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Rolling bearing fault detection method based on multilayer residual network model

A technology of network model and rolling bearing, which is applied in biological neural network model, testing of mechanical components, testing of machine/structural components, etc., can solve problems such as economic loss, high accuracy, and inconsistency, and achieve good robustness, The effect of high classification accuracy and fast feature extraction speed

Inactive Publication Date: 2022-05-06
TANGSHAN IND VOCATIONAL TECHN COLLEGE
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Rolling bearings are not only widely used in production, they are also vulnerable parts in mechanical equipment. According to statistics, about 30% of the faults in rotating machinery are caused by the failure of rolling bearings; after using fault diagnosis technology, the probability of mechanical equipment failure can be extremely high The maintenance cost is 25%-50% lower than before; due to the long life of the rolling bearing, according to the national standard, there is a 90% reliability threshold as a standard part, even if the same material uses the same process on the same machine The same batch of bearings is produced, and its service life is also different; usually in reality, regular inspection and repairs are carried out based on the designed fatigue life, which has a great chance of damaging the equipment and causing economic losses;
[0004] At present, in the practice of enterprises, if it is a rolling bearing in an unimportant occasion, it mainly depends on the experience of engineers and technicians to judge whether there is a fault in the operation process of the bearing through touch and hearing; in more important occasions, it is necessary to regularly disassemble and check the operation of the bearing , but it is impossible to predict the fault state in a timely and effective manner; at the same time, the current detection algorithms for bearing faults mainly focus on the traditional use of time-domain signals as signal input features, and then use machine learning or deep learning methods for classification, with high accuracy However, it has not yet reached the conditions that meet the industrial testing applications of core components;

Method used

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  • Rolling bearing fault detection method based on multilayer residual network model
  • Rolling bearing fault detection method based on multilayer residual network model
  • Rolling bearing fault detection method based on multilayer residual network model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0062] Embodiment 1: with reference to attached figure 1 A rolling bearing fault detection method based on a multi-layer residual network model is shown, including the steps

[0063]Step1. Use the sensor to collect and extract the vibration signal of the bearing

[0064] Use an acceleration sensor to measure the vibration signals of the experimental bearing under normal conditions and under four conditions: inner ring fault, outer ring fault, and rolling element fault, and obtain sample point data including vibration signals in normal state and fault state;

[0065] Step2. Input the vibration signal collected by the sensor into the residual neural network, and repeatedly use the residual network to iterate the vibration signal between different convolutional layers

[0066] Step201. Input the vibration signal extracted in Step 1 into the convolution layer 1 for feature extraction;

[0067] Wherein: 1 input channel is arranged in the described convolution layer 1, 4 output ch...

Embodiment 2

[0100] Embodiment 2: as Figure 2-3 As shown, in order to prove the effectiveness of this method, the vibration data in the United States is used from the open data set of Case Western Reserve University in the United States:

[0101] (1) In this experiment, the bearings at the drive end of the motor and the fan end are used as the diagnostic objects, and single-point damage is introduced on the inner ring, rolling element, and outer ring of the test bearings by EDM to simulate three types of bearing failures. The fault damage scales are 0.007inch, 0.014inch and 0.021inch respectively, and then under similar working conditions (equal load, close speed), the fault signal is collected by the acceleration sensor on the upper side of the motor drive end, and the sampling frequency is 12kHz. According to the location and size of the bearing fault, the bearing categories are divided into ten categories, with 1024 data points as a sample, and 1600 samples are selected for each catego...

Embodiment 3

[0107] Embodiment 3: use faulty bearing to carry out comparative test to the fault detection method of different rolling bearings below, further the detection method of the present invention is verified:

[0108] Step 1: Select a set of rolling bearing data, and the failure conditions of the bearings are shown in Table 1:

[0109] Table 1: Fault information of rolling bearings

[0110]

[0111] Step 2: Use time-frequency feature+SV, ED+SV, wavelet+SV, CNN-SV and the multi-layer residual network model of the present invention to detect the faulty bearing described in step 1 respectively, and detect The obtained results are compared with the data in Table 1 above, and the comparison results of the time-consuming and accuracy of each detection method are shown in Table 2:

[0112] Table 2: Comparison of accuracy rates of different methods

[0113]

[0114] As can be seen from the results in Table 2, under the same conditions, the diagnostic method based on the multi-layer...

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Abstract

The invention discloses a rolling bearing fault detection method based on a multilayer residual network model. The rolling bearing fault detection method comprises the following steps: Step 1, collecting and extracting a vibration signal of a bearing by using a sensor; step 2, carrying out feature iteration on the vibration signal by utilizing a residual network; 3, the time domain signals subjected to iteration enter a neural network full-connection layer, weighted summation is conducted on the time domain signals through the network full-connection layer, the weight coefficient of a network structure is automatically optimized through the back propagation process, and a diagnosis model is obtained; step 4, inputting test data into the diagnosis model for diagnosis, and completing the detection of the bearing fault; according to the method, bearing signal faults are extracted in a cross mode based on the multi-layer residual error network model, so that the loss function value can be rapidly reduced to complete fault diagnosis, the feature extraction speed is high, data does not need to be preprocessed, the diagnosis precision is high, and the method has the advantages of being rapid in feature extraction, high in precision and good in robustness.

Description

technical field [0001] The invention relates to the technical field of fault detection, in particular to a rolling bearing fault detection method based on a multi-layer residual network model. Background technique [0002] Modern industrial production requires very tight production rhythms. Once mechanical equipment fails, it will disrupt the production process, causing huge economic losses and even casualties. Nowadays, mass production relies more on high-reliability production and processing equipment, and mechanical fault diagnosis has become A rapidly changing engineering technology; [0003] Rolling bearings are not only widely used in production, they are also vulnerable parts in mechanical equipment. According to statistics, about 30% of the faults in rotating machinery are caused by the failure of rolling bearings; after using fault diagnosis technology, the probability of mechanical equipment failure can be extremely high The maintenance cost is 25%-50% lower than ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G01M13/045
CPCG01M13/045G06N3/045G06F2218/08G06F2218/12
Inventor 王萌
Owner TANGSHAN IND VOCATIONAL TECHN COLLEGE
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