Rolling bearing fault diagnosis method based on parallel feature learning and multiple classifiers

Abstract

The invention provides a rolling bearing intelligent fault diagnosis method based on multi-classifier integration and parallel feature learning, and aims to improve the classification precision of a model, and the method comprises the following implementation steps: obtaining a training sample set and a test sample set; establishing a plurality of stacked auto-encoder models, carrying out paralleltraining on the stacked auto-encoder models by taking the training sample set as an input, and extracting a plurality of characteristics of the training sample set; performing feature evaluation on the extracted features based on a softmax model, and screening the features according to a corresponding threshold value and an evaluation index value to form a feature subset; establishing a pluralityof classifiers based on a softmax model according to the feature subset, obtaining the classification precision of each classifier by taking the feature subset as input, reselecting a plurality of classifiers according to a threshold value to construct an integrated multi-classifier model, obtaining an integrated multi-classifier model prediction label through a majority voting method, and mapping the prediction label and the fault type of the rolling bearing to realize the intelligent fault diagnosis of the rolling bearing.

Description

Technical field [0001] The invention belongs to the technical field of intelligent fault diagnosis of rotating machinery, and relates to a rolling bearing fault diagnosis method, in particular to a rolling bearing fault intelligent diagnosis method based on parallel feature learning and integrated multi-classifier, which can be used for automatic fault diagnosis of rotating machinery such as rolling bearings. Background technique [0002] Rotating machinery plays an important role in industrial equipment. Rolling bearings are one of the most important parts in rotating machinery such as motors, wind turbines and gearboxes. It consists of rolling elements, outer rings, inner rings and cages. Rolling bearings usually work under complex working conditions, such as different working conditions, vibration, temperature, load, etc. These factors often lead to the degradation of rolling bearing performance or even failure. The performance status of the rolling bearing directly affects t...

AI Technical Summary

Problems solved by technology

However, although this method can extract deep features by integrating the deep autoencoder model in the feature extraction and selection stage, it does not effectively evaluate the extracted deep features, and cannot screen out the most relevant and more representative features that are highly relevant to the diagnostic target. The deep features of the model affect the classification and diagnosis accuracy of the model; in addition, this method only uses a softmax classifier for diagnosis in the fault classification and diagnosis stage, which has poor robustness and leads to low diagnostic accuracy of the model under random interference

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