本文選題：采煤機(jī)滾動(dòng)軸承　切入點(diǎn)：非線性系統(tǒng)　出處：《遼寧工程技術(shù)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：煤炭資源在我國(guó)能源體系結(jié)構(gòu)中具有非常重要的地位和作用,采煤機(jī)作為煤礦生產(chǎn)過程的關(guān)鍵設(shè)備,是集機(jī)械、電子、電氣、傳動(dòng)、液壓等為一體的復(fù)雜機(jī)械。采煤機(jī)設(shè)備的安全、穩(wěn)定運(yùn)行對(duì)于保證煤炭生產(chǎn)的安全、促進(jìn)企業(yè)生產(chǎn)效率具有重要意義。由于采煤機(jī)常處于潮濕、粉塵顆粒多、電磁干擾嚴(yán)重等復(fù)雜井下運(yùn)行環(huán)境,時(shí)常出現(xiàn)軸承破損等采煤機(jī)關(guān)鍵部件故障。一旦出現(xiàn)此類故障,將導(dǎo)致整個(gè)煤礦生產(chǎn)過程停滯,乃至癱瘓。針對(duì)采煤機(jī)滾動(dòng)軸承故障,本文在深入研究與分析采煤機(jī)運(yùn)行環(huán)境、工作特點(diǎn)、影響因素等導(dǎo)致采煤機(jī)軸承故障的基礎(chǔ)上,提出一種將RBF神經(jīng)網(wǎng)絡(luò)(RBF, RBF Neural Network)與自適應(yīng)步長(zhǎng)螢火蟲算法(ASGSO, self-Adaptive Step Glowworm Swarm Optimization)相耦合的擬合算法實(shí)現(xiàn)對(duì)采煤機(jī)滾動(dòng)軸承故障非線性系統(tǒng)的有效辨識(shí)。RBF神經(jīng)網(wǎng)絡(luò)具備了強(qiáng)大的時(shí)變數(shù)據(jù)處理能力及網(wǎng)絡(luò)穩(wěn)定性,因此更能直接表征本質(zhì)非線性系統(tǒng)的動(dòng)態(tài)特性。以小波包和RBF神經(jīng)網(wǎng)絡(luò)為基礎(chǔ),提出了由小波包分解提取各個(gè)節(jié)點(diǎn)特征能量譜與自適應(yīng)步長(zhǎng)螢火蟲算法優(yōu)化的RBF神經(jīng)網(wǎng)絡(luò)進(jìn)行分類辨識(shí)的采煤機(jī)滾動(dòng)軸承故障診斷方法。對(duì)振動(dòng)傳感器輸出的信號(hào)進(jìn)行小波包分解,運(yùn)用基于代價(jià)函數(shù)的局域判別基(LDB)算法對(duì)小波包分解進(jìn)行裁剪,獲取最優(yōu)的特征能量譜,經(jīng)處理后作為特征向量訓(xùn)練ASGSO-RBF神經(jīng)網(wǎng)絡(luò),建立診斷模型。充分利用經(jīng)改進(jìn)后的ASGSO算法強(qiáng)大的全局多目標(biāo)搜索能力對(duì)RBF的權(quán)值與中心、寬度在求解空間中進(jìn)行快速精確的在線搜索,并結(jié)合辨識(shí)理論建立基于ASGSO-RBF耦合算法的采煤機(jī)滾動(dòng)軸承故障辨識(shí)系統(tǒng)。利用井下實(shí)際采集到的各影響因素監(jiān)測(cè)數(shù)據(jù)進(jìn)行辨識(shí)實(shí)驗(yàn),結(jié)果表明：在較高學(xué)習(xí)效率的前提下,其辨識(shí)精度和泛化能力明顯強(qiáng)于單一的RBF神經(jīng)網(wǎng)絡(luò)、GSO-RBF耦合模型以及工程常用的BP神經(jīng)網(wǎng)絡(luò)且具有較強(qiáng)的魯棒性。該方法對(duì)井下采煤機(jī)故障災(zāi)害的防治提供了充分的理論指導(dǎo)。
[Abstract]:The coal resource has a very important position and function in the energy system structure of our country. As the key equipment in the coal mine production process, the coal mining machine is a collection of machinery, electronics, electricity and transmission. The safe and stable operation of shearer equipment is of great significance for ensuring the safety of coal production and promoting the production efficiency of enterprises. Serious electromagnetic interference and other complex downhole operating environment, such as bearing breakage and other key parts of coal mining machine faults. Once such faults occur, the whole coal mine production process will be stalled or even paralyzed. In view of the fault of the shearer rolling bearing, On the basis of deeply studying and analyzing the running environment, working characteristics and influencing factors of the shearer bearing, this paper studies and analyses the bearing failure of the shearer. A fitting algorithm coupled with RBF neural network (RBF Neural network) and adaptive step size firefly algorithm (ASGSO, self-Adaptive Step Glowworm Swarm optimization) is proposed to effectively identify the nonlinear system of rolling bearing fault of shearer. Large time-varying data processing capacity and network stability, Therefore, the dynamic characteristics of essential nonlinear systems can be expressed more directly, based on wavelet packets and RBF neural networks. This paper presents a fault diagnosis method for roller bearing of shearer based on RBF neural network optimized by wavelet packet decomposition and adaptive step size firefly algorithm. Wavelet packet decomposition, The local discriminant LDB (LDB) algorithm based on cost function is used to cut wavelet packet decomposition to obtain the optimal characteristic energy spectrum. After processing, the ASGSO-RBF neural network is trained as a feature vector. The diagnosis model is established. The powerful global multi-objective search ability of the improved ASGSO algorithm is fully utilized to search the weights and centers of the RBF, and the width of the RBF is searched quickly and accurately in the solution space. Combined with the identification theory, the fault identification system of shearer rolling bearing based on ASGSO-RBF coupling algorithm is established. The identification experiment is carried out by using the monitoring data of various factors collected from underground. The results show that: under the premise of higher learning efficiency, Its identification accuracy and generalization ability are obviously stronger than the single RBF neural network GSO-RBF coupling model and the BP neural network commonly used in engineering. This method provides sufficient theoretical guidance for the prevention and treatment of underground shearer fault disaster.
【學(xué)位授予單位】：遼寧工程技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TD421.6

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1 張俊男;基于ASGSO-RBF算法的采煤機(jī)滾動(dòng)軸承故障診斷研究[D];遼寧工程技術(shù)大學(xué);2015年

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本文編號(hào)：1586702