Rolling bearing residual life prediction method based on improved multi-granularity cascade forest

A rolling bearing and life prediction technology, which is applied in the testing of mechanical components, testing of machine/structural components, measuring devices, etc., can solve the problems of low computing efficiency and poor accuracy

Active Publication Date: 2020-09-18
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

[0007] The technical problem to be solved by the present invention is: In order to solve the problems of poor accuracy and low operation efficiency in the existing artificial intelligence method in the

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  • Rolling bearing residual life prediction method based on improved multi-granularity cascade forest
  • Rolling bearing residual life prediction method based on improved multi-granularity cascade forest
  • Rolling bearing residual life prediction method based on improved multi-granularity cascade forest

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Embodiment Construction

[0036] give together Figures 1 to 17 , the realization of the inventive method is described as follows:

[0037] 1 Feature extraction

[0038] 1.1 Vibration signal preprocessing

[0039] Under normal conditions, monotonic and stable features are more conducive to improving the prediction accuracy of the remaining life of the rolling bearing, so the present invention performs iterative processing on the obtained frequency domain amplitude features. The calculation method of the iterative feature IF is shown in formula (1):

[0040]

[0041] In the formula: l represents the length of the one-dimensional time series, IF t Indicates the iterative feature at the current time t.

[0042] 1.2 CNN deep feature extraction

[0043] CNN is a multi-layer deep neural network that can handle overfitting problems well. A typical convolutional neural network consists of a convolutional layer, a pooling layer, and a fully connected layer, such as figure 1 shown.

[0044] In the conv...

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Abstract

The invention discloses a rolling bearing residual life prediction method based on an improved multi-granularity cascade forest, belongs to the field of rolling bearing residual life prediction, and solves the problems of poor precision and low operation efficiency of an existing artificial intelligence method in rolling bearing residual life prediction. The method includes: firstly, carrying outiterative computation on a rolling bearing frequency domain signal obtained through fast Fourier transform to obtain iterative features; replacing a multi-granularity scanning structure in the multi-granularity cascade forest with a convolutional neural network, extracting deep features of iterative features by using the convolutional neural network, and constructing a performance degradation feature set; and then integrating a single CATBoost model capable of achieving GPU parallel acceleration, introducing a determination coefficient R2 to construct a CasCatBoost structure so as to improve the representation capability of the model, and selecting the average life percentage p of the last cascade layer of the model to represent output; and finally, fitting p by using a linear function topredict the residual life of the bearing. The method has relatively high operation efficiency and prediction precision.

Description

technical field [0001] The invention relates to a method for predicting the remaining life of a rolling bearing, which belongs to the field of predicting the remaining life of a rolling bearing. Background technique [0002] Rolling bearings are one of the key components of rotating machinery, and their health status directly affects the operation performance of the entire equipment. Accurately predicting the life degradation trend of rolling bearings can provide valuable status information and sufficient response time for equipment maintenance. Normal operation and reduced maintenance costs of rolling bearings are of great significance [1] . [0003] Multi-grained cascade forest (multi-grained cascade forest, gcForest) is a deep learning model based on multi-grained scanning and cascade forest structure, in which the cascade forest consists of two random forests [2] . Because the multi-grain cascade forest has the advantages of less hyperparameters, model complexity can ...

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Application Information

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IPC IPC(8): G06F30/27G06K9/62G06K9/46G06K9/00G01M13/045
CPCG01M13/045G06V10/454G06F2218/08G06F2218/12G06F18/2148G06F18/24323
Inventor 王玉静王诗达康守强康成璐谢金宝王庆岩
Owner HARBIN UNIV OF SCI & TECH
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