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

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

Wen, Minzhen (Fudan University)
Ibrahim, Mesfin Seid (Wollo University)
Meda, Abdulmelik Husen (The Hong Kong Polytechnic University)
Zhang, Kouchi (TU Delft Electronic Components, Technology and Materials)
Fan, J. (TU Delft Electronic Components, Technology and Materials; Fudan Zhangjiang Institute)

2024

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 EGR-constructed time series health indicators and employed with the LSTM-RNN model demonstrates the effectiveness of the proposed methods.

Anomaly detection
Deep Learning Algorithms
Entropy generation rate (EGR)
Light-emitting diodes (LEDs)
Mahalanobis distance (MD)
Remaining Useful Lifetime (RUL) Prediction

http://resolver.tudelft.nl/uuid:89ffbd88-1fd7-4c23-b3c3-25febdd69702

https://doi.org/10.1016/j.eswa.2023.121832

2024-05-06

0957-4174

Expert Systems with Applications, 238

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2024 Minzhen Wen, Mesfin Seid Ibrahim, Abdulmelik Husen Meda, Kouchi Zhang, J. Fan