US 2026/0092832 A1 · METHOD AND SYSTEM FOR BEARING FAULT TYPE DETERMINATION

Abstract

A method of determining a type of fault of a bearing in a machine includes obtaining vibration signal data that is representative of characteristics of a bearing in a machine, including a one-dimensional time domain signal. The method further includes converting the vibration signal data to a first image, which is a two-dimensional grayscale image representation of the vibration signal data. The method further includes executing a fault prediction model by inputting the first image to determine a type of fault in the bearing. The execution includes extracting, using a feature extraction model, multiple feature vectors from the first image that are representative of the characteristics of the bearing. The feature vectors are extracted using location-agnostic convolution operation and location-specific involution operation. The executing further includes determining, by the fault prediction model, the type of fault in the bearing based on the feature vectors.

Claims20

Dependent claims

A method of determining a type of fault of a bearing in a machine, the method comprising:

obtaining vibration signal data that is representative of characteristics of a bearing in a machine, wherein the vibration signal data includes a one-dimensional time domain signal;
converting the vibration signal data to a first image, wherein the first image is a two-dimensional grayscale image representation of the vibration signal data;
and
executing a fault prediction model by inputting the first image to determine a type of fault in the bearing, wherein the executing includes:
extracting, using a feature extraction model, multiple feature vectors from the first image that are representative of the characteristics of the bearing, wherein the feature vectors are extracted using location-agnostic convolution operation and location-specific involution operation, and
determining, by the fault prediction model, the type of fault in the bearing based on the feature vectors.

3 more elements

A non-transitory computer-readable storage medium for storing computer-readable instructions that, when executed by a computer, cause the computer to perform a method of training an image prediction model to generate training data for training a fault prediction model to predict a fault type of a bearing based on an image representative of characteristics of the bearing, the method comprising:

obtaining multiple sets of data, wherein each set of data includes (a) noise image, (b) a ground-truth fault image representative of one of multiple types of a fault of a bearing in a machine, and (c) a fault type associated with the ground-truth fault image, wherein the training includes:
training an image prediction model using the sets of data to generate a specified fault image for a specified fault type, wherein the training includes:
generating, using a generator of the image prediction model, a predicted fault image based on the noise image and the fault type,
determining, using a discriminator of the image prediction model, whether a difference between the predicted fault image and the ground-truth fault image is less than a specified threshold, and
iteratively training the image prediction model until the difference is reduced.

2 more elements

Dependencies are inferred from the claim text.

Method and system for bearing fault type determination

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Method and system for bearing fault type determination

Life of this patent

Abstract

Claims20