Holistic fault detection and diagnosis system in imbalanced, scarce, multi-domain (ISMD) data setting for component-level prognostics and health management (PHM).

Rohan, Ali

Abstract

In the current Industry 4.0 revolution, prognostics and health management (PHM) is an emerging field of research. The difficulty of obtaining data from electromechanical systems in an industrial setting increases proportionally with the scale and accessibility of the automated industry, resulting in a less interpolated PHM system. To put it another way, the development of an accurate PHM system for each industrial system necessitates a unique dataset acquired under specified conditions. In most circumstances, obtaining this one-of-a-kind dataset is difficult, and the resulting dataset has a significant imbalance, a lack of certain useful information and contains multi-domain knowledge. To address those issues, this paper provides a fault detection and diagnosis system that evaluates and preprocesses imbalanced, scarce, multi-domain (ISMD) data acquired from an industrial robot, utilizing signal processing (SP) techniques and deep learning-based (DL) domain knowledge transfer. The domain knowledge transfer is used to produce a synthetic dataset with a high interpolation rate that contains all the useful information about each domain. For domain knowledge transfer and data generation, continuous wavelet transform (CWT) with a generative adversarial network (GAN) was used, as well as a convolutional neural network (CNN), to test the suggested methodology using transfer learning and categorize several faults. The proposed methodology was tested on a real experimental bench that included an industrial robot created by Hyundai Robotics. This test had a satisfactory outcome with a 99.7% (highest) classification accuracy achieved by transfer learning on several CNN benchmark models.

Citation

ROHAN, A. 2022 Holistic fault detection and diagnosis system in imbalanced, scarce, multi-domain (ISMD) data setting for component-level prognostics and health management (PHM). Mathematics [online], 10(12), article number 2031. Available from: https://doi.org/10.3390/math10122031

• Journal Article Type: Article
• Acceptance Date: Jun 8, 2022
• Online Publication Date: Jun 11, 2022
• Publication Date: Jun 30, 2022
• Deposit Date: Jul 6, 2023
• Publicly Available Date: Jul 6, 2023
• Journal: Mathematics
• Electronic ISSN: 2227-7390
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 12
• Article Number: 2031
• DOI: https://doi.org/10.3390/math10122031
• Keywords: Domain knowledge transfer; Big data; Generative adversarial networks (GAN); Convolutional neural networks (CNN); Prognostics and health management (PHM); Artificial intelligence
• Public URL: https://rgu-repository.worktribe.com/output/1982263