# Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery

arXiv: https://arxiv.org/abs/1810.05644 · HTML: https://mlsanity.com/paper/1810.05644/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2018.

## Built on by (most cited first, 9 of 9)

- [Time-Series Regeneration with Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation](https://mlsanity.com/paper/2101.03678/) (2021)
- [Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation](https://mlsanity.com/paper/2203.16373/) (2022)
- [Lithium-ion Battery State of Health Estimation based on Cycle Synchronization using Dynamic Time Warping](https://mlsanity.com/paper/2109.13448/) (2021)
- [Transfer Learning-based State of Health Estimation for Lithium-ion Battery with Cycle Synchronization](https://mlsanity.com/paper/2208.11204/) (2022)
- [A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends](https://mlsanity.com/paper/2409.19629/) (2024)
- [Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent](https://mlsanity.com/paper/2402.01098/) (2024)
- [Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction](https://mlsanity.com/paper/2405.04336/) (2024)
- [Interaction models for remaining useful life estimation](https://mlsanity.com/paper/2301.05029/) (2023)
- [Liquid Latent State Dynamics for Interpretable Turbofan Degradation Modeling](https://mlsanity.com/paper/2607.01986/) (2026)

## Abstract (arXiv)

Accurately estimating the remaining useful life (RUL) of industrial machinery is beneficial in many real-world applications. Estimation techniques have mainly utilized linear models or neural network based approaches with a focus on short term time dependencies. This paper, introduces a system model that incorporates temporal convolutions with both long term and short term time dependencies. The proposed network learns salient features and complex temporal variations in sensor values, and predicts the RUL. A data augmentation method is used for increased accuracy. The proposed method is compared with several state-of-the-art algorithms on publicly available datasets. It demonstrates promising results, with superior results for datasets obtained from complex environments.