Rotary machinery fault diagnosis method under complex working condition based on meta transfer learning

Abstract

The invention discloses a rotary machinery fault diagnosis method under a complex working condition based on meta transfer learning, and the method comprises the steps of collecting original sensor signals of mechanical equipment in different states, and making an image data set; dividing the data set into a training set and a verification set; selecting a deep convolutional network as a pre-training model, and finishing training learning on ImageNet; using a meta-learning method to improve a parameter migration parameter initialization problem existing in migration learning, and obtaining parameter initialization optimization methods for multi-source domain and semi-supervised domain adaptive problems respectively; initializing a Meta-TCNN fault diagnosis model by using VGG-16 network parameters and adopting a meta learning optimization method; updating the Meta-TCNN parameters by adopting a fine tuning strategy; using the training set to train the Meta-TCNN model; stopping training until the final classification accuracy is not obviously improved any more; and verifying the trained Meta-TCNN model by using the verification set, and applying the model of which the parameters are completely optimized to a fault diagnosis task. The application range of the fault diagnosis method is expanded, and the cost is reduced.

Description

technical field [0001] The invention relates to the technical field of energy manufacturing, in particular to a fault diagnosis method for rotating machinery under complex working conditions based on meta-transfer learning. Background technique [0002] Driven by the integration and innovation of intelligent manufacturing, industrial big data, and Industry 4.0, the modern industry is experiencing a new revolution from traditional manufacturing to intelligent manufacturing. As one of the most important roles in this revolution, mechanical equipment is crucial to make accurate judgments and timely responses to its degradation and failure. In recent years, deep learning has also set off a wave of intelligent fault diagnosis. Currently popular deep learning-based diagnostic models include deep autoencoders, deep belief networks, recurrent neural networks, and convolutional neural networks (CNN). [0003] But the current success of deep learning relies on a large amount of faul...

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Problems solved by technology

Therefore, the performance of most of the above models will severely degrade when the data distribution between the training set (source domain) and the test set (target domain) is different

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