Rolling bearing fault detection method based on multilayer residual network model

Abstract

The invention discloses a rolling bearing fault detection method based on a multilayer residual network model. The rolling bearing fault detection method comprises the following steps: Step 1, collecting and extracting a vibration signal of a bearing by using a sensor; step 2, carrying out feature iteration on the vibration signal by utilizing a residual network; 3, the time domain signals subjected to iteration enter a neural network full-connection layer, weighted summation is conducted on the time domain signals through the network full-connection layer, the weight coefficient of a network structure is automatically optimized through the back propagation process, and a diagnosis model is obtained; step 4, inputting test data into the diagnosis model for diagnosis, and completing the detection of the bearing fault; according to the method, bearing signal faults are extracted in a cross mode based on the multi-layer residual error network model, so that the loss function value can be rapidly reduced to complete fault diagnosis, the feature extraction speed is high, data does not need to be preprocessed, the diagnosis precision is high, and the method has the advantages of being rapid in feature extraction, high in precision and good in robustness.

Description

technical field [0001] The invention relates to the technical field of fault detection, in particular to a rolling bearing fault detection method based on a multi-layer residual network model. Background technique [0002] Modern industrial production requires very tight production rhythms. Once mechanical equipment fails, it will disrupt the production process, causing huge economic losses and even casualties. Nowadays, mass production relies more on high-reliability production and processing equipment, and mechanical fault diagnosis has become A rapidly changing engineering technology; [0003] Rolling bearings are not only widely used in production, they are also vulnerable parts in mechanical equipment. According to statistics, about 30% of the faults in rotating machinery are caused by the failure of rolling bearings; after using fault diagnosis technology, the probability of mechanical equipment failure can be extremely high The maintenance cost is 25%-50% lower than ...

AI Technical Summary

Problems solved by technology

[0003] Rolling bearings are not only widely used in production, they are also vulnerable parts in mechanical equipment. According to statistics, about 30% of the faults in rotating machinery are caused by the failure of rolling bearings; after using fault diagnosis technology, the probability of mechanical equipment failure can be extremely high The maintenance cost is 25%-50% lower than before; due to the long life of the rolling bearing, according to the national standard, there is a 90% reliability threshold as a standard part, even if the same material uses the same process on the same machine The same batch of bearings is produced, and its service life is also different; usually in reality, regular inspection and repairs are carried out based on the designed fatigue life, which has a great chance of damaging the equipment and causing economic losses;

[0004] At present, in the practice of enterprises, if it is a rolling bearing in an unimportant occasion, it mainly depends on the experience of engineers and technicians to judge whether there is a fault in the operation process of the bearing through touch and hearing; in more important occasions, it is necessary to regularly disassemble and check the operation of the bearing , but it is impossible to predict the fault state in a timely and effective manner; at the same time, the current detection algorithms for bearing faults mainly focus on the traditional use of time-domain signals as signal input features, and then use machine learning or deep learning methods for classification, with high accuracy However, it has not yet reached the conditions that meet the industrial testing applications of core components;

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