# A Hybrid Deep Learning Model-based Remaining Useful Life Estimation for Reed Relay with Degradation Pattern Clustering

arXiv: https://arxiv.org/abs/2209.06429 · HTML: https://mlsanity.com/paper/2209.06429/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 14 Sep 2022.

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

- [Time-Series Regeneration with Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation](https://mlsanity.com/paper/2101.03678/) (2021)
- [A Transfer Learning-based State of Charge Estimation for Lithium-Ion Battery at Varying Ambient Temperatures](https://mlsanity.com/paper/2101.03704/) (2021)
- [Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation](https://mlsanity.com/paper/2203.16373/) (2022)
- [AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes](https://mlsanity.com/paper/2008.12987/) (2020)
- [IIoT-Enabled Health Monitoring for Integrated Heat Pump System Using Mixture Slow Feature Analysis](https://mlsanity.com/paper/2104.09876/) (2021)
- [A deep learning-based remaining useful life prediction approach for bearings](https://mlsanity.com/paper/1812.03315/) (2018)
- [Transfer Learning-based State of Health Estimation for Lithium-ion Battery with Cycle Synchronization](https://mlsanity.com/paper/2208.11204/) (2022)

## Built on by (most cited first, 1 of 1)

- [Digital Twin for Real-time Li-ion Battery State of Health Estimation with Partially Discharged Cycling Data](https://mlsanity.com/paper/2212.04622/) (2022)

## Abstract (arXiv)

Reed relay serves as the fundamental component of functional testing, which closely relates to the successful quality inspection of electronics. To provide accurate remaining useful life (RUL) estimation for reed relay, a hybrid deep learning network with degradation pattern clustering is proposed based on the following three considerations. First, multiple degradation behaviors are observed for reed relay, and hence a dynamic time wrapping-based K-means clustering is offered to distinguish degradation patterns from each other. Second, although proper selections of features are of great significance, few studies are available to guide the selection. The proposed method recommends operational rules for easy implementation purposes. Third, a neural network for remaining useful life estimation (RULNet) is proposed to address the weakness of the convolutional neural network (CNN) in capturing temporal information of sequential data, which incorporates temporal correlation ability after high-level feature representation of convolutional operation. In this way, three variants of RULNet are constructed with health indicators, features with self-organizing map, or features with curve fitting. Ultimately, the proposed hybrid model is compared with the typical baseline models, including CNN and long short-term memory network (LSTM), through a practical reed relay dataset with two distinct degradation manners. The results from both degradation cases demonstrate that the proposed method outperforms CNN and LSTM regarding the index root mean squared error.