本文關(guān)鍵詞：基于電流信號的轉(zhuǎn)子系統(tǒng)故障診斷與采煤機(jī)截割工況識別　出處：《太原理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 電機(jī)電流信號 轉(zhuǎn)子系統(tǒng) 故障診斷 特征提取 主成分分析 總體平均經(jīng)驗(yàn)?zāi)B(tài)分解

【摘要】：隨著中國制造2025和工業(yè)4.0的提出,機(jī)械設(shè)備作為生產(chǎn)制造企業(yè)的核心裝備,發(fā)揮著舉足輕重的作用。為保證設(shè)備安全、可靠、高效地運(yùn)行,避免惡性事故的發(fā)生和經(jīng)濟(jì)的損失,開展機(jī)械設(shè)備的故障診斷和運(yùn)行狀態(tài)監(jiān)測,具有非常重要的意義。近年來,電機(jī)電流特征分析法作為一種新興檢測技術(shù)逐漸受到廣大學(xué)者青睞,通過監(jiān)測電機(jī)電流信號進(jìn)行機(jī)械故障診斷和狀態(tài)識別已經(jīng)成為一個(gè)研究熱點(diǎn),本文在此基礎(chǔ)上對電流信號的特征提取方法、轉(zhuǎn)子系統(tǒng)故障診斷和采煤機(jī)截割工況識別方法進(jìn)行了探索研究,主要工作內(nèi)容如下:1、從理論角度分析了負(fù)載扭矩變化對電機(jī)電流信號的影響,負(fù)載扭矩波動體現(xiàn)在電機(jī)電流信號頻譜上會產(chǎn)生頻率調(diào)制現(xiàn)象,即電流基頻e0f兩側(cè)出現(xiàn)ieff?0的頻率分量。通過在Matlab/Simulink中建立電機(jī)模型,仿真驗(yàn)證了理論的正確性。2、針對電機(jī)電流信號特征提取困難,特征頻率易被工頻湮沒的問題,將總體平均經(jīng)驗(yàn)?zāi)B(tài)分解(EEMD)引入電流信號的處理中,利用改進(jìn)小波閾值去噪、EEMD及互相關(guān)分析相結(jié)合方法對電流信號進(jìn)行處理,通過在轉(zhuǎn)子試驗(yàn)臺上施加正弦扭矩激勵來模擬扭矩變化,采集電機(jī)電流信號進(jìn)行處理。試驗(yàn)結(jié)果表明,利用互相關(guān)分析篩選IMF分量的方法,能夠快速有效地進(jìn)行IMF分量的選取并抑制50Hz工頻及其諧波的干擾,提取扭矩波動的頻率,從而證明了該方法在實(shí)際應(yīng)用中的可行性。3、針對轉(zhuǎn)子系統(tǒng)的不平衡、不對中故障,利用EEMD-PCA的方法提取電機(jī)電流信號的幅值域和時(shí)頻域特征參數(shù),在轉(zhuǎn)子系統(tǒng)故障模擬試驗(yàn)臺上采集電機(jī)電流信號,利用BP神經(jīng)網(wǎng)絡(luò)和支持向量機(jī)對故障進(jìn)行識別。試驗(yàn)結(jié)果表明利用EEMD-PCA進(jìn)行特征提取能夠有效提高識別效果,且EEMD-PCA-SVM識別準(zhǔn)確率達(dá)到了93.4%,高于EEMD-PCA-BP的80.0%。4、針對采煤機(jī)截割過程中的煤巖工況識別問題,利用小波包能量法對電機(jī)電流信號進(jìn)行特征提取,得到特征向量,再從特征向量和支持向量機(jī)參數(shù)兩個(gè)方面對識別算法進(jìn)行優(yōu)化,試驗(yàn)結(jié)果表明優(yōu)化后的PSO-SVM算法對不同滾筒轉(zhuǎn)速、不同截割高度下的煤巖截割工況識別率均達(dá)到了90%以上,效果比較理想。
[Abstract]:With the development of manufacture in China 2025 and 4.0, mechanical equipment, as the core equipment of manufacturing enterprises, plays an important role in order to ensure the safety, reliability and efficient operation of the equipment. In recent years, it is very important to avoid the occurrence of malignant accidents and economic losses, and to carry out fault diagnosis and operation state monitoring of machinery and equipment. As a new detection technology, the motor current characteristic analysis method has gradually been favored by the majority of scholars. Mechanical fault diagnosis and state identification by monitoring the motor current signal has become a research hotspot. In this paper, the current signal feature extraction method, rotor system fault diagnosis and shearer cutting condition identification methods are explored and studied. The main work is as follows: 1. The influence of load torque variation on motor current signal is analyzed theoretically. Load torque fluctuation is reflected in the frequency modulation phenomenon in the frequency spectrum of motor current signal, that is, ieffs appear on both sides of current base frequency e0f. By establishing the motor model in Matlab/Simulink, the correctness of the theory is verified by simulation. It is difficult to extract the characteristics of the motor current signal. The characteristic frequency is easy to be annihilated by power frequency. The total average empirical mode decomposition (EEMD) is introduced into the current signal processing, and the improved wavelet threshold is used to de-noise. The current signal is processed by EEMD and cross-correlation analysis. The torque change is simulated by applying sinusoidal torque excitation on the rotor test-bed, and the motor current signal is collected for processing. The test results show that. By using the method of cross-correlation analysis to select IMF components, the selection of IMF components can be carried out quickly and effectively, and the interference of 50Hz power frequency and its harmonics can be suppressed, and the frequency of torque fluctuation can be extracted. It is proved that the method is feasible in practical application. The method is aimed at the unbalance of rotor system and misalignment fault. The amplitude range and time-frequency characteristic parameters of motor current signal are extracted by EEMD-PCA method, and the motor current signal is collected on the rotor system fault simulation test platform. BP neural network and support vector machine are used to identify the fault. The experimental results show that the feature extraction using EEMD-PCA can effectively improve the recognition effect. The accuracy of EEMD-PCA-SVM recognition is 93.4, which is higher than that of EEMD-PCA-BP (80.0.4). The wavelet packet energy method is used to extract the feature of the motor current signal and the eigenvector is obtained. Then the recognition algorithm is optimized from two aspects: the eigenvector and the support vector machine parameters. The experimental results show that the optimized PSO-SVM algorithm can recognize the cutting conditions of coal and rock at different drum speed and cutting height, and the recognition rate of coal and rock cutting conditions is more than 90%, and the effect is satisfactory.
【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】：2017
【分類號】：TD421.6

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