Real-time fault detection, classification and diagnosis in manufacturing and process industries is essential to prevent unplanned downtime and improve the reliability of industrial operations. While several well-accepted machine learning techniques exist for fault detection and classification, there is a need for a reliable and generalized fault diagnosis technique that identifies sensors responsible for industrial faults in real time. In this work, we propose a variable perturbation matrix-based method for fault diagnosis in industrial processes. We utilize the Long ShortTerm Memory for prediction due to its ability to memorize temporal information in time-series data. First, the fault is detected, then one or more independent variables are perturbed across the fault detection point to check the sensitivity of the diagnosis model for the corresponding variables. Thus, a perturbation matrix is calculated and variables with high sensitivity are selected as the variables responsible for fault. The proposed method is applied to an Industry 4.0 quality control test bed set up for electronic components, the dataset for which is provided in the Prognostics and Health Management Euorpe-21 (PHME-21) data challenge. The proposed method accurately detected and classified 6 faults in the test bed and correctly diagnosed the most significant variables. Due to high fault detection accuracy coupled with sensitivity-based fault diagnosis, the method is suitable for multivariate industrial systems.
How to Cite
Fault diagnosis, variable perturbation, Multivariate time series, Industrial processes
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.