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      基于改進(jìn)CYCBD的滾動(dòng)軸承復(fù)合故障自適應(yīng)診斷方法

      2023-01-13 01:07:34劉桂敏王曉東李卓睿
      關(guān)鍵詞:峭度故障診斷卷積

      劉桂敏,馬 軍,熊 新,王曉東,李卓睿

      基于改進(jìn)CYCBD的滾動(dòng)軸承復(fù)合故障自適應(yīng)診斷方法

      劉桂敏,馬 軍※,熊 新,王曉東,李卓睿

      (1. 昆明理工大學(xué)信息工程與自動(dòng)化學(xué)院,昆明 650500;2. 昆明理工大學(xué)云南省人工智能重點(diǎn)實(shí)驗(yàn)室,昆明 650500)

      為實(shí)現(xiàn)滾動(dòng)軸承復(fù)合故障自適應(yīng)診斷,該研究提出了基于循環(huán)含量比-歸一化諧波比例(Ratio of Cyclic Content-Normalized Proportion of Harmonics,RCC-NPH)融合指標(biāo)改進(jìn)的最大二階循環(huán)平穩(wěn)盲解卷積(Maximum second order cyclostationary blind deconvolution,CYCBD)方法。首先,構(gòu)建了RCC-NPH融合指標(biāo),解決了CYCBD算法循環(huán)頻率確定依賴先驗(yàn)知識(shí)及遍歷所有故障頻率空間耗時(shí)的問題。其次,根據(jù)RCC-NPH融合指標(biāo)圖估計(jì)CYCBD的循環(huán)頻率集,實(shí)現(xiàn)了CYCBD參數(shù)的自適應(yīng)選擇。再次,采用自適應(yīng)參數(shù)CYCBD方法對(duì)輸入信號(hào)進(jìn)行解卷積運(yùn)算,提取了不同循環(huán)頻率對(duì)應(yīng)的故障信號(hào)。最后,對(duì)提取的故障信號(hào)進(jìn)行Hilbert包絡(luò)解調(diào)分析,完成故障的辨識(shí)。利用該方法分別對(duì)仿真信號(hào)和軸承復(fù)合故障信號(hào)進(jìn)行試驗(yàn),均能有效檢測(cè)信號(hào)中包含的故障成分,實(shí)現(xiàn)了復(fù)合故障的自適應(yīng)診斷。與其他指標(biāo)相比,該方法能夠有效避免噪聲和諧波的干擾,適用于復(fù)合故障診斷。

      軸承;故障診斷;最大二階循環(huán)平穩(wěn)盲解卷積;循環(huán)含量比;歸一化諧波比例

      0 引 言

      滾動(dòng)軸承作為農(nóng)業(yè)工程中大型機(jī)械設(shè)備的核心部件,其健康狀況關(guān)系著設(shè)備的安全運(yùn)行。因此,對(duì)滾動(dòng)軸承進(jìn)行狀態(tài)監(jiān)測(cè)和故障診斷具有重要意義[1-2]。農(nóng)業(yè)機(jī)械設(shè)備多工作在野外,運(yùn)行環(huán)境復(fù)雜、運(yùn)行工況多變,產(chǎn)生的故障往往是多故障伴生的復(fù)合故障,準(zhǔn)確提取滾動(dòng)軸承的復(fù)合故障特征是實(shí)現(xiàn)故障診斷的關(guān)鍵[3-4]。然而,復(fù)合故障成分復(fù)雜,弱故障成分容易被強(qiáng)背景噪聲和強(qiáng)故障掩蓋,大大增加了故障診斷的難度。因此,研究多個(gè)潛在故障分量的快速完備提取方法,實(shí)現(xiàn)復(fù)合故障特征的有效分離,已成為研究的重點(diǎn)和熱點(diǎn)之一[5]。

      振動(dòng)信號(hào)能夠有效反映部件的健康狀態(tài),在故障診斷中得到了廣泛應(yīng)用[6]。近年來,許多學(xué)者對(duì)此開展了研究,提出了時(shí)頻分解[7]、隨機(jī)共振[8]、稀疏理論[9]、形態(tài)濾波[10]、盲反卷積[11]等方法。相關(guān)研究發(fā)現(xiàn),滾動(dòng)軸承不同故障源信號(hào)在傳遞過程中通過卷積相互耦合,盲反卷積理論可以通過反卷積過程減小傳遞路徑影響,提取故障脈沖,在故障診斷中具有十分獨(dú)特的優(yōu)勢(shì)[12]。Wiggins[13]提出最小熵反褶積(Minimum Entropy Deconvolution,MED),通過設(shè)計(jì)合適的有限脈沖響應(yīng)濾波器,利用迭代算法使濾波所提取信號(hào)的峭度值最大,成功應(yīng)用于地震信號(hào)處理。此后,MED被廣泛用于增強(qiáng)信號(hào)脈沖特征,但MED適合對(duì)單脈沖信號(hào)進(jìn)行解卷積,對(duì)周期性脈沖效果不佳[14]。McDonald等[15]引入相關(guān)峭度,融合故障周期信息和峭度分析優(yōu)勢(shì),提出了最大相關(guān)峭度反褶積(Maximum Correlated Kurtosis Deconvolution,MCKD)。MCKD自提出以來,在故障診斷領(lǐng)域得到了廣泛的應(yīng)用[16]。但相關(guān)峭度受位移數(shù)的限制,極大地影響了MCKD的濾波效果。為了解決上述問題,McDonald等[17]又提出了多點(diǎn)最優(yōu)最小熵解卷積(Multi-point Optimal Minimum Entropy Deconvolution Adjusted,MOMEDA),以多點(diǎn)峭度最大值作為求解最優(yōu)濾波器的迭代條件,提取振動(dòng)信號(hào)中的沖擊成分。然而,MOMEDA在降低噪聲的同時(shí),也大大降低了振動(dòng)信號(hào)中的脈沖幅值,影響了故障特征的有效提取。為此,Buzzoni等[18]通過突出濾波信號(hào)的循環(huán)平穩(wěn)性進(jìn)行反卷積,提出了最大二階循環(huán)平穩(wěn)盲解卷積(Maximum second order cyclostationary blind deconvolution,CYCBD)方法。CYCBD可以有效提取周期性脈沖信號(hào),并對(duì)周期性脈沖成分進(jìn)行增強(qiáng),具有良好的降噪性能。同時(shí),相關(guān)研究也發(fā)現(xiàn):CYCBD循環(huán)頻率的設(shè)定直接關(guān)系到解卷積信號(hào)的循環(huán)平穩(wěn)性,對(duì)解卷積效果影響很大。且現(xiàn)有循環(huán)頻率的選擇多依靠人工經(jīng)驗(yàn)或優(yōu)化算法,不利于生產(chǎn)實(shí)際中滾動(dòng)軸承的復(fù)合故障診斷[19-20]。因此,研究有效的復(fù)合故障信號(hào)循環(huán)頻率自適應(yīng)估計(jì)方法對(duì)提升CYCBD方法的可行性和普適性具有重要意義。

