Training method of fault detection model, and fault detection method of electromechanical equipment

Abstract

The invention relates to a training method of a fault detection model, and a fault detection method of electromechanical equipment. The fault detection model training method comprises the steps of obtaining training signal data; performing feature extraction on the training signal data to obtain a first signal feature; reconstructing and decomposing the training signal data to obtain a second signal feature; arranging the first signal feature and the second signal feature according to a preset sequence, and inputting an arrangement result into an initial fault detection model to obtain a detection result; wherein the preset sequence represents the importance degree of the first signal feature and the second signal feature; calculating the loss between the detection result and a preset label, and training the initial fault detection model according to the loss; and when the loss reaches convergence, completing training of the initial fault detection model to obtain a fault detection model. According to the method, the precision of the fault detection model obtained through training can be improved, and then the accuracy of the obtained detection result is improved.

Description

Technical field [0001] This application relates to the field of computer technology, in particular to a method for training a fault detection model and a method for fault detection of electromechanical equipment. Background technique [0002] In the industrial field, electromechanical equipment can help people reduce work difficulty and improve work efficiency. It is essential to ensure the normal working state of electromechanical equipment. Therefore, effective fault detection of electromechanical equipment can improve the operational safety of electromechanical equipment. Reduce the probability of accidents. When electromechanical equipment is working, it usually generates a series of vibration signals. Analysis based on the vibration signals can determine the type of malfunction of the electromechanical equipment. [0003] Based on vibration signal analysis, the fault type can be divided into steps such as signal acquisition, feature extraction and fault identification. Tradit...

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Problems solved by technology

[0005] Based on this, it is necessary to provide a fault detection model training method and a fault detection method for electromechanical equipment in view of the low accuracy of traditional technology in fault identification.

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