本文選題：等譜流形學習算法 + 降維�。� 參考：《蘭州理工大學》2017年碩士論文

【摘要】：當前隨著社會經(jīng)濟和科學技術的不斷發(fā)展,各類電機出現(xiàn)在工業(yè)生產(chǎn)和人們的日常工作中并且它所起到的作用也越來越大。而電機的故障一旦發(fā)生,輕則會影響人們的生產(chǎn)生活,重則會危害人的生命安全以及造成嚴重的經(jīng)濟損失。因此,為了滿足工業(yè)自動化對電機的高品質需求,研究應用于診斷電機故障的方法在現(xiàn)代生活中具有重大意義。隨著科技的不斷發(fā)展,電機診斷更需要準確性和快速性,也就導致在電機故障診斷領域存在一些技術難題。例如高維故障數(shù)據(jù)的特征提取不精確導致診斷精度低的難題、單一診斷方法的局限性、較低的運算效率等問題�；谏鲜銮闆r,本文主要研究了等譜流形學習算法、狼群算法、組合診斷模型以及云計算來解決問題,并最后通過美國凱斯西儲大學的數(shù)據(jù)作為實例進行仿真驗證。具體內(nèi)容概況如下:本文首先針對電機故障的高維數(shù)據(jù)在提取特征集時不精確導致其診斷精度低的問題,引入了等譜流形學習算法進行降維處理,即采用此算法對經(jīng)過主成分分析降維處理后的數(shù)據(jù)進行二次降維,此算法通過其修正后的稀疏重構權矩陣構建鄰接圖,使得經(jīng)降維后同類樣本更聚集,不同類樣本更疏散,有效實現(xiàn)了高維數(shù)據(jù)的去冗降維,最后將其和主成分分析進行比較分析,其效果顯著。然后針對診斷精度易受其等譜流形學習算法、最小二乘支持向量機參數(shù)影響的問題,通過狼群算法對其中參數(shù)進行優(yōu)化。在優(yōu)化最小二乘支持向量機的參數(shù)組合時使用Fisher準則函數(shù)作為所選參數(shù)的優(yōu)劣標準,而在優(yōu)化等譜流形學習算法的參數(shù)時使用的適應度函數(shù)為最近鄰分類法的識別率,利用優(yōu)化的參數(shù)建立最優(yōu)的診斷模型。仿真實驗表明該模型有很好的診斷結果,并在優(yōu)化參數(shù)時將狼群算法和粒子群優(yōu)化算法進行了比較分析,其診斷效果顯著。最后針對電機故障征兆的多樣性和單一診斷方法的局限性等問題,本文通過采用組合診斷模型進行解決。本文將最小二乘支持向量機、模糊神經(jīng)網(wǎng)絡以及RBF神經(jīng)網(wǎng)絡相結合并按照最小化診斷誤差平方和來形成最優(yōu)的組合模型,通過實驗得到的結果可以看出組合診斷模型能夠對單一方法帶來的缺陷進行彌補,并與這三種單一診斷方法相比,具有更高的故障識別率,魯棒性也更好。最后利用云平臺技術解決了組合復雜模型帶來的運行時間長等問題,有效地提高了故障診斷的效率。
[Abstract]:At present, with the development of social economy and science and technology, all kinds of motors appear in industrial production and people's daily work, and it plays a more and more important role. Once the fault of motor occurs, light will affect people's production and life, heavy will endanger the safety of human life and cause serious economic losses. Therefore, in order to meet the high quality demand of motor in industrial automation, it is of great significance to study the method of fault diagnosis of motor in modern life. With the development of science and technology, motor diagnosis needs more accuracy and rapidity, which leads to some technical problems in the field of motor fault diagnosis. For example, the inaccuracy of feature extraction of high-dimensional fault data leads to the difficulty of low diagnostic accuracy, the limitation of single diagnosis method, and the low computational efficiency. Based on the above situation, this paper mainly studies isospectral manifold learning algorithm, wolf swarm algorithm, combined diagnosis model and cloud computing to solve the problem, and finally through the case of Western Reserve University data as an example to verify the problem. The specific contents are as follows: firstly, aiming at the problem of low diagnostic accuracy caused by the imprecision of the high dimensional data of motor fault extraction, the isospectral manifold learning algorithm is introduced to reduce the dimension. That is to say, this algorithm is used to reduce the dimension of the data after dimension reduction by principal component analysis (PCA). The algorithm constructs the adjacent graph by its modified sparse reconstruction weight matrix, which makes the similar samples gather more after dimensionality reduction, and the different classes of samples are more evacuated. The dimensionality reduction of high dimensional data is realized effectively, and the results are compared with that of principal component analysis (PCA). Then, aiming at the problem that the diagnosis accuracy is easily affected by the parameters of the isospectral manifold learning algorithm and the least squares support vector machine, the parameters are optimized by the wolf swarm algorithm. Fisher criterion function is used to optimize the parameter combination of least squares support vector machine, while the fitness function used in optimizing the parameters of isospectral manifold learning algorithm is the recognition rate of nearest neighbor classification. The optimal diagnostic model is established by using the optimized parameters. The simulation results show that the model has good diagnostic results, and the wolf swarm optimization algorithm and particle swarm optimization algorithm are compared with each other in the optimization of parameters, and the diagnosis effect is remarkable. Finally, aiming at the diversity of motor fault symptoms and the limitation of single diagnosis method, the combined diagnosis model is adopted to solve the problems. In this paper, the least square support vector machine, fuzzy neural network and RBF neural network are combined to form the optimal combination model according to minimizing the sum of diagnostic error squared. The experimental results show that the combined diagnosis model can make up for the defects brought by the single method, and compared with the three single diagnosis methods, it has higher fault identification rate and better robustness. Finally, the long running time caused by the combined complex model is solved by using cloud platform technology, and the efficiency of fault diagnosis is improved effectively.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM307

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本文編號：2065784