      滾動(dòng)軸承復(fù)合故障信號(hào)循環(huán)頻率自適應(yīng)估計(jì)的核心是構(gòu)建合適的評(píng)價(jià)指標(biāo),以準(zhǔn)確評(píng)價(jià)故障信號(hào)成分。立足于新指標(biāo)的構(gòu)建及改進(jìn),學(xué)者相繼提出了Gini指數(shù)[21]、多點(diǎn)峭度(Multipoint Kurtosis,MK)[22]、包絡(luò)諧噪比[23]、循環(huán)含量比(Ratio of Cyclic Content,RCC)[24]、歸一化諧波比例(Normalized Proportion of Harmonics,NPH)[25]等故障成分評(píng)價(jià)指標(biāo)。上述指標(biāo)在單一故障檢測(cè)方面具有良好的性能,但用于復(fù)合故障周期脈沖檢測(cè)時(shí),受被測(cè)信號(hào)中強(qiáng)故障、強(qiáng)脈沖噪聲及隨機(jī)噪聲的影響,其故障檢測(cè)能力下降甚至失效?;谖墨I(xiàn)[24-28]的研究,在原有研究[29]已實(shí)現(xiàn)復(fù)合故障有效分離的基礎(chǔ)上,提出了一種新的故障成分檢測(cè)指標(biāo)——RCC-NPH融合指標(biāo),進(jìn)一步改進(jìn)了CYCBD方法,以期實(shí)現(xiàn)滾動(dòng)軸承復(fù)合故障的自適應(yīng)診斷,提升CYCBD方法的有效性和適用性。

      1 理論分析

      1.1 RCC-NPH融合指標(biāo)構(gòu)建

      1.1.1 RCC指標(biāo)分析

      RCC是Borghesani提出的用于提取故障沖擊性的軸承特征指標(biāo)[24],其定義如式(1)所示。

      1.1.2 NPH指標(biāo)分析

      NPH指標(biāo)可以定量評(píng)估周期信號(hào)中諧波分量所占的比例。同時(shí),考慮了指定頻率及其所有的諧波成分,并通過對(duì)指定頻率及其諧波個(gè)數(shù)的歸一化處理,弱化分子數(shù)的主導(dǎo)作用[25]。根據(jù)式(4)對(duì)解析信號(hào)進(jìn)行Hilbert變換,得到其包絡(luò)譜如式(5)所示。

      1.1.3 RCC-NPH融合指標(biāo)原理

      為全面表征復(fù)合故障信號(hào)特點(diǎn),融合RCC和NPH指標(biāo)的優(yōu)勢(shì),提出了一種兼顧信號(hào)信噪比、沖擊性及諧波成分的新指標(biāo)——RCC-NPH融合指標(biāo)。

      首先,計(jì)算各頻率成分的RCC和NPH值。其次,由于這兩個(gè)指標(biāo)的幅度范圍不同,故將其歸一化為相同的標(biāo)度[27]。使用最小最大值歸一化技術(shù),將[Min, Max]的極限設(shè)置為[0, 1]。將相關(guān)性最大的頻率成分歸一化為1,相關(guān)性最小的頻率成分歸一化為0。最大最小歸一化公式如式(7)所示[28]。最后,基于文獻(xiàn)[27]的邏輯,將歸一化的RCC、NPH相乘,計(jì)算出各頻率成分對(duì)應(yīng)的RCC-NPH融合指標(biāo)值,如式(8)所示。

      式中φ為第個(gè)值的大小,表示所有的指標(biāo)值。RCC-NPH融合指標(biāo)是在[0, 1]尺度下進(jìn)行歸一化,相關(guān)性最大的頻率成分歸一化為1,相關(guān)性最小的則歸一化為0,故本文設(shè)置0.5為閾值用于頻率篩選[30]。篩選兩個(gè)指標(biāo)值相關(guān)性均大于0.5的頻率成分,避免了單一指標(biāo)值過大,而另一指標(biāo)過小導(dǎo)致頻率選取不當(dāng)?shù)膯栴},從而實(shí)現(xiàn)對(duì)故障特性的全面衡量。即:若NPH為1,RCC小于0.5,則表明該頻率成分盡管是周期性成分,但沖擊性不強(qiáng);若RCC為1,但NPH小于0.5,則表明信號(hào)無明顯的諧波成分,不是周期性沖擊性成分;若兩者相關(guān)性均在0.7以上,則證明該頻率成分是所要檢測(cè)的周期性脈沖成分。

