A Multi-Index Generative Adversarial Network for Tool Wear Detection with Imbalanced Data

• Published in: Complexity (New York, N.Y.) Vol. 2020; no. 2020; pp. 1 - 10
• Main Authors: Liu, Yimo, Liu, Qinyuan, Jiang, Jingwen, Xiao, Haoping, Zhang, Guokai, Wang, Liying
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Accuracy; Correlation coefficients; Cutting tools; Decision making; Discriminant analysis; Feature extraction; Generative adversarial networks; Liquors; Machine learning; Manufacturing; Methods; Neural networks; Performance indices; Support vector machines; Tool wear; China
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1155/2020/5831632

The scarcity of abnormal data leads to imbalanced data in the field of monitoring tool wear conditions. In this paper, a novel multi-index generative adversarial network (MI-GAN) is proposed to detect the tool wear conditions subject to imbalanced signal data. First, the generator in the MI-GAN is trained to produce fake normal signals, and the discriminator computes scores of testing signals and generated signals. Next, the generator detects abnormal signals based on the performance of imitating testing signals, and the discriminator will compute the scores of testing signals and generated signals. Subsequently, two indexes, i.e., L2-norm and temporal correlation coefficient (CORT), are put forward to measure the similarity between generated signals and testing signals. Finally, our decision-making function further combines L2-norm and CORT with two discriminator scores to determine the tool conditions. Experimental results show that our method obtains 97% accuracy in tool wear detection based on imbalanced data without manual feature extraction, which outperforms traditional machine learning methods.