Research On Method Of Rotating Machinery Fault Diagnosis Based On Manifold Learning

The basic problem of fault diagnosis is to obtain fault status by extracting feature, design decision functions based on information of equipment running,feature extraction and pattern recognition is the core of the problem.Machinery generally runs in a complex and poor working conditions,so the state signal has large amount of data, high nonlinearity characteristic, strong noise and interference.We are more and more difficult to manage and process these large and complex data, specific performance is that on the one hand we can get the amount of data, on the other hand it is hard to get more help in decision. Since 2000, manifold learning methods begin to develop and become research focus of the machine learning and pattern recognition. This method extends from euclidean space to manifold space in data analysis and state decision, efficiently and quickly to dig out the essential characteristics of the data from the high-dimensional data sets, to find the internal laws of data, and to achieve an accurate diagnosis.The main work include the following:In the perspective of nonlinear noise reducation, three manifold learning methods are proposed.In local tangent space alignment algorithm based on the intrinsic dimension, at first the intrinsic dimension of signal is obtained, and then the data in high dimensional phase space are reducated to the intrinsic dimension space, at last one-dimensional signal is obtained by reverse process. Algorithm avoid from blindness of dimension reduction targets' selection, improve the efficiency of noise reduction. In local tangent space mean reconstruction algorithm, low dimension data after noise reducation in local tangent space are reconstructed to the high dimension data by obtaining the mean of each point in global space. Algorithm's nature is the second noise reduction, not only enhancing effect of noise reduction,but also avoiding from the distortion of phase space data in the course of the global arrangement. Making use of restraining characteristic to colored noise of high-order cumulan,covariance matrix is constructed with a fourth-order cumulant function instead of construct second-order moment function covariance matrix,local tangent space alignment algorithm based on fourth-order cumulan is also proposed. This algorithm improves effect of noise reduction to signal with colored noise.In local fisher discriminant analysis, projection basis vectors obtained by calculating asymmetric the characteristic equatio are no-orthogonal,this leads to be difficult to data's reconstruction. To solve this question,iteration orthogonal and schur orthogonal local fisher discriminant methods are proposed. Orthogonal local fisher discriminant algorithm may effectively preserve the structure information of nearest neighbors in manifold space, and in the prosess of main features'seeking, class information are retained,and then main features obtained can maintain or even reduce the with-class divergence of the same category sample, at the same time make between-class distance becaome far, better achieve fault classification.In this paper,kernel method is introduced orthogonal local fisher discriminant analysis, iterative orthogonal and schur orthogonal local fisher fault diagnosis algorithm based on kernel method are proposed. Feature signals are projected into the high dimensional kernel space by nonlinear kernel function, and make orthogonal local fisher discriminant analysis in this space. Algorithm has achived transformation from linear to nonlinear method, and obtain better effect than linear orthogonal fault diagnosis.In the perspective of fault diagnosis based on the concept of local margin, local fuzzy clustering margin fisher discriminance is proposed. The fisher discriminant function is built by directly computing local within-divergence and between-class divergence using local margin points in neighborhood, instead of using all points, greatly increased the efficiency of the algorithm. In order to avoid from using possible pseudo margin points, a method is proposed ny means of fuzzy clustering algorithm to find the real local boundary.Meanwhile,by kener method local fuzzy clustering margin fisher discriminance becomes non-linear algorithm,and has better fault diagnosis ability.In the perspective of supervised manifold learning, increment local tangent space alignment(ILTSA) and linear local tangent space alignment(LLTSA) algorithm are improved, and nonlinear support vector machine(SVM) classifier is introduced, supervised ILTSA-SVM and supervised LLTSA-SVM are proposed. Two algorithms increaze generalization ability and fault diagnosis ability of the non-linear manifold learning.