Department of Engineering, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK.
Institute of Energy and Environment, University of Strathclyde, 204 George Street, Glasgow G1 1XW, UK.
Sensors (Basel). 2018 Sep 14;18(9):3098. doi: 10.3390/s18093098.
In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome.
在这项工作中，我们旨在对从多个资产的新电厂站点收集的更广泛的电磁干扰（EMI）放电源进行分类。这带来了更复杂和具有挑战性的分类任务。该研究涉及调查和开发新的和改进的特征提取和数据降维算法，基于图像处理技术。该方法是利用Gramian Angular Field 技术将测量的 EMI 时信号映射到图像中，从中提取重要信息，同时去除冗余。每种放电类型的图像都包含一个独特的指纹。然后，在映射图像中使用两种特征降维方法，即局部二值模式（LBP）和局部相位量化（LPQ）。这提供了可以实现到随机森林（RF）分类器中的特征向量。在新的数据库集上，比较了先前和两种新提出的方法的性能，包括分类准确性、精度、召回率和 F 度量。结果表明，新方法的性能优于先前的方法，其中 LBP 特征的效果最佳。