      1.2 基于RCC-NPH融合指標(biāo)改進(jìn)的CYCBD

      CYCBD以二階循環(huán)平穩(wěn)性指標(biāo)(ICS2)最大化為目標(biāo),通過迭代特征值分解算法求解,提取故障特征。通過對(duì)含噪觀測(cè)信號(hào)進(jìn)行解卷積運(yùn)算,獲取具有循環(huán)平穩(wěn)性的目標(biāo)源信號(hào)0,即

      式(9)中表示源信號(hào),為逆濾波器,*表示卷積運(yùn)算。用矩陣形式表示為=,如式(10)所示。

      其中和分別表示信號(hào)的長(zhǎng)度和逆濾波器的長(zhǎng)度。二階循環(huán)平穩(wěn)性的一般表達(dá)式如式(11)所示。

      信號(hào)中周期成分如式(15)所示。

      則由公式(10)、式(14)~(15)可以得到ICS2的表達(dá)式為

      加權(quán)矩陣如式(17)所示。

      由上述原理可知,CYCBD算法效果受到濾波器長(zhǎng)度的影響,濾波器長(zhǎng)度較大會(huì)增加計(jì)算時(shí)間,較小則濾波效果不理想。結(jié)合前期試驗(yàn)綜合考慮信號(hào)特點(diǎn)和計(jì)算效率,本文選擇700作為CYCBD的濾波器長(zhǎng)度[31-33]。同時(shí),循環(huán)頻率作為計(jì)算二階循環(huán)平穩(wěn)性指標(biāo)的關(guān)鍵,決定著解卷積信號(hào)的循環(huán)平穩(wěn)性。現(xiàn)有CYCBD算法的循環(huán)頻率依靠先驗(yàn)知識(shí)進(jìn)行人為設(shè)定,不利于復(fù)合故障的自適應(yīng)診斷。為實(shí)現(xiàn)復(fù)合故障的自適應(yīng)診斷,采用提出的RCC-NPH融合指標(biāo)確定復(fù)合故障信號(hào)中包含的全部循環(huán)頻率,消除先驗(yàn)知識(shí)對(duì)信號(hào)的影響。

      RCC-NPH融合指標(biāo)確定CYCBD循環(huán)頻率的具體實(shí)現(xiàn)步驟如下:

      1)根據(jù)理論故障頻率設(shè)置RCC-NPH融合指標(biāo)的頻率范圍;

      2)初始化循環(huán)次數(shù)=0,循環(huán)頻率數(shù)1=1;

      3)=+1,根據(jù)公式(8)計(jì)算信號(hào)頻率對(duì)應(yīng)的RCC-NPH融合指標(biāo)值;

      4)若RCC-NPH融合指標(biāo)大于閾值0.5,則存入循環(huán)頻率向量(1),1=1+1,否則返回步驟3)繼續(xù)循環(huán);

      5)以循環(huán)頻率向量(1)中的頻率為循環(huán)頻率,根據(jù)公式(16)計(jì)算對(duì)應(yīng)的ICS2;

      6)求解最優(yōu)濾波器,獲取單一故障成分;

      7)返回步驟3)進(jìn)行循環(huán)計(jì)算,直至頻率范圍中所有頻率計(jì)算完畢。

      2 滾動(dòng)軸承復(fù)合故障自適應(yīng)診斷方法實(shí)現(xiàn)過程

      本文RCC-NPH融合指標(biāo)改進(jìn)的CYCBD滾動(dòng)軸承復(fù)合故障診斷步驟如下:

      1)輸入采集的復(fù)合故障振動(dòng)信號(hào);

      2)根據(jù)公式(8)計(jì)算各頻率成分對(duì)應(yīng)的RCC- NPH融合指標(biāo)值,構(gòu)造RCC-NPH融合指標(biāo)圖,自適應(yīng)選擇循環(huán)頻率;

      3)根據(jù)選取的CYCBD循環(huán)頻率,設(shè)置相應(yīng)的循環(huán)頻率集,采用改進(jìn)的CYCBD算法對(duì)輸入信號(hào)進(jìn)行解卷積運(yùn)算,提取對(duì)應(yīng)的解卷積信號(hào);

      4)對(duì)提取的信號(hào)進(jìn)行Hilbert包絡(luò)解調(diào)分析,完成故障識(shí)別。

      3 試驗(yàn)分析

      3.1 仿真信號(hào)分析

      為驗(yàn)證本文所提方法的有效性,根據(jù)式(18)建立復(fù)合故障仿真信號(hào)進(jìn)行初步驗(yàn)證[34]。仿真信號(hào)采樣頻率f為12.8 kHz,分析點(diǎn)數(shù)為8192,()是信噪比為-3 dB的高斯白噪聲。為提高仿真信號(hào)的辨識(shí)性,相關(guān)參數(shù)設(shè)置如表1所示。

      表1 仿真信號(hào)參數(shù)

      式中是信號(hào)中包含的故障周期數(shù),分別是外圈和內(nèi)圈的故障周期,=f/f,=f/f。其他參數(shù)設(shè)置如表 1所示。

      圖1為復(fù)合故障仿真信號(hào)的時(shí)域波形和Hilbert包絡(luò)解調(diào)譜。由于受背景噪聲干擾,無法從圖1a中觀察到的周期性脈沖,也無法從圖1b中篩選出故障的特征頻率。

