本文關(guān)鍵詞： 信息熵 滑移截取 最優(yōu)濾波器 特征提取 滾動軸承　出處：《振動與沖擊》2017年21期 　論文類型：期刊論文

【摘要】：以故障信號局部包含信息的差異性為基礎(chǔ),結(jié)合相空間重構(gòu)和信息熵理論,提出滑移信息熵序列對故障信息進(jìn)行局部沖擊特征識別。在此基礎(chǔ)上,引入最小熵反卷積、最優(yōu)濾波器構(gòu)建等理論,成功實現(xiàn)了滾動軸承的微弱故障診斷。仿真數(shù)據(jù)和實驗數(shù)據(jù)分析論證結(jié)果表明,提出的故障特征提取技術(shù)對于滾動軸承微弱沖擊故障特征具有優(yōu)越的識別和提取能力,對于實現(xiàn)滾動軸承強(qiáng)噪聲背景下故障智能診斷具有重要的意義。
[Abstract]:Based on the difference of local information contained in fault signal, combined with phase space reconstruction and information entropy theory, a slip information entropy sequence is proposed to identify the local impact characteristics of fault information. On this basis, minimum entropy deconvolution is introduced. Based on the theory of optimal filter construction, the weak fault diagnosis of rolling bearing is successfully realized. The simulation data and experimental data are analyzed and demonstrated. The proposed fault feature extraction technique has a superior ability to identify and extract the weak impact fault features of rolling bearings, and is of great significance to the intelligent fault diagnosis of rolling bearings under the strong noise background.
【作者單位】： 浙江大學(xué)機(jī)械設(shè)計與自動化研究所;浙江大學(xué)熱工與動力系統(tǒng)研究所;
【基金】：國家自然科學(xué)基金(51305392) 浙江省自然科學(xué)基金(LZ15E050001) 流體傳動與控制國家重點實驗室青年基金(SKLo FP_QN_1501)
【分類號】：TH17
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本文編號：1530185