Active Learning Framework for Time-Series Classification of Vibration and Industrial Process Data

Abstract

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.