Pattern recognition method capable of holding vectorial machine for equipment fault diagnosis

Abstract

The invention discloses a pattern recognition method of a supporting vector machine for fault diagnosis of a device, in particular to a device failure pattern recognition method of a reduction Tikhonov regularization supporting vector machine auto-selected by utilizing parameters. The invention comprises the following steps: (1) achieving a derivation process of Tikhonov regularization supporting models of the vector machine; (2) constructing a subsample set to reject redundant sample information by utilizing a pruning method so as to build models of the reduction Tikhonov regularization supporting vector machine; (3) taking classification accuracy as a fitness function, auto-selecting width parameters and balance parameters of a Gauss kernel function of the reduction Tikhonov regularization supporting vector machine by utilizing a heredity algorithm and by taking classification accuracy as a fitness function so as to build the models of the reduction Tikhonov regularization supporting vector machine auto-selected by utilizing parameters; and (4) verifying the pattern recognition method by utilizing failure samples of a motor device so as to indicate superiority of the method proposed in the invention.

Description

technical field [0001] The invention relates to a method in the field of equipment fault diagnosis, in particular to a method for identifying equipment fault patterns using a reduced Tikhonov regularized support vector machine with automatic parameter selection. Background technique [0002] In order to avoid the problem of excessive reliance on professional and technical personnel for equipment fault diagnosis, many scholars have introduced neural networks, expert systems, and clustering algorithms into the field of equipment fault diagnosis, and have achieved certain results in practice. However, these technologies still have some problems. For example, based on The fault diagnosis method of artificial neural network is based on the principle of minimum empirical risk. During the learning process, it is easy to fall into the local minimum, and over-learning phenomenon occurs, resulting in a decline in generalization ability, which affects the diagnosis effect. Further, the ...

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Problems solved by technology

However, since all fault samples are used to construct the kernel function matrix at one time, the kernel function matrix and sample matrix will inevitably occupy more memory space. Therefore, when dealing with large data sets, the dimension of the kernel function matrix is ​​too large. On the one hand, It brings a huge burden on the memory space, and on the other hand affects the running speed and execution efficiency; further, when the Tikhonov regularized support vector machine is classifying the failure mode, the width parameter of the Gaussian kernel function controls the Tikhonov regularized support vector machine. The regression and classification ability of the support vector machine, and the balance parameter is used to balance the training error and the hyperplane separation gap. The optimization of the two parameters plays a very important role in improving the fault classification performance of the support vector machine. At present, scholars are choosing Tikhonov regularization support The balance parameters and kernel function parameters of the vector machine adopt the experimental fit method, which is very time-consuming and cannot obtain the optimal parameters, so the optimal classification accuracy cannot be obtained

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