A friction noise prediction method based on a BP neural network

A BP neural network and friction noise technology, applied in the field of friction noise prediction based on BP neural network, can solve the problems of unstable frequency, long time calculation, difficult data, etc., and achieve the effect of strong nonlinear mapping ability

Pending Publication Date: 2019-04-26
UNIV OF SHANGHAI FOR SCI & TECH
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Problems solved by technology

For the prediction method of friction noise, there are finite element-based brake squeal modeling and analysis methods, which include the frequency domain complex eigenvalue method and the time domain transient dynamics analysis method. The frequency domain complex eigenvalue method cannot clearly point out The mechanism of brake squeal, and the assumption of linearization lacks consideration of time-var

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  • A friction noise prediction method based on a BP neural network
  • A friction noise prediction method based on a BP neural network
  • A friction noise prediction method based on a BP neural network

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[0019] Examples:

[0020] The friction noise prediction method based on BP neural network of this embodiment is used to predict the frequency of friction noise generated during the braking process of a brake on a vehicle, and includes the following steps:

[0021] Step 1. Obtain friction noise and data samples related to friction noise p 1i , Where i=1, 2,...,n.

[0022] Data sample p 1i Including contact pressure, speed, and friction noise frequency.

[0023] Step 2. To the data sample p 1i Perform normalization processing to obtain corresponding normalized data samples p 2i , And the data sample p 2i Divided into two groups, namely the first normalized data sample and the second normalized data sample.

[0024] The formula for normalization is:

[0025]

[0026] Among them, max(p) is the maximum value before normalization, and min(p) is the minimum value before normalization.

[0027] Step 3: Use newff, a function specially created for BP neural network in MATLAB, to establish a frictio...

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Abstract

The invention provides a friction noise prediction method based on a BP (Back Propagation) neural network, which is used for predicting the frequency of friction noise generated in the braking processof a brake on a vehicle and comprises the following steps: step 1, acquiring the friction noise and a data sample p1i associated with the friction noise; Step 2, performing normalization processing on the data samples p1i to obtain corresponding normalized data samples p2i respectively, and dividing the data samples p2i into two groups, namely a first normalized data sample and a second normalized data sample; Step 3, establishing a friction noise prediction model by using a function newff specially establishing a BP neural network in MATLAB; 4, inputting the first normalized data sample intoa friction noise prediction model to obtain an updated network weight and an updated network threshold; And 5, inputting the second normalized data sample into the friction noise prediction model until the training error is 0.01, and determining a final network weight and a final network threshold to obtain a qualified friction noise prediction model.

Description

technical field [0001] The invention relates to a friction noise prediction method, in particular to a friction noise prediction method based on a BP neural network. Background technique [0002] Noise is often generated during the friction process. The noise generated by friction has a wide frequency range, high noise intensity, and complex causes and changes. The prediction of friction noise has always been a hot issue in the field of tribology. For the prediction method of friction noise, there are finite element-based brake squeal modeling and analysis methods, which include the frequency domain complex eigenvalue method and the time domain transient dynamics analysis method. The frequency domain complex eigenvalue method cannot clearly point out The mechanism of brake squeal, and the assumption of linearization lacks consideration of time-varying loads and material properties and other unsteady characteristics, often resulting in "over-prediction" and "under-prediction...

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Application Information

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IPC IPC(8): G06F17/50G06N3/08
CPCG06N3/084G06F2119/10G06F30/15G06F30/20
Inventor 王书文王腾迪
Owner UNIV OF SHANGHAI FOR SCI & TECH
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