Intelligent machine condition monitoring (CM) for automatic fault diagnosis relies on data-driven algorithms to characterize machine health for predictive maintenance activities on smart factory floors. Since data collection can be expensive, CM data sets may not cover all the possible fault conditions, necessitating that CM algorithms continually learn new conditions. State-of-the-art CM research has focused on detecting unknown conditions rather than integrating unknown conditions into future predictions. Therefore, CM-ready Continual Learning (CL) solutions should learn to classify new conditions and use improved representations that minimize the need for future fine-tuning. Meta-learning approaches like Few-Shot Prototypical Networks (FSPN) regularize base-task learning to find these more generalizable representations. Experiments on a motor data set demonstrate that FSPN with only 5 or 10 examples of the novel fault consistently outperforms static, fine-tuning, and Elastic Weight Consolidation (EWC) approaches for CL, increasing the overall accuracy by up to 19 points (53% to 72%). Compared to recent FSPN work for image classification, these results show that FSPN may be advantageous for CM due to the limited class diversity of CM data sets. Future work should extend the FSPN architecture to include open set recognition and quantitatively analyze varying numbers of base-task classes.
|Title of host publication||2022 IEEE 18th International Conference on Automation Science and Engineering, CASE 2022|
|Number of pages||6|
|State||Published - 2022|
|Event||18th IEEE International Conference on Automation Science and Engineering, CASE 2022 - Mexico City, Mexico|
Duration: Aug 20 2022 → Aug 24 2022
|Name||IEEE International Conference on Automation Science and Engineering|
|Conference||18th IEEE International Conference on Automation Science and Engineering, CASE 2022|
|Period||8/20/22 → 8/24/22|
Bibliographical noteFunding Information:
This work is supported by the National Science Foundation under Grant No. 2015889 *Corresponding author: Matthew Russell (phone: 1-859-317-3373; e-mail: firstname.lastname@example.org) Matthew Russell is with the Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506 USA (e-mail: email@example.com).
© 2022 IEEE.
- Continual Learning
- Deep Learning Methods
- Failure Detection and Recovery
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering