本文選題：散狀物料 + 電子皮帶秤　； 參考：《南京理工大學(xué)》2016年博士論文

【摘要】：隨著中國(guó)經(jīng)濟(jì)的高速發(fā)展,各種散狀物料尤其是大宗工業(yè)原、燃料的貿(mào)易運(yùn)輸量急劇上升,使得散料貿(mào)易中動(dòng)態(tài)稱量的要求越來(lái)越高。目前,如何長(zhǎng)期保持≤0.1%的計(jì)量精度已成為國(guó)內(nèi)外諸多散料貿(mào)易中動(dòng)態(tài)計(jì)量專家和科技工作者亟需解決的難題。機(jī)器學(xué)習(xí)的發(fā)展給傳統(tǒng)行業(yè)帶來(lái)了不一樣的色彩,隨著"互聯(lián)網(wǎng)+"戰(zhàn)略計(jì)劃的提出,傳統(tǒng)衡器行業(yè)將面臨新的轉(zhuǎn)型升級(jí)。本文以目前應(yīng)用最廣泛的散狀物料連續(xù)累計(jì)稱重設(shè)備——電子皮帶秤為研究對(duì)象,結(jié)合各種機(jī)器學(xué)習(xí)方法,對(duì)其累計(jì)計(jì)量精度所涉及到的"皮帶效應(yīng)"、局部性故障、輸送帶跑偏、溫度變化等問(wèn)題展開(kāi)研究。在電子皮帶秤結(jié)構(gòu)組成和稱重原理基礎(chǔ)上,結(jié)合以往的實(shí)驗(yàn)經(jīng)驗(yàn),以累計(jì)計(jì)量的測(cè)量信號(hào)流程為研究脈絡(luò),對(duì)其累計(jì)計(jì)量精度的誤差源和耐久性問(wèn)題進(jìn)行了深入討論研究,對(duì)影響精度最主要誤差源以及耐久性誤差源進(jìn)行了總結(jié)。通過(guò)研究總結(jié)發(fā)現(xiàn),需對(duì)運(yùn)行中電子皮帶秤的多個(gè)誤差因素以及各個(gè)故障狀態(tài)進(jìn)行實(shí)時(shí)在線監(jiān)測(cè),并針對(duì)這些誤差因素變化以及不同故障狀態(tài)的不同程度建立一個(gè)具有較好泛化性能和魯棒性的精度補(bǔ)償模型,以真正提高累計(jì)計(jì)量的耐久性。針對(duì)"皮帶效應(yīng)",從梁理論出發(fā)對(duì)稱重力誤差進(jìn)行了機(jī)理研究,對(duì)陣列式皮帶秤"內(nèi)力理論"的理論公式進(jìn)行了推導(dǎo)。然后,以"內(nèi)力理論"對(duì)QPS皮帶秤全性能試驗(yàn)中心的4#皮帶秤進(jìn)行精度補(bǔ)償試驗(yàn)。通過(guò)試驗(yàn)分析得出:無(wú)故障時(shí),精度可達(dá)OIMLR50 2014(E)中的0.2級(jí)精度等級(jí),即累計(jì)稱重誤差±≤0.1%;但當(dāng)存在輸送帶跑偏、稱重架卡料等一些故障時(shí),精度很不理想,需對(duì)故障狀態(tài)進(jìn)行實(shí)時(shí)在線監(jiān)測(cè),并加以補(bǔ)償。針對(duì)皮帶秤稱重區(qū)域內(nèi)的故障對(duì)稱重精度的影響,對(duì)故障的在線監(jiān)測(cè)方法進(jìn)行了研究。首先針對(duì)皮帶秤不同流量稱重?cái)?shù)據(jù)密度的不均勻,分別提出改進(jìn)型DENCLUE和改進(jìn)型DBSCAN,并都應(yīng)用于的稱重區(qū)域故障在線檢測(cè),其中改進(jìn)型DENCLUE采用動(dòng)態(tài)閾值法替代爬山法大大降低了算法復(fù)雜度,相比較于改進(jìn)型DBSCAN具有更好的聚類精度和更快的聚類速度;然后采用BRNN和改進(jìn)型BTSVM對(duì)檢測(cè)出來(lái)的故障進(jìn)行在線識(shí)別,最后將識(shí)別出來(lái)故障碼、故障位置(即哪個(gè)稱重單元)、當(dāng)前托輥傳感器數(shù)據(jù)以及同一時(shí)刻正常數(shù)據(jù)的平均值作為故障特征。陣列式皮帶秤故障試驗(yàn)表明:"基于改進(jìn)型DENCLUE的在線檢測(cè)+基于改進(jìn)型BTSVM在線識(shí)別"模型具有更好的稱重區(qū)域故障在線監(jiān)測(cè)性能。針對(duì)皮帶秤輸送帶跑偏故障對(duì)稱重精度的影響,對(duì)輸送帶跑偏的在線監(jiān)測(cè)方法進(jìn)行了研究。研究引入流形學(xué)習(xí)和深層神經(jīng)網(wǎng)絡(luò),分別建立了基于LTSA+GRNN+SVM和基于CDBN+SVM的在線跑偏監(jiān)測(cè)模型,模型能產(chǎn)生顯性非線性映射將原始稱重?cái)?shù)據(jù)壓縮成3維的跑偏特征。二者的訓(xùn)練結(jié)果以及試驗(yàn)測(cè)試結(jié)果表明,皆具有很好的跑偏識(shí)別精度,可取代傳統(tǒng)硬件檢測(cè)設(shè)備,但適合于不同工作場(chǎng)合,LTSA+GRNN+SVM很適用于皮帶秤稱重標(biāo)定較為頻繁、跑偏檢測(cè)實(shí)時(shí)性要求不是很高的情況,而CDBN+SVM非常適用于標(biāo)定不是很頻繁、但實(shí)時(shí)性和識(shí)別精度要求很高的情況。最后依據(jù)散狀物料連續(xù)稱重累計(jì)流量累加法計(jì)量原理,引入過(guò)程神經(jīng)網(wǎng)絡(luò)建立累計(jì)計(jì)量精度綜合補(bǔ)償模型,并以"內(nèi)力理論"、稱重區(qū)域內(nèi)故障特征以及輸送帶跑偏特征為準(zhǔn)定義了補(bǔ)償模型的輸入。研究基于過(guò)程神經(jīng)網(wǎng)絡(luò)精度補(bǔ)償模型的訓(xùn)練算法,通過(guò)融合正則化極限學(xué)習(xí)機(jī)和誤差最小化極限學(xué)習(xí)機(jī)算法,提出一種基于EM-RPELM的精度補(bǔ)償模型。張力變化、有故障狀態(tài)、溫度變化的試驗(yàn)結(jié)果表明,基于EM-RPELM的精度補(bǔ)償模型具有良好的魯棒性、泛化性能以及一定的不平衡數(shù)據(jù)處理能,補(bǔ)償后的精度總體達(dá)到0.2級(jí)。
[Abstract]:With the rapid development of China's economy, all kinds of bulk materials, especially large industrial ones, have increased the amount of fuel trade and transportation, which makes the demand for dynamic weighing in the bulk trade higher and higher. At present, how to maintain the measurement accuracy of less than 0.1% for a long time has become the urgent need for many dynamic measurement experts and scientists and technicians in the bulk of bulk materials trade. To solve the problem. The development of machine learning brings a different color to the traditional industry, with the advance of "Internet plus" strategic plan, traditional weighing industry will face a new transformation and upgrading. In this paper the bulk material is currently the most widely used continuous cumulative weighing equipment - electronic belt scale as the research object, combined with a variety of machine learning square On the basis of the structure composition and weighing principle of the electronic belt scale, the accumulative measurement signal flow is taken as the research vein, and the error source of the accumulative measurement accuracy is discussed. The problem of durability is discussed in depth, and the main error sources of the influence precision and the source of the durability error are summarized. Through the study, it is found that the multiple error factors of the electronic belt scale in the operation and the real time on-line monitoring of each fault state