assumptions about how a system degrades or a fault evolves, e.g., exponential degradation. However, in many domains degradation may not follow a pattern. We propose a Long Short Term Memory based Encoder-Decoder (LSTM-ED) scheme to obtain an unsupervised health index (HI) for a system using multi-sensor time-series data. LSTM-ED is trained to reconstruct the time-series corresponding to healthy state of a system. The reconstruction error is used to compute HI which is then used for RUL estimation. We evaluate our approach on publicly available Turbofan Engine and Milling Machine datasets. We also present results on a real-world industry dataset from a pulverizer mill where we find significant correlation between LSTM-ED based HI and maintenance costs.