4.6 Article

A novel ResNet-based model structure and its applications in machine health monitoring

Journal

JOURNAL OF VIBRATION AND CONTROL
Volume 27, Issue 9-10, Pages 1036-1050

Publisher

SAGE PUBLICATIONS LTD
DOI: 10.1177/1077546320936506

Keywords

ResNet; convolution neural network; machine health monitoring; bearing; tool Wear

Funding

  1. Key-Area Research and Development Program of Guangdong Province [2020B090927002]
  2. National Natural Science Foundation of China [51675204]
  3. National Science and Technology Major Project of China [2018ZX04035002002]
  4. Natural Science Foundation of Hubei Province [2019CFB326]
  5. Scientific Research Foundation for Doctoral Program of Hubei University of Technology [BSQD2017003]

Ask authors/readers for more resources

Machine health monitoring is crucial in modern manufacturers for reducing downtime and cutting production costs. Deep learning methods extract features more efficiently, while the use of short-time Fourier transform as a data preprocessing method and an optimized model based on ResNet improve training and accuracy.
Machine health monitoring has become increasingly important in modern manufacturers because of its ability to reduce downtime of the machine and cut down the production cost. Enormous signals acquired from machinery are capable of reflecting current working conditions by in-depth analysis with various data-driven methods. Hand-crafted feature extraction and representation from the traditional methods are essential but daunting tasks, and these methods may not be suitable for these massive data. Compared with traditional methods, deep learning ones are able to extract the best feature combination during model training without any artificial intervention, which makes it easier, more efficient, and more effective to monitor machine health, but the training cost and training time hamper its application. The short-time Fourier transform is adopted as the data preprocessing method to cut down the training cost and boost the training procedure. Inspired by the great achievements of ResNet, the new optimized model based on ResNet has been proposed with layer-by-layer dimension reduction of the feature maps. The proposed model is also able to avoid information loss in the conventional pooling layer. All the potential candidate model blocks are introduced and compared, and the best one is selected as the final one. Repeated model block layers are adapted for the best feature combinations, followed by a two-layer full connection layer for the final targets. The proposed method is validated by conducting experiments on bearing fault diagnosis and tool wear prediction dataset. The final results show that the proposed model achieves the best accuracy rate in the classification task and the lowest root mean squared error in the prediction task.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available