Motor temperature rise forecast method based on radial basis function (RBF) neural network

A neural network and temperature rise technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems that cannot describe the temperature change process of the motor, loss, motor burnout, etc.

Inactive Publication Date: 2015-12-16
DONGHUA UNIV
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Problems solved by technology

However, the motor may start frequently in a complex working environment, which may cause the motor to burn out and cause major losses
Although the thermal protection of motors can be measured by some traditional methods, such as thermometer method,

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  • Motor temperature rise forecast method based on radial basis function (RBF) neural network
  • Motor temperature rise forecast method based on radial basis function (RBF) neural network
  • Motor temperature rise forecast method based on radial basis function (RBF) neural network

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[0020] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0021] The present invention is to set up a motor temperature rise model τ=τ ∞ +(τ 0 -τ ∞ )e -t / T , and then the steady-state temperature rise τ, an important parameter in the mathematical model predicted by the neural network ∞ and the temperature rise time constant T are substituted into the above model to obtain the temperature rise τ of the motor at time t, and then predict the real-time temperature of the motor.

[00...

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Abstract

The invention proposes a motor temperature rise forecast method based on a radial basis function (RBF) neural network. According to the method, a motor temperature rise parameter of a modern car window is forecasted by building the RBF neural network, and meanwhile, the parameter forecasted by the neural network is substituted into a built motor temperature rise mathematic model so as to achieve real-time forecast on the motor temperature. The RBF neural network selected by the invention is an optimal network in a forward network, and is higher in algorithm speed than an ordinary back propagation (BP) neural network algorithm, the classification ability is high, the convergence rate is high during the studying process, and the problem of local optimum of a studying method is also avoided.

Description

technical field [0001] The invention relates to a method for predicting motor temperature rise based on RBF neural network, and belongs to the technical field of motor thermal protection and real-time temperature monitoring. Background technique [0002] With the vigorous development of today's social economy and science and technology, the capacity of motors used in the field of automation has gradually expanded, and the structure and composition of the system have become more and more complex. However, the motor may start frequently in a complex working environment, which may cause the motor to burn out and cause major losses. Although the thermal protection of motors can be measured by some traditional methods, such as thermometer method, pasting temperature measuring paper method and resistance method, etc., these methods have some shortcomings, and they cannot be more reliable through some specific models. Describe the temperature change process in the working conditio...

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Application Information

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IPC IPC(8): G06F17/50
Inventor 周武能张杨潘亮
Owner DONGHUA UNIV
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