Abstract
Foreign object detection is an important part of quality control of electricity meters. An automatic detection device is developed based on acoustic identification. In order to suppress background noise interference, we design a novel sound separation algorithm to separate the mixed sound signals to obtain the target source signal produced by foreign objects. Firstly, the improved principal-component-analysis-based multi-layered nonnegative matrix factorization (PMNMF) is used to separate sound signals. Secondly, the SVM is used to classify and identify sound signals. A suppot vector machine (SVM) as the classifier is used to compare the PMNMF algorithm with the basic NMF algorithm. The results indicate that the sound data pre-processed with the improved NMF algorithm results in a significantly higher identification rate up to about 95%.
Highlights
With the widespread application of the electricity meters, their measurement accuracy and reliability are of great importance
The automatic detection device collects the sound signal generated during the shaking of the meters, and uses the acoustic identification method to complete the detection of foreign objects
The rest of this paper is as follows: the second section presents the foreign body detection device, the third presents the basic Nonnegative matrix factorization (NMF) principle and the improved NMF algorithm respectively, the fourth elaborates the principle of suppot vector machine (SVM) algorithm in detail, the fifth section presents the experiment based on the collected experimental data, describes the experimental results and data analysis, and the sixth presents the conclusions based on the experimental results
Summary
With the widespread application of the electricity meters, their measurement accuracy and reliability are of great importance. The manual detection method of "manual shaking and ear hearing" is traditionally adopted to detect the foreign object in the electricity meters. The automatic detection device collects the sound signal generated during the shaking of the meters, and uses the acoustic identification method to complete the detection of foreign objects. Due to the complexity of the structure of the detection device and the presence of background noise interference, it is difficult to accurately detect the foreign objects. The rest of this paper is as follows: the second section presents the foreign body detection device, the third presents the basic NMF principle and the improved NMF algorithm respectively, the fourth elaborates the principle of SVM algorithm in detail, the fifth section presents the experiment based on the collected experimental data, describes the experimental results and data analysis, and the sixth presents the conclusions based on the experimental results
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