【摘要】：軸承是旋轉(zhuǎn)類機(jī)械設(shè)備中使用的最為廣泛,同時(shí)也是損壞最快的部件之一。軸承能否處于正常的工作狀態(tài)直接影響著機(jī)械化生產(chǎn)的效率,當(dāng)軸承發(fā)生故障時(shí),機(jī)器會(huì)產(chǎn)生異常的振動(dòng)和噪聲。如何提取振動(dòng)故障信號(hào)的特征值并準(zhǔn)確地識(shí)別出故障類型是故障診斷的關(guān)鍵,為此本文提出了基于隱馬爾科夫模型的方法建立軸承故障診斷模型。隱馬爾科夫模型具有分類能力強(qiáng),訓(xùn)練樣本少,計(jì)算速度快的特點(diǎn),適合于非平穩(wěn)振動(dòng)信號(hào)分析,通過(guò)對(duì)采集到的振動(dòng)信號(hào)進(jìn)行特征提取,訓(xùn)練具有相應(yīng)狀態(tài)數(shù)的隱馬爾科夫模型,計(jì)算出相似概率,通過(guò)相似概率判斷出對(duì)應(yīng)的故障的類型。具體的研究?jī)?nèi)容如下：1.在對(duì)滾動(dòng)軸承故障信號(hào)進(jìn)行分析之前,首先對(duì)已提取到的軸承故障信號(hào)的特征數(shù)據(jù)進(jìn)行隱馬爾科夫模型的建立,并且驗(yàn)證了隱馬爾科夫模型在軸承故障診斷中的可行性。2.提出一種基于奇異值分解與隱馬爾科夫模型的故障識(shí)別方法,并建立隱馬爾科夫故障識(shí)別模型。首先對(duì)滾動(dòng)軸承故障信號(hào)數(shù)據(jù)進(jìn)行奇異值分解,將分解得到的奇異值矩陣進(jìn)行矢量量化并送入建立好的隱馬爾科夫模型中進(jìn)行故障類型的識(shí)別。通過(guò)實(shí)驗(yàn)驗(yàn)證奇異值分解和隱馬爾科夫模型相結(jié)合的方法在軸承故障診斷中的有效性。3.提出一種基于S變換與隱馬爾科夫模型的故障識(shí)別方法,并建立隱馬爾科夫故障識(shí)別模型。首先對(duì)滾動(dòng)軸承故障信號(hào)數(shù)據(jù)進(jìn)行S變換,然后對(duì)S變換后的時(shí)頻譜矩陣進(jìn)行奇異值分解,最后進(jìn)行矢量量化并送入建立好的隱馬爾科夫模型中進(jìn)行故障類型的識(shí)別。通過(guò)實(shí)驗(yàn)驗(yàn)證S變換和HMM相結(jié)合的方法在軸承故障診斷中的有效性。4.設(shè)計(jì)振動(dòng)信號(hào)采集與分析系統(tǒng),振動(dòng)傳感器采用聲發(fā)射傳感器PXR02,模數(shù)轉(zhuǎn)換芯片采用AD9225,以STM32為主控制器以及配置相應(yīng)的無(wú)線傳輸模塊進(jìn)行振動(dòng)信號(hào)的采集。使用VS的串口傳輸功能進(jìn)行數(shù)據(jù)的接收,最后通過(guò)Matlab進(jìn)行振動(dòng)信號(hào)數(shù)據(jù)的分析,以此來(lái)完成對(duì)軸承運(yùn)行狀態(tài)的實(shí)時(shí)監(jiān)測(cè)與故障的診斷。
[Abstract]:Bearing is one of the most widely used and damaged parts in rotating machinery. Whether the bearing is in the normal working state directly affects the efficiency of mechanized production. When the bearing fails, the machine will produce abnormal vibration and noise. How to extract the eigenvalue of vibration fault signal and identify the fault type accurately is the key of fault diagnosis. In this paper, a method based on hidden Markov model is proposed to establish the bearing fault diagnosis model. The hidden Markov model has the characteristics of strong classification ability, few training samples and fast calculation speed. It is suitable for the analysis of non-stationary vibration signals. The hidden Markov model with corresponding number of states is trained to calculate the similarity probability and the corresponding fault type is determined by the similarity probability. The specific research contents are as follows: 1. Before analyzing the rolling bearing fault signal, the characteristic data of the bearing fault signal have been established by the Hidden Markov Model, and the feasibility of the Hidden Markov Model in the bearing fault diagnosis has been verified. 2. A fault identification method based on singular value decomposition (SVD) and Hidden Markov Model (hmm) is proposed. Firstly singular value decomposition is used to decompose the fault signal data of rolling bearing. The singular value matrix is vector quantized and sent to the established hidden Markov model for fault type identification. The effectiveness of singular value decomposition (SVD) combined with Hidden Markov Model (hmm) in bearing fault diagnosis is verified by experiments. 3. A fault identification method based on S-transform and Hidden Markov model is proposed, and a hidden Markov fault identification model is established. First, the fault signal data of rolling bearing is transformed by S transform, then the time spectrum matrix after S transform is decomposed by singular value, finally vector quantization is carried out and the fault type is identified in the established hidden Markov model. The effectiveness of S-transform combined with HMM in bearing fault diagnosis is verified by experiments. 4. 4. The vibration signal acquisition and analysis system is designed. The vibration sensor adopts the acoustic emission sensor PXR02, A / D conversion chip and uses AD9225, and STM32 as the main controller and installs the corresponding wireless transmission module to collect the vibration signal. The serial port transmission function of VS is used to receive the data. Finally, the vibration signal data is analyzed by Matlab to realize the real-time monitoring and fault diagnosis of the bearing running state.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TH133.3

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