# Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes

arXiv: https://arxiv.org/abs/2405.06068 · HTML: https://mlsanity.com/paper/2405.06068/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 9 May 2024.

## Builds on (most cited first, 7 of 7)

- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [Remaining Useful Lifetime Prediction via Deep Domain Adaptation](https://mlsanity.com/paper/1907.07480/) (2019)
- [Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder](https://mlsanity.com/paper/1608.06154/) (2016)
- [Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction](https://mlsanity.com/paper/2302.01704/) (2023)
- [A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data](https://mlsanity.com/paper/2207.11353/) (2022)
- [Federated Multilinear Principal Component Analysis with Applications in Prognostics](https://mlsanity.com/paper/2312.06050/) (2023)

## Built on by (most cited first, 2 of 2)

- [Prognostics for Autonomous Deep-Space Habitat Health Management under Multiple Unknown Failure Modes](https://mlsanity.com/paper/2411.12159/) (2024)
- [A Multi-head Attention Fusion Network for Industrial Prognostics under Discrete Operational Conditions](https://mlsanity.com/paper/2604.10248/) (2026)

## Abstract (arXiv)

Industrial prognostics focuses on utilizing degradation signals to forecast and continually update the residual useful life of complex engineering systems. However, existing prognostic models for systems with multiple failure modes face several challenges in real-world applications, including overlapping degradation signals from multiple components, the presence of unlabeled historical data, and the similarity of signals across different failure modes. To tackle these issues, this research introduces two prognostic models that integrate the mixture (log)-location-scale distribution with deep learning. This integration facilitates the modeling of overlapping degradation signals, eliminates the need for explicit failure mode identification, and utilizes deep learning to capture complex nonlinear relationships between degradation signals and residual useful lifetimes. Numerical studies validate the superior performance of these proposed models compared to existing methods.