Tool wear recognition and signal labeling with small cross-labeled samples in impeller machining

Data-driven deep learning method is the main way to study the condition monitoring of mechanical equipment, in which sufficient labeled signals to train the model parameters is a typical problem. The existing methods to obtain the labeled signals mainly focus on manual marking. For the non-batch imp...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology Vol. 123; no. 11-12; pp. 3845 - 3856
Main Authors: Ou, Jiayu, Li, Hongkun, Wang, Zhaodong, Yang, Chao, Peng, Defeng
Format: Journal Article
Language:English
Published: London Springer London 01.12.2022
Springer Nature B.V
Subjects:
Advanced manufacturing technologies
CAE) and Design
Classification
Computer-Aided Engineering (CAD
Condition monitoring
Conditional probability
Conditional random fields
Deep learning
Engineering
Fault diagnosis
Impellers
Industrial and Production Engineering
Labeling
Labels
Laboratories
Machine tools
Machining
Machining centres
Marking
Mechanical Engineering
Media Management
Neural networks
Original Article
Signal processing
Three dimensional imaging
Time series
Tool wear
Small cross-labeled samples
Deep conditional random field neural network
Signal labeling
Tool wear recognition
ISSN:0268-3768, 1433-3015
Online Access:Get full text
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Summary:Data-driven deep learning method is the main way to study the condition monitoring of mechanical equipment, in which sufficient labeled signals to train the model parameters is a typical problem. The existing methods to obtain the labeled signals mainly focus on manual marking. For the non-batch impeller processing with variable working conditions, manually marking signals is not the wisest move. To solve this problem, this manuscript puts forward a deep conditional random field neural network (CRFNN) method. This framework fully utilizes the sensitivity of the conditional probability model to adjacent data marker information, and small cross-labeled samples are used to predict the labels of unknown signals. At the same time, the variational autoencoder is used to convert the one-dimensional time series signal into a three-dimensional image, which solves the problem that the empty tool signals have a great impact on the tool wear condition monitoring in the process of impeller blade machining. Experimental results on a CNC machining center demonstrate the effectiveness and feasibility of the proposed method and outperform the existing works under industrial small labeled samples.
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ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-022-10514-7