本文選題：信號(hào)故障檢測(cè)　切入點(diǎn)：小波閾值去噪　出處：《計(jì)算機(jī)仿真》2017年10期

【摘要】：為了準(zhǔn)確提取電機(jī)信號(hào)故障頻率特征,提出了一種基于差量分析和小波閾值的故障諧波檢測(cè)方法。差量分析解決了基波頻率對(duì)故障頻率的干擾問題。研究表明信號(hào)中的噪聲會(huì)對(duì)故障頻率的檢測(cè)產(chǎn)生較大影響,小波閾值函數(shù)具有很好的消噪能力。結(jié)合兩者之間的優(yōu)點(diǎn),先利用差量分析法對(duì)基波頻率進(jìn)行消除,再將處理后的差量信號(hào)利用改進(jìn)閾值函數(shù)消除噪聲。仿真結(jié)果表明,所提的方法提高了故障頻率的檢測(cè)性能。與傳統(tǒng)的檢測(cè)方法相比較,故障頻率特征更易提取。
[Abstract]:In order to accurately extract the motor fault signal frequency characteristics, this paper presents a fault detection method of harmonic differential analysis based on wavelet threshold and solves the interference problem. The fault frequency of the fundamental frequency differential analysis. The results show that the noise in the signal will have a greater impact on the frequency of fault detection, wavelet threshold denoising function with ability good. Combined with the advantages of both, the first use of the fundamental frequency of the elimination of differential analysis method, and then after the differential signal using the improved threshold function to remove noise. The simulation results show that the proposed method improves the detection performance of fault frequency. Compared with the traditional detection methods, the fault frequency characteristic easy to extract.

【作者單位】： 北京信息科技大學(xué)信息與通信工程學(xué)院;
【基金】：國(guó)家自然基金面上項(xiàng)目(51374223) 北京市科技提升計(jì)劃項(xiàng)目(PXM2016_014224_000021)
【分類號(hào)】：TM307

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