Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data

By Liang Guo, Yaguo Lei, Saibo Xing, Tao Yan, Naipeng Li. IEEE transactions on industrial electronics (1982. Print), 2019

1,048 citations recorded by the source corpus.

The success of intelligent fault diagnosis of machines relies on the following two conditions: 1) labeled data with fault information are available; and 2) the training and testing data are drawn from the same probability distribution. However, for some machines, it is difficult to obtain massive labeled data. Moreover, even though labeled data can be obtained from some machines, the intelligent fault diagnosis method trained with such labeled data possibly fails in classifying unlabeled data acquired from the other machines due to data distribution discrepancy. These problems limit the successful applications of intelligent fault diagnosis of machines with unlabeled data. As a potential tool, transfer learning adapts a model trained in a source domain to its application in a target domain. Based on the transfer learning, we propose a new intelligent method named deep convolutional transfer learning network (DCTLN). A DCTLN consists of two modules: condition recognition and domain adaptation. The condition recognition module is constructed by a one-dimensional (1-D) convolutional neural network (CNN) to automatically learn features and recognize health conditions of machines. The domain adaptation module facilitates the 1-D CNN to learn domain-invariant features by maximizing domain recognition errors and minimizing the probability distribution distance. The effectiveness of the proposed method is verified using six transfer fault diagnosis experiments.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/f50e54684086a91bb481f76f7180c1ef3c4cb312

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Thien Huynh-The
2. Zhu Han
3. Quoc-Viet Pham
4. Ming Zhao
5. Jing Lin
6. Zidong Wang
7. Shen Zhang
8. Kaixuan Liang

In Control and Systems Engineering

1. Zhu Han
2. Yihao Xue
3. Ming Zhao
4. Jing Lin
5. Mengjie Huang
6. Rui Yang
7. Zidong Wang
8. Shen Zhang

Citations in the corpus

• Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, background): “Several references, such as [162], and [161], attempted to visualize the learnt CNN kernel to interpret its physical meanings.”
• Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, methodology): “[120] applied MMD to measure the distribution discrepancies of the source and target-bearing datasets.”
• Artificial Intelligence for the Metaverse: A Survey (2022, methodology): “[152] Data-driven learning model using transfer learning mechanism on CNN architecture.”
• A comprehensive review on convolutional neural network in machine fault diagnosis (2020, methodology): “[210] [211] [213] [214] [215] ([216] MMD operating conditions [212] machines [217] Correlation alignment operating conditions Adversarial transfer [219] Adversarial discriminator operating conditions [220] discriminator and MMD machines [221] [222] Adversarial Wasserstein operating conditions [223] ”
• Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, background): “Specifically, a domain adaptation module is proposed in [158] to facilitate a 1-D CNN to learn domain-invariant features by maximizing the domain recognition error and minimizing the probability distribution distance.”