【摘要】：隨著科技的發(fā)展，鋼絲繩輸送帶因其負(fù)載重、運(yùn)輸量大、傳輸距離長等特點(diǎn)，成為煤礦、鋼鐵和冶金等行業(yè)的主要運(yùn)輸設(shè)備，并且這些行業(yè)對鋼絲繩輸送帶的依賴程度日益增強(qiáng)，人們也越來越關(guān)注鋼絲繩輸送帶的運(yùn)行狀態(tài)。 處于長期運(yùn)行中的鋼絲繩輸送帶，一旦發(fā)生斷絲、形變、磨損等故障，會造成鋼絲繩的強(qiáng)度下降直至斷裂，最終造成嚴(yán)重的人員傷亡和經(jīng)濟(jì)損失。為了降低煤礦事故的發(fā)生，本文陳述了鋼絲繩輸送帶無損檢測國內(nèi)外的發(fā)展現(xiàn)狀，分析了鋼絲繩輸送帶故障的原理和金屬磁記憶檢測的機(jī)理，研究了金屬磁記憶信號的降噪算法，證明了最小二乘支持向量機(jī)理論在鋼絲繩輸送帶早期故障診斷中的先進(jìn)性和可行性。 首先，介紹了鋼絲繩輸送帶無損檢測技術(shù)的發(fā)展現(xiàn)狀和金屬磁記憶技術(shù)的研究現(xiàn)狀。通過分析鋼絲繩輸送帶故障產(chǎn)生的原因，研究了金屬磁記憶技術(shù)的作用機(jī)理及金屬磁記憶法在故障檢測中的優(yōu)勢。與傳統(tǒng)檢測方法對比，提出金屬磁記憶技術(shù)應(yīng)用于鋼絲繩輸送帶檢測，并對其進(jìn)行了可行性分析。 其次，金屬磁記憶信號非常微弱，極易受到現(xiàn)場環(huán)境的干擾，如果不進(jìn)行降噪處理會嚴(yán)重影響檢測結(jié)果。依據(jù)集合經(jīng)驗(yàn)?zāi)B(tài)分解在信號處理領(lǐng)域的突出特點(diǎn)，提出了改進(jìn)型的集合經(jīng)驗(yàn)?zāi)B(tài)分解法對金屬磁記憶信號進(jìn)行降噪。通過集合經(jīng)驗(yàn)?zāi)B(tài)分解法與金屬磁記憶技術(shù)相結(jié)合，可以準(zhǔn)確的判定鋼絲繩輸送帶應(yīng)力集中的區(qū)域。 然后，，從降噪的金屬磁記憶信號中提取多個特征量，輸入到最小二乘支持向量機(jī)早期故障診斷系統(tǒng)內(nèi)�；诮⒌脑缙诠收显\斷系統(tǒng)，識別和診斷鋼絲繩輸送帶的運(yùn)行狀態(tài)。 最后，選用粒子群優(yōu)化算法對最小二乘支持向量機(jī)的參數(shù)進(jìn)行尋優(yōu)。仿真結(jié)果表明，該早期故障診斷系統(tǒng)能夠?qū)崿F(xiàn)對鋼絲繩輸送帶狀態(tài)的識別，具有較理想的準(zhǔn)確性。
[Abstract]:With the development of science and technology, steel rope conveyor belt has become the main transportation equipment in coal mine, steel and metallurgical industry because of its heavy load, large transport capacity and long transmission distance. And these industries rely more and more on steel rope conveyor belt, people pay more and more attention to the running state of steel rope conveyor belt. The wire rope conveyor belt in long-term operation, once broken wire, deformation, wear and other failures, will cause the strength of the wire rope down to fracture, resulting in serious casualties and economic losses. In order to reduce the occurrence of coal mine accidents, this paper describes the development status of non-destructive testing of steel rope conveyor belt at home and abroad, analyzes the principle of wire rope conveyor belt fault and the mechanism of metal magnetic memory detection. The de-noising algorithm of metal magnetic memory signal is studied. It is proved that the least square support vector machine theory is advanced and feasible in the early fault diagnosis of steel rope conveyor belt. Firstly, the development of nondestructive testing technology of steel rope conveyor belt and the research status of metal magnetic memory technology are introduced. Based on the analysis of the causes of the fault of the steel rope conveyor belt, the mechanism of the metal magnetic memory technology and the advantages of the metal magnetic memory method in the fault detection are studied. Compared with the traditional detection method, the application of metal magnetic memory technology to the detection of steel rope conveyor belt is put forward, and the feasibility analysis is made. Secondly, the metal magnetic memory signal is very weak, so it is easy to be disturbed by the field environment. If the noise reduction is not carried out, the detection results will be seriously affected. According to the outstanding characteristics of set empirical mode decomposition in the field of signal processing, an improved set empirical mode decomposition method is proposed to reduce the noise of metal magnetic memory signal. Through the combination of empirical mode decomposition method and metal magnetic memory technology, the area of stress concentration of steel rope conveyor belt can be accurately determined. Then, several features are extracted from the noise-reducing metal magnetic memory signal and input into the least squares support vector machine (LS-SVM) early fault diagnosis system. Based on the established early fault diagnosis system, the running state of steel rope conveyor belt is identified and diagnosed. Finally, the particle swarm optimization algorithm is used to optimize the parameters of least squares support vector machine. The simulation results show that the early fault diagnosis system can recognize the state of steel rope conveyor belt and has a better accuracy.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TD50

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