基于小波包的開關電流電路故障診斷

張鎮; 段哲民; 龍英

doi:10.13374/j.issn2095-9389.2017.07.017

基于小波包的開關電流電路故障診斷

doi: 10.13374/j.issn2095-9389.2017.07.017

張鎮^1, ,,
段哲民¹,
龍英²

1) 西北工業大學電子信息學院, 西安 710072
2) 長沙學院電子與電氣工程學院, 長沙 410022

基金項目:

國家自然科學基金資助項目(61201108，61102035)

詳細信息

中圖分類號: TH165.3
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- 文章訪問數: 535
- HTML全文瀏覽量: 183
- PDF下載量: 10
- 被引次數: 0
出版歷程
- 收稿日期: 2016-12-13

Fault detection in switched current circuits based on preferred wavelet packet

1) School of Electronic Information, Northwestern Polytechnic University, Xi'an 710072, China
2) Department of Electronic & Electrical Engineering, Changsha University, Changsha 410022, China

摘要

摘要: 為提高開關電流電路故障診斷的精度,提出了一種基于小波包優選和優化BP神經網路的開關電流電路特征抽取與識別方法.首先對開關電流電路原始響應信號進行多層次的小波包分解,接著計算N層分解后的歸一化能量值,以特征偏離度作為評價選擇最優小波包基,構建最優故障特征向量,最后將提取的最優故障特征通過遺傳算法優化的BP神經網絡進行分類.該方法以實例電路進行驗證,結果表明所有的軟故障均得到了有效的分類,說明了該方法在開關電流電路故障診斷中的優越性.
- 開關電流電路 /
- 故障診斷 /
- 小波包變換 /
- 遺傳算法 /
- BP神經網絡
Abstract: In order to improve the accuracy of switched current circuit fault diagnosis, a feature extraction and recognition method of switched current circuit based on wavelet packet optimization and optimization of BP neural network was proposed. Firstly, the wavelet packet decomposition of the original response signal of the switched current circuit was carried out. Then, the normalized energy value after the decomposition of the N layer was calculated, and the optimal wavelet packet basis was selected by using the characteristic deviation as the evaluation. Finally, the optimal fault feature vector was constructed. The extracted optimal fault characteristics were classified by BP neural network optimized by genetic algorithm. The results of this method were verified by the example circuit. The results show that all the soft faults are effectively classified, and the superiority of the method in the fault diagnosis of the switched current circuit is illustrated.
- switched current circuit /
- fault detection /
- wavelet packet transform /
- genetic algorithm /
- BP neural network

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參考文獻(18)

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