Fault diagnosis method for rolling bearing under variable working conditions

Abstract

The invention relates to a fault diagnosis method for a rolling bearing under variable working conditions, and the method solves a problem that the universality of a deep learning model becomes poor because of the complex and variable working conditions of mechanical equipment through combining with a transfer learning algorithm on the basis of employing a convolutional neural network learning model. The method comprises the steps that firstly, data collected under different working conditions is cut to divide samples, the samples are preprocessed through FFT, then low-level features of the samples are extracted through improved ResNet-50, and then a multi-scale feature extractor analyzes the low-level features from different angles to obtain high-level features to serve as input of a classifier; in the training process, high-level features of a training sample and a test sample are extracted at the same time, the conditional distribution distance between the training sample and the test sample is calculated and serves as one part of back propagation of a target function to achieve intra-class self-adaption, the influence of domain drift is reduced, and a deep learning model can bebetter qualified for a fault diagnosis task under the variable working condition.

Description

technical field [0001] The invention belongs to the technical fields of mechanical fault diagnosis and computer artificial intelligence, and in particular relates to an intra-class self-adaptive bearing fault diagnosis method under variable working conditions based on convolutional neural network and transfer learning. Background technique [0002] In recent years, the development of industry has higher and higher requirements for long-term safe and reliable operation of mechanical equipment. In order to avoid major economic losses and personal injuries, the development and application of fault diagnosis technology has become an important means to improve the safety and stability of mechanical systems. Fault diagnosis technology monitors the operating status of equipment to determine the location of the fault and timely investigate potential safety hazards. Therefore, in order to prevent catastrophic accidents, it is particularly important to strengthen the condition monitor...

AI Technical Summary

Problems solved by technology

However, this assumption is difficult to establish in actual industrial applications. On the one hand, because rotating machinery is often in a working environment with complex speed and load, the historical data used for training models and the real-time data collected when actually monitoring the status of equipment obey different laws. distribution, making the versatility and generalization of models based on traditional deep methods worse

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