Fiber manufacturing is a complex and dynamic process where quality control poses unique challenges, requiring timely detection and accurate classification of faults. Automating the quality control process using machine-learning techniques has the potential to reduce costs and increase efficiency. However, training machine-learning models to classify faults requires labeling large quantities of time-series data generated from the sensors, which can be costly and time-consuming. In this work, we developed and evaluated Active Learning (AL) techniques to enhance the performance of fault classification models while reducing the number of instances that require labeling. AL is based on focusing the labeling effort on a small number of selected, informative samples, thereby reducing the cost and time requirements for creating a labeled dataset. Moreover, in manufacturing, the frequencies of different types of faults vary widely, resulting in a class imbalance problem. The selection of instances per class to be labeled is beyond the control of traditional AL techniques, which may lead to significant bias in the classification performance and the selection of instances to be labeled for AL strategies, particularly in a dynamic manufacturing environment. To address this problem, we also develop and incorporate a class-balancing instance selection algorithm that tries to select more instances from the classes with fewer labeled examples. The AL  techniques implemented here reduce the amount of labeled data necessary for accurate fault classification by a factor of 5 compared to conventional supervised machine-learning techniques. Additionally, our class-balancing instance selection algorithm effectively addresses the class imbalance problem in our dataset. Overall, our results indicate that our AL pipeline constitutes a promising solution for efficient and accurate fault classification using time-series data from industrial manufacturing.