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Antifriction bearing fault diagnosis method based on depth belief network and support vector machine

A technology of deep belief network and support vector machine, which is applied in the direction of mechanical bearing testing, mechanical component testing, machine/structural component testing, etc., can solve the problems of huge raw data, long computing time, unfavorable practical application, etc., and achieve enhanced The effect of improving fault characteristics and classification accuracy

Inactive Publication Date: 2018-04-17
TONGREN POLYTECHNIC COLLEGE
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Problems solved by technology

However, the input of traditional deep belief network is raw data. Due to the huge raw data, the calculation time is too long, and it does not use the practical application in engineering.

Method used

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  • Antifriction bearing fault diagnosis method based on depth belief network and support vector machine
  • Antifriction bearing fault diagnosis method based on depth belief network and support vector machine
  • Antifriction bearing fault diagnosis method based on depth belief network and support vector machine

Examples

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Embodiment example 1

[0068] Taking the bearing data of Western Reserve University in the United States as an example, the implementation method of rolling bearing fault diagnosis based on energy operator demodulation deep belief network and support vector machine is illustrated.

[0069] (1) Test data

[0070] The rolling bearing experimental platform includes a 2-horsepower motor (left side) (1h=746w), a torque sensor (middle), a dynamometer (right side) and electronic control equipment. The test bench includes the drive end bearing and the fan end bearing, and the acceleration sensor is installed at the 12 o'clock position of the drive end and the fan end of the motor housing respectively. The vibration signal is collected by a 16-channel DAT recorder, and the sampling frequency of the drive end bearing fault data is 48,000 points per second. In this test, we choose the driving end (bearing) as the research object, the bearing model of the driving end is SKF6205, the bearing speed is 1797r / min ...

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Abstract

An antifriction bearing fault diagnosis method based on an energy operator demodulated depth belief network (DBN) and a particle swarm optimized support vector machine (PSO-SVM); the method comprisesthe following steps: using an energy operator demodulation method to obtain an instantaneous Teager oscillogram and solving a time frequency characteristic statistics parameter thereof; using the DBNto extract secondary characteristics of the time frequency characteristic statistics; finally inputting the extracted characteristic parameter into the PSO-SVM for fault classification. The antifriction bearing fault diagnosis method is higher in accuracy, can greatly shorten the algorithm training time, thus improving the fault diagnosis accuracy and efficiency.

Description

technical field [0001] The invention relates to the field of rolling bearing parameter diagnosis, and is a rolling bearing fault diagnosis method based on a deep belief network demodulated by an energy operator and a support vector machine. Background technique [0002] Bearing is the most widely used part in rotating machinery, and it is also an extremely vulnerable part. Its operating status will directly affect the performance of the entire machine. If the bearing fails, it will cause huge economic losses. Therefore, the failure of the bearing Diagnosis has important practical significance. [0003] With the development of artificial intelligence technology, the fault diagnosis system is moving towards the direction of intelligence. The traditional mechanical fault diagnosis method can no longer meet the actual requirements, so that it can be replaced by machine learning, which is becoming more and more popular. At present, the commonly used machine learning methods inc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/04
CPCG01M13/045
Inventor 熊景鸣潘琳朱昇张志昌黄陈林
Owner TONGREN POLYTECHNIC COLLEGE
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