      圖1 復(fù)合故障仿真信號(hào)時(shí)域波形及其Hilbert包絡(luò)譜

      采用本文提出的方法對(duì)復(fù)合故障信號(hào)進(jìn)行分析。為避免頻率范圍設(shè)置過大導(dǎo)致算法效率下降,頻率范圍過小導(dǎo)致漏診。根據(jù)文獻(xiàn)[35],將頻率的范圍在包含最小和最大頻率段的基礎(chǔ)上,增加±20%的允許誤差,即:(0.8min, 1.2max),故將此次試驗(yàn)的頻率范圍設(shè)置為[80 Hz, 155 Hz]。根據(jù)式(8)計(jì)算頻率范圍內(nèi)各頻率信號(hào)對(duì)應(yīng)的RCC-NPH融合指標(biāo)值,得到了圖2所示的RCC-NPH融合指標(biāo)圖。從圖2中可以看出,信號(hào)中包含1=100 Hz、2=128 Hz兩個(gè)明顯的頻率成分,說明該信號(hào)包含兩種故障,與構(gòu)造的信號(hào)設(shè)置的故障頻率一致。由此可見,RCC-NPH融合指標(biāo)圖準(zhǔn)確的檢測(cè)出了原始復(fù)合故障信號(hào)中包含的全部故障成分。

      注:f1和f2為超過閾值線的信號(hào)頻率。

      為驗(yàn)證所提融合指標(biāo)RCC-NPH的優(yōu)越性,分別與歸一化RCC、歸一化NPH、自相關(guān)譜、多點(diǎn)峭度譜進(jìn)行對(duì)比,試驗(yàn)結(jié)果如圖3所示。從圖3a和3b中可以看出,RCC圖和NPH圖中雖然包含所需檢測(cè)的故障成分,但還存在其他大于閾值或及其接近閾值的干擾頻率,極易造成誤診;從圖3c中可以看出,自相關(guān)譜中包含明顯的內(nèi)圈故障周期(T=f/f=100,f為采樣頻率,f為內(nèi)圈故障頻率)及其倍周期(2 T~9T),同時(shí)也包含外圈故障周期(T=fs/f=128,f為外圈故障頻率)及其倍周期(2 f~6f),但外圈周期成分的自相關(guān)值與內(nèi)圈周期成分的自相關(guān)值相比十分微弱,極易被忽略,不利于復(fù)合故障的全面診斷;從圖3d中可以看出,多點(diǎn)峭度譜中包含明顯的外圈故障周期及其半周期和倍周期(2T~6T),不含內(nèi)圈故障周期,導(dǎo)致漏診。因此,指標(biāo)性能系統(tǒng)對(duì)比結(jié)果表明,融合指標(biāo)RCC-NPH可以更準(zhǔn)確的檢測(cè)復(fù)合故障信號(hào)中包含的故障成分。

      a. RCC b. NPHc. 自相關(guān)譜c. Autocorrelation spectrumd. 多點(diǎn)峭度譜d. Multipoint kurtosis spectrum 注:Ti和To分別為內(nèi)圈故障周期和外圈故障周期。Note: Ti and To are inner and outer fault cycles respectively

      為進(jìn)一步完成復(fù)合故障診斷,采用本文所提的自適應(yīng)診斷方法進(jìn)行處理。先將RCC-NPH融合指標(biāo)圖中大于閾值的第一個(gè)頻率1作為CYCBD的循環(huán)頻率,設(shè)置循環(huán)頻率集為[100, 200, …, 1000],所提取信號(hào)的時(shí)域波形如圖4a所示。再將大于閾值的第二個(gè)頻率2作為CYCBD的循環(huán)頻率,設(shè)置循環(huán)頻率集為[128, 256, …, 1280],所取信號(hào)時(shí)域波形如圖4b所示。分別對(duì)提取的故障信號(hào)進(jìn)行Hilbert包絡(luò)解調(diào)分析,結(jié)果如圖5所示。從圖5a中可以看出,提取的第一個(gè)信號(hào)包絡(luò)譜中存在明顯的基頻(100 Hz)及其倍頻(2f~9f),與外圈故障頻率一致,可判斷存在外圈故障;從圖5b中可以看出,信號(hào)包絡(luò)譜中存在明顯的基頻(128 Hz)及其倍頻(2f~7f),與內(nèi)圈故障頻率一致,故內(nèi)圈故障被有效識(shí)別。

      圖4 CYCBD篩選信號(hào)的時(shí)域波形

      注:ff分別為內(nèi)圈和外圈故障頻率, Hz。

      Note:fand fare the frequency of inner and outer fault, respectively, Hz.

      圖5 CYCBD篩選信號(hào)的Hilbert包絡(luò)譜

      Fig.5 The Hilbert envelope spectrum of the extracted by CYCBD

      3.2 試驗(yàn)驗(yàn)證分析

      為進(jìn)一步論證本文方法的有效性,基于自制試驗(yàn)平臺(tái)數(shù)據(jù)完成試驗(yàn)驗(yàn)證。自制試驗(yàn)平臺(tái)由驅(qū)動(dòng)電動(dòng)機(jī)、轉(zhuǎn)軸、液壓油缸、測(cè)試軸承、傳感器等部件組成,結(jié)構(gòu)如圖6a所示。試驗(yàn)軸承是深溝球軸承,型號(hào)為6205-2RSH,結(jié)構(gòu)參數(shù)如表2所示。

      在測(cè)試軸承的內(nèi)圈和外圈設(shè)計(jì)了一個(gè)寬0.2 mm的裂縫用于模擬復(fù)合故障,如圖6b所示。試驗(yàn)轉(zhuǎn)速f為1 797 r/min,采樣頻率f為25.6 kHz,采樣時(shí)間為10 s。軸承內(nèi)圈故障頻率理論計(jì)算值為162.33 Hz,外圈故障頻率理論計(jì)算值為107.22 Hz。復(fù)合故障信號(hào)時(shí)域波形及頻譜如圖7所示。從圖7中可以看出,時(shí)域波形具有明顯的脈沖成分,且呈現(xiàn)周期性波動(dòng),但僅根據(jù)時(shí)域波形不能直接知曉滾動(dòng)軸承的故障類型,頻譜中盡管包含內(nèi)外圈故障頻率,但被其他突出的頻率成分掩蓋,不利于復(fù)合故障的有效識(shí)別。

