Bearing vibration signal processing model construction method, model and signal classification method

The bearing vibration signal processing model is constructed through a self-supervised learning method, and the signal similarity is calculated using DTW and Euclidean distances, and feature fusion is combined with time-domain and frequency-domain features, which solves the problem of feature extraction and difficulty in obtaining labeled data in the existing technology, and achieves better generalization and fault recognition capabilities.

CN119669825AActive Publication Date: 2025-03-21SUZHOU VOCATIONAL INSTITUTE OF INDUSTRIAL TECHNOLOGY
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Patent Information

Application Number
CN202411731574.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-28
Publication Date
2025-03-21
Estimated Expiration
2044-11-28

AI Technical Summary

Technical Problem

The prior art relies on manual extraction of features in bearing vibration signal processing, making it difficult to fully capture complex signal characteristics. The supervised learning method requires a large amount of labeled data, which makes it difficult to obtain labeled data, resulting in insufficient generalization capabilities of the model.

Method used

The bearing vibration signal processing model is constructed using a self-supervised learning method, and the signal similarity is calculated through DTW and Euclidean distances. The loss function guides the encoder to extract signal characteristics, combine time domain and frequency domain characteristics for feature fusion, and use a linear classifier for classification.

Benefits of technology

It improves the generalization ability of the model, can better handle complex nonlinear signals, reduces dependence on labeled data, reduces manual labeling costs, and improves the ability to identify signals of unknown fault types.

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Abstract

The invention discloses a bearing vibration signal processing model construction method, a model and a signal classification method, which are used for constructing time domain and frequency domain loss functions by calculating a plurality of feature vector distances so as to train an encoder. According to the method, by minimizing the difference value of different distances in the time domain and frequency domain loss functions, an encoder can be guided to extract similar feature representations in signals, so that the similar feature representations are gathered in a feature space, and dissimilar features are separated. When a trained encoder (a convolutional neural network with an attention mechanism) processes a vibration signal of an unknown fault type, some feature representations in the signal are different from those of other signals, so that the feature representations are gathered in a feature space and are far away from the feature representations of other signals, and the fault detection accuracy is improved. Therefore, the recognition of the vibration signal of the unknown fault type is effectively realized.
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