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Rolling bearing fault prediction method based on continuous deep belief network

A technology of deep belief networks and rolling bearings, which is applied in mechanical bearing testing, instruments, character and pattern recognition, etc. It can solve problems such as slow convergence speed, difficulty in effectively representing complex nonlinear functions, lack of theory in kernel function and parameter selection, etc.

Inactive Publication Date: 2016-09-28
NORTHWESTERN POLYTECHNICAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The neural network is easy to fall into local minimum, and its convergence speed is too slow; the support vector machine is more suitable for the learning of small samples but not for the learning of large samples, and the selection of its kernel function and parameters lacks corresponding theory
More importantly, these traditional models are essentially shallow machine learning models, which usually contain no more than one layer of nonlinear feature transformation. When given a limited number of samples and computing units, it is difficult for shallow structure models to effectively represent complex nonlinear function
However, the evolution law of bearing faults is a nonlinear and non-stationary time series with extremely complex changes. Therefore, it is difficult to make accurate predictions for such complex time series under the conditions of poor information and uncertainty by using traditional shallow prediction methods such as neural networks. valid forecast
Especially when dealing with noisy data, the shallow prediction model tends to record the noisy data and cause overfitting

Method used

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  • Rolling bearing fault prediction method based on continuous deep belief network
  • Rolling bearing fault prediction method based on continuous deep belief network
  • Rolling bearing fault prediction method based on continuous deep belief network

Examples

Experimental program
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Effect test

Embodiment 1

[0093] Produce a nonlinear, non-stationary time series x (t) by the chaotic Mackey-Glass differential delay equation, to verify the validity of the inventive method when analyzing and expressing nonlinear, non-stationary signals:

[0094] d x ( t ) d t = a x ( t - τ ) 1 + x 10 ( t - τ ) - b x ( t )

[0095] In this example, the initial conditions are set as a=0.25, b=0.1, x...

Embodiment 2

[0098] In this example, the rolling bearing life monitoring data in the prediction database of NASA is used to verify the accuracy of the method of the present invention in rolling bearing fault prediction. The experimental device is installed on a shaft with four bearings, driven by a DC motor, the speed is maintained at 2000rpm, and the radial load on the shaft is 6000 lbs. The bearing is a Rexnord ZA-115 double-row bearing, each bearing has 16 rolling elements, a diameter of 0.311 inches, a pitch diameter of 2.815 inches, and a contact angle of 15.17°. There are two PCB 353B33 high-sensitivity acceleration sensors placed vertically on each bearing to collect vibration acceleration signals. The vibration data is collected every 20 minutes by using the DAQCard-6062E acquisition card of NI Company. The sampling frequency of the data is 20kHz, and the data length of each sampling is 20480 points.

[0099] Figure 4 It is the whole life vibration signal of No. 3 rolling bearin...

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Abstract

The invention proposes a rolling bearing fault prediction method based on a continuous deep belief network. The method comprises the following steps: first, extracting the time-domain characteristic quantity of vibration signals of a rolling bearing; then, fusing the extracted time-domain characteristic information using a locally linear embedding method, so as to define a new comprehensive monitoring index for better quantitative evaluation of performance degradation of the bearing; training a continuous restricted Boltzmann machine step by step to construct a continuous deep belief network prediction model; and using a genetic algorithm to optimize the structure of the continuous deep belief network so as to further improve the prediction precision. The prediction method is reliable in result, has good real-time performance, is simple and feasible, and is suitable for rolling bearing fault prediction.

Description

technical field [0001] The invention belongs to the field of mechanical equipment health monitoring, in particular to a rolling bearing fault prediction method based on a continuous deep belief network. Background technique [0002] Rolling bearings are the most widely used mechanical parts in rotating machinery, and they are also one of the most vulnerable components. Rolling bearings may be damaged due to various reasons during operation, such as improper assembly, poor lubrication, moisture and foreign matter intrusion, corrosion and overload, etc. may cause premature damage to rolling bearings. Even if the installation, lubrication and maintenance are normal, after a period of operation, the rolling bearing will suffer from fatigue, peeling and wear and cannot work normally. Therefore, rolling bearing fault prediction is very important. It can not only ensure the safe operation of equipment, prevent major accidents, and improve economic benefits, but also provide a reli...

Claims

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Application Information

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IPC IPC(8): G01M13/04G06K9/00G06K9/62
CPCG01M13/04G06F2218/06G06F2218/08G06F2218/12G06F18/2133
Inventor 姜洪开邵海东张雪莉王福安
Owner NORTHWESTERN POLYTECHNICAL UNIV
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