需要金幣： 1000 個(gè)金幣 |
資料包括：完整論文 |  |
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| 轉(zhuǎn)換比率：金額 X 10=金幣數(shù)量， 例100元=1000金幣 | 論文字?jǐn)?shù)：24552 |  | |
| 折扣與優(yōu)惠：團(tuán)購(gòu)最低可5折優(yōu)惠 - 了解詳情 | 論文格式：Word格式(*.doc) |  |
摘要：滾動(dòng)軸承是機(jī)械設(shè)備中必不可少的基礎(chǔ)元件，它由內(nèi)圈、外圈、滾動(dòng)體、保持架四部分組成。滾動(dòng)軸承起源很早，歷史悠久。隨著科學(xué)技術(shù)的不斷發(fā)展，軸承制造技術(shù)也日新月異，滾動(dòng)軸承的精確度也越來(lái)越高，然而軸承故障是不可避免的，比如軸承長(zhǎng)時(shí)間工作后會(huì)造成疲勞剝落、腐蝕、磨損、擦傷、裂紋等。眾所周知，軸承故障帶來(lái)的危害是巨大的，包括人員傷亡、經(jīng)濟(jì)損失等。那么通過(guò)什么樣的方法可以識(shí)別進(jìn)而診斷軸承故障呢？本文通過(guò)獲取的滾動(dòng)軸承的振動(dòng)信號(hào)，這些軸承振動(dòng)信號(hào)來(lái)源于美國(guó)凱斯西儲(chǔ)大學(xué)電氣工程軸承實(shí)驗(yàn)。然后采用由模式濾波法原理編制而成的軟件計(jì)算和分析這些振動(dòng)信號(hào)，分析結(jié)果得到大量聲音子的時(shí)域圖，再利用時(shí)域和頻譜轉(zhuǎn)換關(guān)系得到頻譜圖，結(jié)合時(shí)域圖和頻譜圖采用人工智能的方法對(duì)這些聲音子進(jìn)行分離并歸類(lèi)，最后分析歸類(lèi)結(jié)果中每一類(lèi)聲音子的特征來(lái)診斷軸承的故障。 關(guān)鍵詞：振動(dòng)信號(hào) 信號(hào)分離 特征提取 模式濾波法 故障診斷
Abstract: The rolling bearing is an essential foundation component in the equipment of machinery. It consists of inner ring, Outer Ring, Cage Train and Rolling Element. The rolling bearing origins early and has a long history. Along with the development of technology of science ,the technology of bearing manufacturing also changing, and the accuracy of the rolling bearing is more and more higher. However，the bearing fault is inevitable . For example , the long working time of bearings can cause fatigue peel, corrosion, wear, bruises, crack and so on. As is known to all, the bearing fault can bring a huge harm, Including the injury or the death of person, economic loss, etc. So what kind of method can be used to identify and then diagnosis the fault of the rolling bearing? This article through the vibration signal of rolling bearings, These bearing vibration signal comes from the electrical engineering bearing experiment of the American case western reserve university. And then use a software to calculate, and analyzed the vibration signal, the software is programmed by the principle of Mode of filtering method . we can get a lot of time domain figure of the voice through the analysis results, and then use the relationship of time domain and frequency conversion to achieve frequency spectrogram. Combined with the time domain figure and frequency spectrogram using artificial intelligence method to separate and classify these voices. The finally, through analysising the feature of every kind of voice of the classified results to diagnosis the bearing fault. Key words：Vibration signal Signal separation Feature extraction Mode of filtering method Fault diagnosis |