      表2 測(cè)試軸承參數(shù)

      采用本文提出的方法對(duì)復(fù)合故障試驗(yàn)數(shù)據(jù)進(jìn)行分析,設(shè)置頻率范圍為[85 Hz, 200 Hz],構(gòu)造RCC-NPH融合指標(biāo)圖,如圖8所示。從圖8中可以看出,RCC-NPH融合指標(biāo)值大于閾值的頻率成分有2個(gè),分別是109和163 Hz。同時(shí)觀察發(fā)現(xiàn),圖中頻率為136 Hz的成分也相對(duì)突出,但小于設(shè)定閾值,不影響循環(huán)頻率的確定。經(jīng)計(jì)算可知,該頻率成分為復(fù)合故障內(nèi)外圈信號(hào)的耦合頻率,即1/2(f+f)。由此可見,RCC-NPH融合指標(biāo)圖準(zhǔn)確識(shí)別出了復(fù)合故障包含的2種故障成分,避免了諧波頻率的干擾。

      同樣,為驗(yàn)證本文指標(biāo)的有效性,與復(fù)合故障信號(hào)的歸一化RCC、歸一化NPH、自相關(guān)譜、多點(diǎn)峭度譜進(jìn)行對(duì)比,試驗(yàn)結(jié)果如圖8b~8e所示。圖中可以看出,通過RCC和NPH雖然包含故障頻率成分,但還存在較多的干擾頻率成分,影響故障的準(zhǔn)確檢測(cè),極易導(dǎo)致誤診;從圖8d中可以看出,自相關(guān)譜中包含明顯的外圈故障周期(T=f/f=234,f為采樣頻率,f為外圈故障頻率)的倍周期(2T及4T),無法檢測(cè)到內(nèi)圈周期成分,導(dǎo)致漏診;從圖8e中可以看出,多點(diǎn)峭度譜中包含內(nèi)圈故障周期成分(T=f/f=158,f為內(nèi)圈故障頻率)及其倍周期(2T),外圈故障周期及其倍周期(2T)。但在復(fù)合故障成分未知的情況下,不能通過多點(diǎn)峭度譜直觀的判斷出具體故障成分。因此,綜合對(duì)比發(fā)現(xiàn),本文所提方法可以準(zhǔn)確直觀的檢測(cè)出復(fù)合故障信號(hào)包含的頻率成分,有效避免了復(fù)合故障的漏診和誤診。

      分別以RCC-NPH融合指標(biāo)圖確定的109 Hz和163 Hz兩個(gè)頻率作為CYCBD的循環(huán)頻率,進(jìn)一步完成復(fù)合故障診斷。設(shè)置循環(huán)頻率集分別為[109, 218, …, 1 090],[163, 326, …, 1 630],提取故障信號(hào),分別對(duì)提取到的故障信號(hào)進(jìn)行Hilbert包絡(luò)解調(diào)分析,結(jié)果如圖9a、9b所示。從圖9a中可以看出,信號(hào)包絡(luò)譜中存在明顯的外圈故障基頻(108.8 Hz)及其倍頻(2 f~9f)。從圖9b中可以看出,信號(hào)包絡(luò)譜中存在明顯的內(nèi)圈故障基頻(162.1 Hz)及其倍頻(2 f~6f)。

      圖7 復(fù)合故障試驗(yàn)信號(hào)時(shí)域波形及其Hilbert包絡(luò)譜

      a. RCC-NPH融合指標(biāo)a. RCC-NPH fusion indexb. RCC

      c. NPHd. 自相關(guān)譜d. Autocorrelation spectrume. 多點(diǎn)峭度譜e. Multipoint kurtosis spectrum

      為進(jìn)一步確認(rèn)頻率成分136 Hz為干擾諧波,設(shè)置循環(huán)頻率集為[136, 272, …, 1 360],提取故障信號(hào)。提取得到的信號(hào)包絡(luò)譜如圖9c所示。從圖9c中可以看出,提取得到的信號(hào)包含的頻率成分仍是以內(nèi)外圈故障頻率為主,以及其諧波成分(f+f,2(f+f))。故136 Hz不是該信號(hào)的獨(dú)立故障源成分。因此,本文所提方法準(zhǔn)確的排除了諧波的干擾,實(shí)現(xiàn)了復(fù)合故障的自適應(yīng)診斷。

      a. 提取得到的信號(hào)1包絡(luò)譜(外圈)a. Envelope spectrum of extracted signal 1 (outer ring)b. 提取得到的信號(hào)2包絡(luò)譜(內(nèi)圈)b. Envelope spectrum of extracted signal 2 (inner circle)c. CYCBD篩選136Hz頻率成分的包絡(luò)譜c. Hilbert envelope spectrum of 136Hz frequency components screened by CYCBD

      4 結(jié) 論

      本文針對(duì)復(fù)合故障難以自適應(yīng)診斷的問題,提出了基于RCC-NPH融合指標(biāo)改進(jìn)的CYCBD滾動(dòng)軸承復(fù)合故障自適應(yīng)診斷方法。通過對(duì)仿真信號(hào)及試驗(yàn)數(shù)據(jù)分析,得出以下結(jié)論:

      1)針對(duì)CYCBD算法循環(huán)頻率確定依賴先驗(yàn)知識(shí)的問題,提出了基于RCC-NPH融合指標(biāo)的循環(huán)頻率估計(jì)方法,準(zhǔn)確地估計(jì)了符合信號(hào)特點(diǎn)的循環(huán)頻率,并通過與歸一化RCC、歸一化NPH、自相關(guān)譜、多點(diǎn)峭度譜等4種指標(biāo)進(jìn)行系統(tǒng)的對(duì)比,驗(yàn)證了RCC-NPH融合指標(biāo)的有效性,為準(zhǔn)確檢測(cè)未知復(fù)合故障奠定了基礎(chǔ),消除了傳統(tǒng)指標(biāo)依賴先驗(yàn)知識(shí)對(duì)復(fù)合故障診斷的影響。

