本文選題：振動(dòng)篩故障 + 小波變換　； 參考：《華僑大學(xué)》2017年碩士論文

【摘要】：振動(dòng)篩是石礦加工行業(yè)的關(guān)鍵設(shè)備之一,常用于物料篩分和清洗,發(fā)生故障,將導(dǎo)致整條生產(chǎn)線(xiàn)停工,經(jīng)濟(jì)損失巨大。因此研究振動(dòng)篩故障診斷方法及研制相應(yīng)的振動(dòng)篩故障監(jiān)測(cè)系統(tǒng),對(duì)預(yù)防振動(dòng)篩故障具有重要的學(xué)術(shù)價(jià)值和實(shí)際工程意義。結(jié)合振動(dòng)篩實(shí)際使用情況,其常見(jiàn)故障有大梁斷裂、彈簧斷裂、側(cè)板開(kāi)裂、螺栓斷裂和篩網(wǎng)磨損等,根據(jù)這些故障類(lèi)型,搭建振動(dòng)篩故障實(shí)驗(yàn)平臺(tái),提取振動(dòng)篩故障的振動(dòng)加速度信號(hào)。采用時(shí)域分析和小波變換技術(shù),提取d1層小波系數(shù)能量、d2層小波系數(shù)能量、d3層小波系數(shù)能量、a3層小波系數(shù)能量、偏態(tài)因素、峰態(tài)因素、裕度指標(biāo)和峰值指標(biāo)這八個(gè)特征量構(gòu)建振動(dòng)篩故障特征向量。分別采用BP神經(jīng)網(wǎng)絡(luò)、支持向量機(jī)、基于主成分分析的支持向量機(jī)這3種方法研究振動(dòng)篩的故障識(shí)別方法。研究結(jié)果表明,BP神經(jīng)網(wǎng)絡(luò)算法的識(shí)別率只有86%且運(yùn)算耗時(shí)為10.12s,而支持向量機(jī)算法的識(shí)別率最高,達(dá)99.82%,運(yùn)算時(shí)間僅為8.63 s。為了保證算法在DSP系統(tǒng)里具有較好的移植性,采用主成分分析提取累計(jì)貢獻(xiàn)率為94.9%的前兩主元,分析各特征量在這兩個(gè)主元中的貢獻(xiàn)率,選取貢獻(xiàn)率最大的兩個(gè)特征量用于支持向量機(jī)的故障識(shí)別,從而降低了信息冗余,基于主成分分析的支持向量機(jī)算法具有運(yùn)算速度最快,為6.21 s,故障識(shí)別率高達(dá)91.85%,能實(shí)現(xiàn)振動(dòng)篩故障監(jiān)測(cè)系統(tǒng)的程序移植。根據(jù)振動(dòng)篩故障特征量的分析和故障識(shí)別算法的研究,設(shè)計(jì)振動(dòng)篩故障監(jiān)測(cè)系統(tǒng)的硬件部分。其中,以低通濾波器為核心,設(shè)計(jì)了能提取振動(dòng)篩故障特征頻段信號(hào)的調(diào)理模塊;為了實(shí)現(xiàn)12路信號(hào)同步高速采集,設(shè)計(jì)了基于AD7606芯片的信號(hào)采集模塊;搭建以DSP芯片為處理器的微控制器模塊,實(shí)現(xiàn)大量數(shù)據(jù)的快速運(yùn)算和故障識(shí)別算法的運(yùn)行。軟件部分權(quán)衡了可行性和實(shí)時(shí)性,對(duì)故障識(shí)別算法進(jìn)行簡(jiǎn)化,設(shè)定兩個(gè)特征量的故障閾值,以此判斷振動(dòng)篩故障。最終完成振動(dòng)篩故障監(jiān)測(cè)系統(tǒng)樣機(jī)制作并進(jìn)行測(cè)試實(shí)驗(yàn),實(shí)驗(yàn)了振動(dòng)篩激振力不平衡、彈簧剛度變化和彈簧高度變化三類(lèi)故障類(lèi)型。結(jié)果表明,故障識(shí)別率達(dá)80%,對(duì)企業(yè)預(yù)防振動(dòng)篩故障具有實(shí)際應(yīng)用價(jià)值。
[Abstract]:Vibrating screen is one of the key equipments in the quarry processing industry. It is often used for material screening and cleaning, which will lead to the shutdown of the whole production line and great economic losses. Therefore, it is of great academic value and practical engineering significance to study the fault diagnosis method of vibrating screen and to develop the corresponding fault monitoring system for vibrating screen. Combined with the actual application of vibrating screen, the common faults are beam break, spring break, side plate crack, bolt fracture and screen wear, etc. According to these fault types, the fault test platform of vibrating screen is built. The vibration acceleration signal of vibration screen fault is extracted. Time domain analysis and wavelet transform technique are used to extract the energy of the wavelet coefficients in layer D1 and the energy of wavelet coefficients in layer D2. The energy of wavelet coefficients in layer D 3 is the energy of wavelet coefficients in layer a 3, the skewness factor and the factor of peak state. The eight eigenvalues of margin index and peak value index are used to construct the fault feature vector of vibrating screen. BP neural network, support vector machine (SVM) and support vector machine (SVM) based on principal component analysis (PCA) are used to study the fault identification method of vibrating screen. The results show that the recognition rate of BP neural network algorithm is only 86% and the computation time is 10.12 s, while the support vector machine algorithm has the highest recognition rate of 99.82 and the operation time is only 8.63 s. In order to ensure that the algorithm has good transplantability in DSP system, the first two principal components with cumulative contribution rate of 94.9% are extracted by principal component analysis, and the contribution rate of each characteristic quantity in these two principal components is analyzed. Two characteristic variables with the largest contribution rate are selected for fault identification of support vector machines, thus reducing information redundancy. The support vector machine algorithm based on principal component analysis has the fastest operation speed. The fault identification rate is as high as 91.85 for 6.21 s, which can realize the program transplantation of the vibrating screen fault monitoring system. According to the analysis of vibration screen fault characteristic quantity and the research of fault identification algorithm, the hardware part of vibration screen fault monitoring system is designed. In order to realize the high-speed and synchronous acquisition of 12 signals, a signal acquisition module based on AD7606 chip is designed. A microcontroller module based on DSP chip is built to realize the fast operation of a large number of data and the running of fault identification algorithm. In the software part, the fault identification algorithm is simplified and the fault threshold of two characteristic quantities is set to judge the fault of vibrating screen. Finally, the prototype of the vibration screen fault monitoring system is made and tested. Three types of fault types are tested, such as the unbalanced exciting force, the change of spring stiffness and the change of spring height. The results show that the rate of fault identification is 80%, which has practical application value for preventing vibration screen faults in enterprises.
【學(xué)位授予單位】：華僑大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TD452;TP274

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