Method for diagnosing leading screw faults

A fault diagnosis and fault diagnosis model technology, applied in the direction of biological neural network models, can solve the problems of manual feature extraction, limited nonlinear expression ability of shallow network, etc., and achieve strong nonlinear expression ability and good fault identification ability , the effect of strong discrimination ability

Inactive Publication Date: 2015-11-11
SOUTHWEST JIAOTONG UNIV
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  • Abstract
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Problems solved by technology

[0005] The purpose of the present invention is to provide a lead screw fault diagnosis method, which can effectively solve the problems of the existing lead screw intelligent fault diagnosis system, which is difficult to manually extract features and has limited non-linear expression ability in the application of shallow networks

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  • Method for diagnosing leading screw faults
  • Method for diagnosing leading screw faults
  • Method for diagnosing leading screw faults

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Embodiment Construction

[0046] The present invention will be further described below in conjunction with the drawings. A lead screw fault diagnosis model based on feature learning, comprising the following steps:

[0047] 1. Establish a network structure with feature learning ability

[0048] 1. Determine the structure of the diagnostic model

[0049] The diluted self-encoder deep neural network structure is adopted, the recognition model of the network is selected from the Softmax regression classifier, and the hidden layer of the network is selected from the second layer.

[0050] 2: Determine the number of inputs to the fault diagnosis model

[0051] The fault diagnosis model has m input nodes, and the input signals of m nodes constitute an input vector x, expressed as follows:

[0052] x=(x1 ,x 2 ,...,x m )

[0053] where x i is the i-th input node of the model, i=1,2,...,m;

[0054] In this example, the number m of input nodes is 12. Respectively time-domain eigenvalues: root mean squar...

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Abstract

The invention discloses a method for diagnosing leading screw faults, belongs to the technical field of machinery fault diagnosis, and especially relates to ball screw fault diagnosis. The problem that a conventional intelligent leading screw fault diagnosis system is difficult for manual extraction of characteristics and limited in applying shallow-layer network non-linear expression capability. A diluted self-coding deep neural network structure is employed, and a network identification model employs a Softmax regression classifier to determine the hidden layer quantity of a network structure. The quantity of the input terminals and the quantity of the output terminals of a fault diagnosis model are determined, a training sample set, pre-training, fine tuned training and a fault diagnosis model test sample set are prepared, and the fault diagnosis performance of the fault diagnosis model is tested. Data sections in the test sample are integrally input continuously and sequentially, and the output quantity of the model is recorded to obtain an actual output list of the model. The ideal output list of the model is compared with the designed output list to obtain the fault performance test and evaluation result of the fault diagnosis model.

Description

technical field [0001] The invention belongs to the technical field of mechanical fault diagnosis, in particular to the fault diagnosis of a ball screw. Background technique [0002] With the advancement of science and technology and the development of industrial needs, ball screws continue to develop in the direction of complexity, high speed, high efficiency, light weight, miniature or large size on the one hand, but face more harsh working and operating environments on the other hand. While meeting the requirements of the equipment, the potential possibility and ways of screw failure are also increasing accordingly, and once the screw fails, it may damage the entire equipment or even affect the entire production process, causing huge economic losses, and may also lead to Catastrophic casualties and serious social impact. Therefore, it is very important to implement on-line monitoring of the ball screw, to study the mechanism of equipment failure, and to establish an effe...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/02
Inventor 郭亮高宏力张一文黄海凤李世超文娟张杰
Owner SOUTHWEST JIAOTONG UNIV
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