      2)針對(duì)未知復(fù)合故障難以全面診斷的問題,采用RCC-NPH融合指標(biāo)優(yōu)化的CYCBD方法消除了信號(hào)之間的相互耦合,提取了各單一故障成分。通過仿真試驗(yàn)及實(shí)測(cè)信號(hào)分析可知,所提方法完成了復(fù)合故障的全面自適應(yīng)診斷,對(duì)滾動(dòng)軸承實(shí)際工程應(yīng)用具有重要借鑒價(jià)值。

      本文所提方法在定工況下的復(fù)合故障診斷中取得了較好的效果,但針對(duì)時(shí)變工況下復(fù)合故障診斷的有效性和適用范圍需要后續(xù)進(jìn)一步研究。

      [1] 馬朝永,盛志鵬,胥永剛,等. 基于自適應(yīng)頻率切片小波變換的滾動(dòng)軸承故障診斷[J]. 農(nóng)業(yè)工程學(xué)報(bào),2019,35(10):34-41.

      Ma Chaoyong, Sheng Zhipeng, Xu Yonggang, et al. Fault diagnosis of rolling bearing based on adaptive frequency slice wavelet transform[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(10): 34-41. (in Chinese with English abstract)

      [2] 鄢小安,賈民平. 基于層次多尺度散布熵的滾動(dòng)軸承智能故障診斷[J]. 農(nóng)業(yè)工程學(xué)報(bào),2021,37(11):67-75.

      Yan Xiaoan, Jia Minping. Intelligent fault diagnosis of rolling element bearing using hierarchical multiscale dispersion entropy[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(11): 67-75. (in Chinese with English abstract)

      [3] 呂軒,胡占齊,周海麗,等. 自適應(yīng)最大相關(guān)峭度反褶積方法診斷齒輪軸承復(fù)合故障[J]. 農(nóng)業(yè)工程學(xué)報(bào),2019,35(12):48-57.

      Lv Xuan, Hu Zhanqi, Zhou Haili, et al. Compound fault diagnosis method for gear bearing based on adaptive maximum correlated kurtosis deconvolution[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(12): 48-57. (in Chinese with English abstract)

      [4] 施杰,伍星,劉韜. 采用HHT算法與卷積神經(jīng)網(wǎng)絡(luò)診斷軸承復(fù)合故障[J]. 農(nóng)業(yè)工程學(xué)報(bào),2020,36(4):34-43.

      Shi Jie, Wu Xing, Liu Tao. Bearing compound fault diagnosis based on HHT algorithm and convolution neural network[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(4): 34-43. (in Chinese with English abstract)

      [5] 齊詠生,劉飛,李永亭,等. 基于MK-MOMEDA和Teager能量算子的風(fēng)電機(jī)組滾動(dòng)軸承復(fù)合故障診斷[J]. 太陽(yáng)能學(xué)報(bào),2021,42(7):297-307.

      Qi Yongsheng, Liu Fei, Li Yongting, et al. Hybrid Fault Diagnosis of Rolling Bearings in Wind Turbines Based on MK-momeda and Teager Energy Operator[J]. Chinese Journal of Solar Energy, 2021, 42(7): 297-307. (in Chinese with English abstract)

      [6] 楊小青,丁康,何國(guó)林. 齒輪故障振動(dòng)嚙合調(diào)幅調(diào)頻信號(hào)分離方法[J]. 振動(dòng)工程學(xué)報(bào),2021,34(2):379-388.

      Yang Xiaoqing, Ding Kang, He Guolin. Separation method of gear fault vibration meshing AM frequency modulation signal[J]. Journal of Vibration Engineering, 2021, 34(2): 379-388. (in Chinese with English abstract)

      [7] Jian C, Yang Y, Shao H D, et al. Enhanced periodic mode decomposition and its application to composite fault diagnosis of rolling bearings[J]. ISA Transactions, 2021, 125: 474-491.

      [8] 賀利芳,崔瑩瑩,張?zhí)祢U,等. 基于冪函數(shù)型雙穩(wěn)隨機(jī)共振的故障信號(hào)檢測(cè)方法[J]. 儀器儀表學(xué)報(bào),2016,37(7):1457-1467.

      He Lifang, Cui Yingying, Zhang Tianqi, et al. Fault signal detection method based on power function type bistable stochastic resonance[J]. Chinese Journal of Scientific Instrument, 2016, 37(7): 1457-1467. (in Chinese with English abstract)

      [9] 李可,李欣欣,宿磊,等. 基于DTCWT與GA改進(jìn)稀疏分解的軸承故障診斷[J]. 華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版),2021,49(6):56-61.

      Li Ke, Li Xinxin, Su Lei, et al. Fault diagnosis of bearings based on improved sparse decomposition via DTCWT and GA[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2021, 49(6): 56-61. (in Chinese with English abstract)

      [10] Tong W G, Chen C Z, Luo Y Q, et al. Research on fault detection of rolling bearings in press line by a new morphological filter based on diagonal slice spectrum lifting[J]. Measurement, 2021, 188: 1-14.

      [11] 張守京,慎明俊,楊靜雯,等. 采用參數(shù)自適應(yīng)最大相關(guān)峭度解卷積的滾動(dòng)軸承故障特征提取[J]. 西安交通大學(xué)學(xué)報(bào),2022,56(3):75-83.

