Bearing fault diagnosis method and system, storage medium and equipment
A fault diagnosis and bearing technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as the inability to make full use of the effective information of vibration signals, and achieve the effect of improving accuracy, improving prediction accuracy, and improving robustness.
Pending Publication Date: 2022-04-01
合肥综合性国家科学中心人工智能研究院
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[0004] A bearing fault diagnosis method, system, storage medium and equipment proposed by the present invention can solve the technical problem that existing methods cannot make full use of the effective information attached to the vibration signal
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[0063] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments.
[0064] Such as figure 1 with figure 2 As shown, the bearing fault diagnosis method described in this embodiment includes the following steps:
[0065] Step 1: Obtain the vibration signal data s of N sensors where the fault occurs 1 ,s 2 ,...s N , build the attention module ATT, and increase the weight of effective information.
[0066] Step 2: Preprocess the data passed through the attention module, convert the acquired vibration signal data in N dimensions into a vibration image according to certain rules, and calculate the pixel value of the...
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According to the bearing fault diagnosis method and system, the storage medium and the equipment, the following steps are executed through computer equipment: acquiring vibration signal data s1, s2,... sN of N sensors at a fault occurrence position, constructing an attention module ATT, and increasing the weight of effective information; preprocessing the data passing through the attention module, converting the acquired vibration signal data of N dimensions into a vibration image according to a set rule, and calculating a pixel value of the image; establishing a harmonic layer, performing feature fusion on the vibration signals of the N channels, and fully considering the signal features of the N channels; and constructing a feature extractor by using a convolutional layer, and outputting a prediction result. According to the method, the vibration signals of N dimensions are extracted and fused, and the weight of effective information is increased by using an attention mechanism, so that the deep network has good robustness.
Description
technical field [0001] The invention relates to the technical field of bearing fault diagnosis, in particular to a bearing fault diagnosis method, system, storage device and equipment. Background technique [0002] In recent years, as an important component in intelligent manufacturing, bearings have been widely used. Once the bearing is damaged during the operation of the machine, it will cause the failure of the transmission part of the rotating machine, which will cause vibration that affects the entire production line, resulting in economic losses and safety hazards. Therefore, bearing fault diagnosis plays an important role in the intelligent manufacturing industry, and a good fault diagnosis method can bring huge economic benefits and safety guarantees. As a method that combines feature extraction and classification, deep learning provides an end-to-end solution for bearing fault diagnosis. [0003] Considering that traditional fault diagnosis methods only sample vib...
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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
Inventor 康宇余乾子曹洋刘斌琨许镇义
Owner 合肥综合性国家科学中心人工智能研究院