Driving motor fault diagnosis model construction method based on intra-class feature transfer learning and multi-source information fusion

Abstract

The invention provides a driving motor fault diagnosis model construction method based on intra-class feature transfer learning and multi-source information fusion, and the method comprises the steps:firstly proposing an improved hierarchical transfer learning method MSTL, which considers the neighbor relation between intra-class samples, maintains the local manifold structure of intra-class data, also can improve the separability of the domain data subjected to transfer learning to different categories, so that the adaptability of the fault diagnosis model to different distribution domain samples is improved; meanwhile, the feature set dimension can be reduced, and the fault diagnosis performance of the fault diagnosis model under variable working conditions is improved. Besides, aimingat the problem that a certain uncertain factor exists in a signal acquired by a single sensor, the D-S evidence theory is adopted to carry out driving motor multi-source information decision-making layer fusion, and secondary D-S evidence fusion is carried out on diagnosis results of vibration and current signals on a model. According to the feature transfer learning method MSTL and the multi-source information fusion diagnosis model provided by the invention, the fault diagnosis accuracy can be improved, and the method has a certain practical value.

Description

technical field [0001] The invention relates to the field of fault diagnosis, in particular to a method for constructing a drive motor fault diagnosis model based on intra-class feature transfer learning and multi-source information fusion. Background technique [0002] The driving motor is an important part of the rotating machinery system. The condition monitoring of the driving motor is of great significance to the safe operation of the equipment. The equipment condition monitoring and fault diagnosis can be divided into three steps: signal acquisition, feature extraction, and state identification. Analyzing equipment vibration signals is the most commonly used means of fault diagnosis. How to extract the characteristic information representing the fault state of the drive motor from the nonlinear and non-stationary vibration signals is the key to realize the bearing fault diagnosis. Time-frequency analysis method is a powerful tool for nonlinear and non-stationary signal...

AI Technical Summary

Problems solved by technology

There are many interference signals in the operating environment of rotating mechanical equipment, and the working conditions of the driving motor are variable, which will cause the distribution difference between the test data and the training data. under the assumption of the same distribution between the set and the test data set

Therefore, it is difficult for traditional fault diagnosis models to ensure ideal fault diagnosis performance under variable conditions.

Due to the many interference signals and complex and changeable working conditions in the operating environment of rotating machinery equipment, there will be certain uncertainties in the signal acquisition by a single sensor, such as one-sidedness caused by measurement errors

In addition, the sensitivity of the sensors to the equipment is also different, so the collected data itself will also have contradictions.

Therefore, when using sensors to detect complex underground working environments, it is one-sided to only use and rely on a single type of sensor to monitor the state

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