Dynamic balance detection and control method based on biogeographic intelligent optimization support vector machine algorithm

An intelligent optimization algorithm and support vector machine technology, applied in static/dynamic balance testing, machine/structural component testing, measuring devices, etc., can solve problems such as dynamic balance detection and control methods that are not mentioned, and improve convergence accuracy. , the effect of simple calculation and few setting parameters

Active Publication Date: 2017-01-25
SHANGHAI UNIV
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Problems solved by technology

In the disclosed inventions or documents, there is no mention of examples of dynamic balance detection and control methods based on biogeographic intelligent optimization support vector machine algorithms

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  • Dynamic balance detection and control method based on biogeographic intelligent optimization support vector machine algorithm
  • Dynamic balance detection and control method based on biogeographic intelligent optimization support vector machine algorithm
  • Dynamic balance detection and control method based on biogeographic intelligent optimization support vector machine algorithm

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Embodiment 1

[0044] Such as figure 1 As shown, the dynamic balance detection control method based on the biogeography intelligent optimization support vector machine algorithm includes the following steps:

[0045] (1.1), collect online data, that is, collect data from sensors installed on the dynamic balance system in real time. The collected data includes real-time data of a rotational speed sensor and several vibration sensors.

[0046] (1.2), input offline data, that is, input the historical measurement data of the dynamic balance system. Select the scale of offline data according to the actual sampling situation. If the actual sampling situation is not good and the effective data collection speed is slow, you can input a larger scale of offline data to speed up the modeling.

[0047] (1.3), set the model accuracy requirements, that is, set the model accuracy requirements for dynamic balance system modeling. The demand for model accuracy has a great influence on the modeling speed a...

Embodiment 2

[0055] This embodiment is basically the same as Embodiment 1, and the special features are as follows:

[0056] Such as figure 2 The above-mentioned biogeography intelligent optimization support vector machine algorithm with Kalman filter includes the following steps:

[0057] (2.1), initialize the parameters of the biogeographic intelligent optimization algorithm BBO.

[0058] Set the number D of the fitness vector SIV and the maximum capacity of the habitat population S max , population size nh, number of iterations N, maximum value of immigration rate function I, maximum value of emigration rate function E, maximum mutation probability m max , Mobility P mod and elite individual Z.

[0059] (2.2), initialize the basic parameters of the support vector machine.

[0060] Set the support vector machine SVM model type to epsilon-SVR, the kernel function type to Gaussian radial basis kernel function and some related default parameters.

[0061] The model optimization funct...

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Abstract

The invention discloses a dynamic balance detection control method based on the biogeographical intelligent optimization support vector machine algorithm. The method comprises the following steps: (1) collecting online data, (2) inputting off-line data, (3) setting the requirement for model precision, (4) preprocessing the data, (5) judging whether a dynamic balance system model exists, (6) judging whether the system unbalance exceeds a threshold value, (7) carrying out the biogeographical intelligent optimization support vector machine algorithm with Kalman filtering, (8) establishing the dynamic balance system model, and (9) carrying out dynamic balance adjustment. According to the method, system modeling is carried out based on the support vector machine algorithm according to the small sample and nonlinearity characteristics of a dynamic balance system, penalty factors and kernel function parameters of the support vector machine algorithm are optimized based on the biogeographical intelligent optimization algorithm, Kalman filtering is adopted for improving the robustness and accuracy of the whole algorithm in consideration of noise interference in a dynamic balance system spot, and therefore high-precision detection control over the dynamic balance system can be achieved.

Description

technical field [0001] The invention belongs to the technical field of automatic control and artificial intelligence, and in particular relates to a dynamic balance detection and control method based on a biogeographic intelligent optimization support vector machine algorithm. [0002] technical background [0003] In automation control, due to the mechanical wear and safety problems that may exist in the case of rotating machinery working for a long time, dynamic balancing technology highlights its importance. If the mass distribution of rotating machinery can be adjusted in real time while the rotating machinery is working , it can reduce the influence of the rotation unbalance on the rotating shaft, so that the rotating machinery can run safely for a long time. [0004] At present, the research on dynamic balance technology mostly adopts traditional methods, such as trial method, influence factor method, etc. These methods have high requirements for the working site enviro...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M1/38
Inventor 王海宽钱世俊费敏锐方骏周志境
Owner SHANGHAI UNIV
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