Mechanical fault diagnosis method based on multi-sensor information fusion migration network

Abstract

The invention discloses a mechanical fault diagnosis method based on a multi-sensor information fusion migration network, and the method comprises the steps of firstly collecting the multi-sensor data, obtaining a plurality of source domain data sets and target domain data sets, and then constructing a multi-sensor information fusion migration network diagnosis model, wherein the model is providedwith a feature sharing layer and M convolutional neural networks; constructing a loss function of each convolutional neural network; training the multi-sensor information fusion migration network diagnosis model, and based on the target domain training data of the M source domain data sets and the target domain data sets, in each iteration, sequentially training the first network to the M-th network according to the sequence of the source domain sensors until the number of iterations or the classification precision is reached; and finally, inputting the target domain test data of the target domain data sets into the model, and obtaining a final classification diagnosis result through model and loss function processing and weighted average of M outputs. The method can effectively improve the mechanical fault diagnosis precision.

Description

technical field [0001] The invention relates to the technical field of mechanical fault diagnosis, in particular to a mechanical fault diagnosis method based on multi-sensor information fusion migration network. Background technique [0002] The structure of the mechanical system is complex and there are many parts. In order to monitor the status of the mechanical system, multiple sensors are usually installed at different positions of the key components of the system to obtain information of different sensitivity and complementarity. However, existing methods usually only use the information of a single sensor for fault diagnosis, and the reliability of diagnosis is affected by various factors such as sensor installation location, signal transmission path, and variable working conditions. How to effectively use and fuse multi-sensor information to improve the classification accuracy and diagnostic reliability of mechanical fault diagnosis models is a difficult problem in th...

AI Technical Summary

Problems solved by technology

However, the existing fusion methods based on feature layer and decision layer are usually based on shallow network structure, which cannot automatically learn deep feature representation. Facing complex mechanical system diagnosis problems, the effect of fusion is often not ideal, and there are certain limitations.

Images