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Anomaly Detection of Light-Emitting Diodes Using the Similarity-Based Metric Test

机译：使用基于相似度的度量测试来异常检测发光二极管

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摘要

Today’s decreasing product development cycle time requires rapid and cost-effective reliability analysis and testing. Qualification is the process of demonstrating that a product is capable of meeting or exceeding specified requirements. Light-emitting diode (LED) qualification tests are often as long as 6000 h, but this length of time does not guarantee the typically required lifetime of 10 years or more. This paper presents a prognostics-based technique that reduces the LED qualification time. An anomaly detection technique called the similarity-based metric test is developed to identify anomalies without utilizing historical libraries of healthy and unhealthy data. The similarity-based metric test extracts features from the spectral power distributions (SPDs) using peak analysis, reduces the dimensionality of the features using principal component analysis, and partitions the data set of principal components into groups using a k-nearest neighbor (KNN)-kernel density-based clustering technique. A detection algorithm then evaluates the distances from the centroid of each cluster to each test point and detects anomalies when the distance is greater than the threshold. From this, the dominant degradation processes associated with the LED die and phosphors in the LED package can be identified. In our case study, anomalies were detected at less than 1200 h using the similarity-based metric test. Thus, our method could decrease the amount of LED qualification testing time by providing users with an earlier time to begin remaining useful life prediction without waiting 6000 h as required by industrial standards.
机译：如今，产品开发周期不断缩短，因此需要快速且经济高效的可靠性分析和测试。合格是指证明产品能够满足或超过指定要求的过程。发光二极管（LED）鉴定测试通常长达6000小时，但是这段时间不能保证通常要求的10年以上的使用寿命。本文提出了一种基于预测的技术，可减少LED鉴定时间。开发了一种称为基于相似性的度量测试的异常检测技术，以在不利用健康和不健康数据的历史库的情况下识别异常。基于相似度的度量测试使用峰分析从频谱功率分布（SPD）中提取特征，使用主成分分析降低特征的维数，并使用k最近邻（KNN）将主成分数据集划分为组-基于内核密度的聚类技术。然后，检测算法会评估从每个群集的质心到每个测试点的距离，并在距离大于阈值时检测异常。由此，可以确定与LED管芯和LED封装中的磷光体相关的主要降解过程。在我们的案例研究中，使用基于相似性的度量测试在不到1200小时的时间内检测到异常。因此，我们的方法可以通过为用户提供更早的时间来开始剩余使用寿命的预测，而不必等待工业标准要求的6000小时来减少LED合格测试时间。

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