Recent technical developments have facilitated the collection and storage of large amounts of time series data for many condition monitoring and maintenance processes. However, most of this data is unlabeled, and producing high-quality labeled data is expensive, time-consuming, and a lot of times inaccurate given the uncertainty surrounding the labeling process and annotators. Active Learning (AL) has emerged as an approach that enables cost and time reductions of the labeling process. Here, we present an active learning framework to be used in the classification of time series from industrial process data, which can be vibration waveforms or control process data. Previous work has focused on active learning for image classification problems. Alternatively, when active learning has focused on time series classification problems, it has not dealt with the cold start problem, which consists of a complete absence of labels at the beginning of the training process. The active learning framework proposed incorporates a pre-clustering step to create an initial labeled dataset. Furthermore, we incorporate two strategies for the generation of features to be used in the AL framework, which are time series imaging and automatic feature generation. We study the learning curves of the different feature extraction techniques and evaluate them in two case studies. The first case is based on vibration data from a ball bearing experiment with faults seeded in the bearings. The second case is based on a production dataset from an industrial control process. We discover that with only having to label up to 10% of the unlabeled instances, after they had been properly queried, it is possible to achieve accuracy over 90%. An active learning framework offers a real possibility to achieve high accuracy while reducing the amount of work that needs to be incorporated into the labeling process.
How to Cite
active learning, condition monitoring, cold-start, time-series classification, vibration
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.