【摘要】：煤炭生產(chǎn)的關(guān)鍵設(shè)備是采煤機(jī),其構(gòu)造復(fù)雜,且在環(huán)境惡劣的井下工作,因此其電氣、液壓等系統(tǒng)故障率較高,經(jīng)過調(diào)研陜西神木市數(shù)十家煤炭企業(yè)得知現(xiàn)在采煤設(shè)備故障診斷非常落后,主要憑借傳統(tǒng)經(jīng)驗(yàn)去判斷出現(xiàn)的問題,其準(zhǔn)確性和效率無法保證,自動(dòng)化程度較低,已經(jīng)成為煤炭產(chǎn)業(yè)發(fā)展的重要瓶頸,因此研究采煤機(jī)故障診斷方法很有現(xiàn)實(shí)意義和實(shí)用價(jià)值。本文主要研究采煤機(jī)故障診斷神經(jīng)網(wǎng)絡(luò)模型、總體結(jié)構(gòu),訓(xùn)練算法與步驟,提出了最優(yōu)學(xué)習(xí)算法(ELM算法),建立采煤機(jī)滾動(dòng)軸承BP神經(jīng)子網(wǎng)絡(luò)故障診斷模型,并進(jìn)行MATLAB診斷誤差研究;研究基于專家系統(tǒng)、模糊神經(jīng)網(wǎng)絡(luò)的混合智能算法采煤機(jī)故障診斷方法,建立采煤機(jī)液壓牽引裝置系統(tǒng)過熱故障診斷樣本及模型,采用單自適應(yīng)BP網(wǎng)絡(luò)算法和模糊BP網(wǎng)絡(luò)算法進(jìn)行故障診斷,并對(duì)比分析診斷誤差、訓(xùn)練速度。本文的主要研究結(jié)果如下:1.基于神經(jīng)網(wǎng)絡(luò)的采煤機(jī)故障診斷方法采用最優(yōu)學(xué)習(xí)算法(ELM算法)避免前饋神經(jīng)網(wǎng)絡(luò)算法誤差和權(quán)值范數(shù)較大等缺點(diǎn),提高故障診斷網(wǎng)絡(luò)的泛化性。2.采煤機(jī)滾動(dòng)軸承BP神經(jīng)子網(wǎng)絡(luò)故障診斷模型經(jīng)6次循環(huán)訓(xùn)練后達(dá)到目標(biāo)誤差,診斷誤差小于0.01,說明基于BP神經(jīng)網(wǎng)絡(luò)采煤機(jī)故障診斷方法是可行的、高效的。3.采煤機(jī)液壓牽引裝置過熱故障診斷模型誤差達(dá)到0.001,模糊模塊自適應(yīng)BP網(wǎng)絡(luò)算法訓(xùn)練樣本僅僅迭代1500次,而自適應(yīng)BP網(wǎng)絡(luò)算法必須要迭代3500次,前者故障診斷效率更高。4.相同樣本訓(xùn)練情況下,基于混合智能算法的采煤機(jī)故障診斷方法診斷誤差小,適應(yīng)能力更好。5.基于混合智能算法的采煤機(jī)故障診斷方法使整個(gè)故障診斷系統(tǒng)的診斷效果得到優(yōu)化。
[Abstract]:The key equipment in coal production is shearer, whose structure is complex, and it works in the downhole where the environment is bad, so its electrical, hydraulic and other systems have a high failure rate. After investigating dozens of coal enterprises in Shenmu City, Shaanxi Province, they know that the fault diagnosis of coal mining equipment is very backward now. It mainly relies on traditional experience to judge the problems that appear. Its accuracy and efficiency cannot be guaranteed, and the degree of automation is low. It has become an important bottleneck in the development of coal industry, so it is of practical significance and practical value to study the fault diagnosis method of shearer. In this paper, the neural network model, overall structure, training algorithm and steps for fault diagnosis of shearer are studied. The optimal learning algorithm (ELM algorithm) is put forward, and the fault diagnosis model of BP neural network for shearer rolling bearing is established. The diagnostic error of MATLAB was studied. Based on expert system and fuzzy neural network, the fault diagnosis method of shearer based on hybrid intelligent algorithm is studied, and the diagnosis sample and model of overheating fault of hydraulic traction device system of shearer are established. The single adaptive BP network algorithm and fuzzy BP network algorithm are used for fault diagnosis, and the diagnosis error and training speed are compared and analyzed. The main results of this paper are as follows: 1. The fault diagnosis method of shearer based on neural network adopts the optimal learning algorithm (ELM algorithm) to avoid the shortcomings of the feedforward neural network algorithm such as large error and weight norm, and to improve the generalization of fault diagnosis network. 2. After six cycles training, the fault diagnosis model of BP neural network of shearer rolling bearing reaches the target error, and the diagnostic error is less than 0.01, which shows that the fault diagnosis method based on BP neural network is feasible and efficient. The model error of overheating fault diagnosis of hydraulic traction device of shearer is 0.001. The training sample of fuzzy module adaptive BP network algorithm is only 1500 iterations, and the adaptive BP network algorithm must be iterated 3500 times. The former fault diagnosis efficiency is higher. 4. In the case of the same sample training, the fault diagnosis method of shearer based on hybrid intelligent algorithm has less error and better adaptability. The fault diagnosis method of shearer based on hybrid intelligent algorithm optimizes the diagnosis effect of the whole fault diagnosis system.
【學(xué)位授予單位】：西北農(nóng)林科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TD421.61;TP183

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