Bearing fault detection method based on variable learning rate multilayer perceptron

Abstract

The invention discloses a bearing fault detection method based on a variable learning rate multi-layer perceptron. The 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 using a multilayer perceptron; 3, a back propagation process is used for automatically optimizing the weight coefficient of the multi-layer perceptron, the learning rate of automatic optimization is changed according to the number of iterations, time domain signals obtained after iteration enter a support vector machine classifier, 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, the bearing signal faults are extracted in a crossed mode through the multi-layer perceptron based on the variable learning rate, the loss function value can be rapidly reduced to complete fault diagnosis, the feature extraction speed is high, data do not need to be preprocessed, 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 bearing fault detection method based on a variable learning rate multilayer perceptron. Background technique [0002] The development of modern industry has greatly improved the automation of equipment. The upgrading of intelligent manufacturing industry needs to continuously strengthen the reliability and efficiency of mechanical equipment. High-efficiency health monitoring systems are widely used in rotating mechanical equipment typified by rolling bearings. Bearing condition monitoring is increasingly important in industry, as improved bearing reliability can significantly reduce machine breakdowns, which can lead to major production losses and safety incidents. [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...

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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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