      Zhang Shoujing, Shen Mingjun, Yang Jingwen, et al. A fault feature extraction method of rolling bearings based on parameter adaptive maximum correlation kurtosis deconvolution[J]. Journal of Xi'an Jiaotong University, 2022, 56(3): 75-83. (in Chinese with English abstract)

      [12] Sawalhi N, Randall R B, Endo H. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis[J]. Mechanical Systems and Signal Processing, 2007, 21(6): 2616-2633.

      [13] Wiggins Ralph A. Minimum entropy deconvolution[J]. Geoexploration, 1978, 16(1-2): 21-35.

      [14] Duan R K, Liao Y H, Yang L, et al. Minimum entropy morphological deconvolution and its application in bearing fault diagnosis[J]. Measurement, 2021, 182: 1-12.

      [15] McDonald Geoff L, Zhao Q, Zuo M J. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection[J]. Mechanical Systems and Signal Processing, 2012, 33(1): 237-255.

      [16] Lyu X, Hu Z Q, Zhou H L, et al. Application of improved MCKD method based on QGA in planetary gear compound fault diagnosis[J]. Measurement, 2019, 139: 236-248.

      [17] Mcdonald G L, Zhao Q. Multipoint optimal minimum entropy deconvolution and convolution fix:Application to vibration fault detection[J]. Mechanical Systems and Signal processing, 2017, 82: 461-477.

      [18] Buzzoni M, Antoni J, D'Elia G. Blind deconvolution based on cyclostationarity maximization and its application to fault identification[J]. Journal of Sound and Vibration, 2018, 432: 569-610.

      [19] Zhang Q Q, Pan H C, Fan Q X, et al. Research on fault extraction method of CYCBD based on seagull optimization algorithm[J]. Shock and Vibration, 2021, 2021: 1-11.

      [20] 黃包裕,張永祥,趙磊. 基于布谷鳥搜索算法和最大二階循環(huán)平穩(wěn)盲解卷積的滾動(dòng)軸承故障診斷方法[J]. 機(jī)械工程學(xué)報(bào),2021,57(9):99-107.

      Huang Baoyu, Zhang Yongxiang, Zhao Lei. Research on fault diagnosis method of rolling bearings based on cuckoo search algorithm and maximum second order cyclostationary blind deconvolution[J]. Journal of Mechanical Engineering, 2021, 57(9): 99-107. (in Chinese with English abstract)

      [21] Miao Y H, Wang J J, Zhang B Y, et al. Practical framework of Gini index in the application of machinery fault feature extraction[J]. Mechanical Systems and Signal Processing, 2022, 165: 1-14.

      [22] 向玲,李京蓄,胡愛軍,等. 基于SK-MOMEDA的風(fēng)電機(jī)組軸承復(fù)合故障特征分離提取[J]. 振動(dòng)、測(cè)試與診斷,2021,41(4):644-651, 826.

      Xiang Ling, LI Jingxu, Hu Aijun, et al. Separation and Extraction of Composite Fault Features of Wind Turbine Bearings Based on SK-MOMEDA[J]. Journal of Vibration, Measurement & Diagnosis, 2021, 41(4): 644-651, 826. (in Chinese with English abstract)

      [23] Li Z R, Ma J, Wang X D, et al. An Optimal parameter selection method for MOMEDA based on EHNR and its spectral entropy[J]. Sensors, 2021, 21(2): 1-27.

      [24] Borghesani P, Pennacchi P, Chatterton S. The relationship between kurtosis and envelope-based indexes for the diagnostic of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2014, 43(1/2): 25-43.

      [25] Miao Y H, Zhang B Y, Zhao M, et al. Period-oriented multi-hierarchy deconvolution and its application for bearing fault diagnosis[J]. ISA Transactions, 2021, 114: 455-469.

      [26] Mo Z L, Wang J Y, Zhang H, Miao Q. Weighted cyclic harmonic-to-noise ratio for rolling element bearing fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(2): 432-442.

      [27] Zheng K, Luo J F, Zhang Y, et al. Incipient fault detection of rolling bearing using maximum autocorrelation impulse harmonic to noise deconvolution and parameter optimized fast EEMD[J]. ISA Transactions, 2019, 89: 256-271.

      [28] Singh J, Darpe A K, Singh S P. Bearing damage assessment using Jensen–Rényi Divergence based on EEMD[J]. Mechanical Systems and Signal Processing, 2017, 87: 307-39.

      [29] 劉桂敏,吳建德,李卓睿,等. Infogram和參數(shù)優(yōu)化CYCBD在滾動(dòng)軸承復(fù)合故障特征分離中的應(yīng)用[J]. 振動(dòng)與沖擊,2022,41(10):55-65.

      Liu Guimin, Wu Jiande, Li Zhuorui, et al. Application of Infogram and Parameter optimization CYCBD in complex fault feature separation of rolling bearings[J]. Journal of Vibration and Shock, 2022, 41(10): 55-65. (in Chinese with English abstract)

      [30] 肖勇,趙云,涂治東,等. 基于改進(jìn)的皮爾遜相關(guān)系數(shù)的低壓配電網(wǎng)拓?fù)浣Y(jié)構(gòu)校驗(yàn)方法[J]. 電力系統(tǒng)保護(hù)與控制,2019,47(11):37-43.

      Xiao Yong, Zhao Yun, Tu Zhidong, et al. Topology checking method for low voltage distribution network based on improved Pearson correlation coefficient[J]. Power system protection and control, 2019, 47(11): 37-43. (in Chinese with English abstract)

      [31] 羅忠,徐迪,李雷,等. 基于改進(jìn)二階循環(huán)平穩(wěn)解卷積的軸承故障檢測(cè)方法[J]. 東北大學(xué)學(xué)報(bào)(自然科學(xué)版),2021,42(5):673-678.