are needed, and the changes of these error factors and the different fault states are taken. A precision compensation model with better generalization performance and robustness is set up to improve the durability of accumulative measurement. Based on the "belt effect", the mechanism of symmetrical gravity error is studied from the beam theory, and the theoretical formula of "internal force theory" of the array belt scale is derived. Then, the "internal force theory" is used for the QP. The precision compensation test of the 4# belt scale of the S belt scale full performance test center is carried out. Through the test analysis, it is concluded that the precision can reach the 0.2 grade precision grade of OIMLR50 2014 (E) without fault, that is, the accumulative weighing error is less than 0.1%, but when there are some faults such as the running belt deviation and the weighing frame card, the precision is very unsatisfactory and the fault state should be realized. On line monitoring and compensation, the on-line monitoring method of the fault is studied in view of the influence of the fault symmetry heavy precision in the weighing area of the belt weigher. Firstly, the improved DENCLUE and the improved DBSCAN are put forward respectively for the uneven density of the weighing data of the belt weigher. Test, the improved DENCLUE uses the dynamic threshold method instead of mountain climbing method to greatly reduce the complexity of the algorithm. Compared with the improved DBSCAN, it has better clustering accuracy and faster clustering speed. Then, BRNN and improved BTSVM are used to identify the detected faults online, and the fault location (i.e., where the fault location) will be identified. A weighing unit), the current roller sensor data and the average value of the normal data at the same time as the fault characteristics. The array type belt scale fault test shows that the "online detection based on improved DENCLUE + based on the improved BTSVM online recognition" model has better performance on the on-line monitoring of weighing area fault. On the basis of the influence of the symmetrical heavy precision of the fault, the on-line monitoring method of the belt running deviation is studied. In this paper, we introduce the manifold learning and the deep neural network, and establish the on-line running deviation monitoring model based on LTSA+GRNN+SVM and CDBN+SVM respectively. The model can produce the running deviation characteristic of the original weighing data into 3 dimension by the explicit nonlinear projection. The training results of the two and the test results show that all of them have good accuracy of deviation recognition and can replace the traditional hardware detection equipment, but it is suitable for different working situations. LTSA+GRNN+SVM is very suitable for the weighing scale of the belt weigher, and the real-time requirement of deviation detection is not very high, and the CDBN+SVM is very suitable for calibration. It is very frequent, but the real time and recognition precision are very high. Finally, according to the accumulative flow cumulation principle of the continuous weighing of the bulk material, the synthetic compensation model of accumulative measurement precision is established by introducing the process neural network, and the compensation model is defined by the "internal force theory", the characteristic of the fault in the weighing area and the characteristic of the belt running deviation. A training algorithm based on the precision compensation model of process neural network is studied. A precision compensation model based on EM-RPELM is proposed by integrating the regularization limit learning machine and the error minimization limit learning machine algorithm. The test results of tension change, failure state and temperature change show that the precision compensation model based on EM-RPELM is shown. It has good robustness, generalization performance and a certain degree of imbalance data processing energy, and the accuracy of compensation reaches 0.2 levels.

【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TH715.1

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