Changchun Institute of Optics,Fine Mechanics and Physics,CAS
| In-Situ early anomaly detection and remaining useful lifetime prediction for high-power white LEDs with distance and entropy-based long short-term memory recurrent neural networks | |
| M. Z. Wen, M. S. Ibrahim, A. H. Meda, G. Q. Zhang and J. J. Fan | |
| 2024 | |
| 发表期刊 | Expert Systems with Applications
 |
| ISSN | 0957-4174 |
| 卷号 | 238页码:14 |
| 摘要 | High-power white light-emitting diodes (LEDs) have demonstrated superior efficiency and reliability compared to traditional white light sources. However, ensuring maximum performance for a prolonged lifetime use presents a significant challenge for manufacturers and end users, especially in safety-critical applications. Thus, identifying functional anomalies and predicting the remaining useful lifetime (RUL) is of enormous importance in the operational longevity of the device. To address such challenges, this study proposes a combination of distance-based Mahalanobis distance (MD), entropy generation rate (EGR), and deep learning models for improved anomaly detection and RUL prediction accuracy. Unlike conventional health indicators based on luminous flux data that are challenging to monitor relevant optical performance, the MD and EGR methods are employed to extract in-situ monitored thermal and electrical data as new health indicators. Long short-term memory recurrent neural networks (LSTM-RNN) and convolutional neural networks (CNN) are established to detect anomalies and predict the RUL. The accelerated degradation tests of 3 W high-power white LED have been conducted, and the online and offline collected experimental data are deployed for model development and performance evaluation. The performance of the proposed methods is compared against the Illuminating Engineering Society of North America (IESNA) TM-21 method. The results indicate that LSTM-RNN, when combined with either MD or EGR, can detect anomalies with significantly fewer data (70 %) than is typically required. Furthermore, a significant improvement in prediction accuracy in RUL prediction based on MD and EGRconstructed time series health indicators and employed with the LSTM-RNN model demonstrates the effectiveness of the proposed methods. |
| DOI | 10.1016/j.eswa.2023.121832 |
| URL | 查看原文 |
| 收录类别 | sci |
| 语种 | 英语 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ciomp.ac.cn/handle/181722/68016 |
| 专题 | 中国科学院长春光学精密机械与物理研究所 |
| 推荐引用方式 GB/T 7714 | M. Z. Wen, M. S. Ibrahim, A. H. Meda, G. Q. Zhang and J. J. Fan. In-Situ early anomaly detection and remaining useful lifetime prediction for high-power white LEDs with distance and entropy-based long short-term memory recurrent neural networks[J]. Expert Systems with Applications,2024,238:14. |
| APA | M. Z. Wen, M. S. Ibrahim, A. H. Meda, G. Q. Zhang and J. J. Fan.(2024).In-Situ early anomaly detection and remaining useful lifetime prediction for high-power white LEDs with distance and entropy-based long short-term memory recurrent neural networks.Expert Systems with Applications,238,14. |
| MLA | M. Z. Wen, M. S. Ibrahim, A. H. Meda, G. Q. Zhang and J. J. Fan."In-Situ early anomaly detection and remaining useful lifetime prediction for high-power white LEDs with distance and entropy-based long short-term memory recurrent neural networks".Expert Systems with Applications 238(2024):14. |
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| In-Situ early anomal（5509KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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