      Luo Zhong, Xu Di, Li Lei, et al. Bearing fault detection based on improved CYCBD method[J]. Journal of Northeastern University (Natural Science), 2021, 42(5): 673- 678. (in Chinese with English abstract)

      [32] Ke Y, Yao C, Song E Z, et al. An early fault diagnosis method of common-rail injector based on improved CYCBD and hierarchical fluctuation dispersion entropy[J]. Digital Signal Processing, 2021, 114: 1-12.

      [33] Zhang B Y, Miao Y H, Lin J, et al. Adaptive maximum second-order cyclostationarity blind deconvolution and its application for locomotive bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2021, 158: 1-18.

      [34] 胡愛軍,趙軍,孫尚飛,等. 基于譜峭度和最大相關(guān)峭度解卷積的滾動(dòng)軸承復(fù)合故障特征分離方法[J]. 振動(dòng)與沖擊,2019,38(4):158-165.

      Hu Aijun, Zhao Jun, Sun Shangfei, et al. Feature separation method of rolling bearing composite fault based on spectral kurtosis and maximum correlation kurtosis deconvolution[J]. Journal of Vibration and Shock, 2019, 38(4): 158-165. (in Chinese with English abstract)

      [35] Wang C G, Li H K, Ou J Y, et al. Identification of planetary gearbox weak compound fault based on parallel dual-parameter optimized resonance sparse decomposition and improved MOMEDA[J]. Measurement, 2020, 165: 1-18.

      Adaptive diagnosis method of composite fault for rolling bearings using improved CYCBD

      Liu Guimin, Ma Jun※, Xiong Xin, Wang Xiaodong, Li Zhuorui

      (1.,,650500,; 2.,,650500,)

      Rolling bearing is the core component of large rotating machinery in agricultural engineering. The composite fault is more harmful than the single fault in the process of operation. The source signals of composite faults are coupled with each other through the convolution in the process of propagation, which brings difficulties to fault detection. The maximum second-order cyclostationary blind deconvolution (CYCBD) can be used to reduce the influence of the transmission path using the deconvolution process. The mutual coupling between signals can be eliminated to effectively extract the periodic pulse signals. However, the CYCBD cycle frequency is directly related to the cycle stability of deconvolution signals. There is a great influence on deconvolution. The fault characteristic frequency depends mainly on the manual experience or optimization. It is a high demand to determine the composite fault diagnosis of rolling bearings in production practice. This study aims to extract the composite fault features of rolling bearings for the adaptive diagnosis of composite faults. An improved composite fault diagnosis was proposed for the CYCBD rolling bearings using RCC-NPH fusion index. Firstly, an investigation was made to comprehensively characterize the composite fault signals, then to integrate the ratio of cyclic content (RCC) and normalized proportion of harmonics (NPH) indexes. A new RCC-NPH fusion index was also proposed to consider the signal SNR, impact property, and harmonic components. As such, the CYCBD was independent of the prior knowledge to determine the cycle frequency covering all the fault frequency space. Secondly, the cycle frequency of CYCBD was set adaptively, according to the RCC-NPH fusion index. The cycle frequency dataset was also set to achieve the adaptive selection of CYCBD parameters. Thirdly, the parameter adaptive CYCBD served as the deconvolution on the input composite fault signals. The fault signals corresponding to different fault frequencies were then extracted to realize the effective separation of composite faults. Finally, the extracted single fault signal was demodulated by the Hilbert envelope to realize the fault identification. An experimental platform was developed to verify the improved model using the simulation signals and the experimental data. Experimental results show that the improved model with the RCC-NPH fusion index accurately and efficiently estimated the cycle frequency in the line with the characteristics of the signal. The CYCBD was also independent of the prior knowledge on the composite fault diagnosis. At the same time, the RCC-NPH fusion index effectively suppressed the interference frequency, in order to visually depict the composite fault features. An accurate extraction was realized for the fault components contained in the signals by systematic comparison with four indexes, including the RCC, NPH, autocorrelation spectrum, and multi-point kurtosis spectrum. The mutual coupling between signals was eliminated to successfully extract each single fault component after adaptive fault diagnosis for the rolling bearing composite faults. The comprehensive diagnosis of composite faults was realized to effectively avoid the misdiagnosis and missed diagnosis. Therefore, the composite fault adaptive diagnosis can be expected to effectively identify and separate each single fault feature in the composite fault, particularly for the adaptive diagnosis of rolling bearing composite faults.

      bearing; fault diagnosis; maximum second-order cyclic stationary blind deconvolution; cyclic content ratio; normalized proportion of harmonics

      10.11975/j.issn.1002-6819.2022.16.011

      TN911.7;TH165.3

      A

      1002-6819(2022)-16-0098-09

      劉桂敏,馬軍,熊新,等. 基于改進(jìn)CYCBD的滾動(dòng)軸承復(fù)合故障自適應(yīng)診斷方法[J]. 農(nóng)業(yè)工程學(xué)報(bào),2022,38(16):98-106.doi:10.11975/j.issn.1002-6819.2022.16.011 http://www.tcsae.org

      Liu Guimin, Ma Jun, Xiong Xin, et al. Adaptive diagnosis method of composite fault for rolling bearings using improved CYCBD[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022, 38(16): 98-106. (in Chinese with English abstract) doi:10.11975/j.issn.1002-6819.2022.16.011 http://www.tcsae.org

      2022-05-30

      2022-08-03

      國(guó)家自然科學(xué)基金(62163020,62173168);云南省科技計(jì)劃項(xiàng)目(2019FD042,202101BE070001-055)

      劉桂敏,研究方向?yàn)樾D(zhuǎn)機(jī)械故障診斷。Email:liuguimin0909@163.com

      馬軍,副教授,研究方向?yàn)闄C(jī)械系統(tǒng)動(dòng)態(tài)建模與分析、機(jī)械設(shè)備混合智能故障診斷與預(yù)示和性能退化趨勢(shì)預(yù)測(cè)。Email:491941203@qq.com

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