High-resolution time-series classification in smart manufacturing systems

Abstract

High-resolution time-series data is increasingly prevalent in today’s smart manufacturing systems driven by the proliferation of sensors and data acquisition technologies. However, classification tasks on such data face challenges like collinearity, model instability, poor generalization, and overfitting. This paper investigates techniques capable of handling high-resolution time-series classification (TSC) tasks in the manufacturing domain. High-resolution time-series data presents challenges impacting the concepts essential for classification algorithms. Additionally, increased noise and the extensive feature space further complicate classification performance, emphasizing the need for innovative solutions to process this data efficiently on resource-constrained devices. In this paper, we address this research gap by defining the problem in depth, investigating possible solutions, and evaluating a subset of solutions in a manufacturing case study. We utilize a CNC machining dataset with over 100,000 data points per time-series. We examine algorithms representing two main approaches: feature engineering for data transformation and dimensionality reduction, and advanced machine learning and deep learning models to capture long-term dependencies. Specifically, we implement Discrete Fourier Transform, Symbolic Aggregate Approximation, Symbolic Fourier Approximation, ARSENAL, DrCIF, and ResNet algorithms for TSC. Experimental results highlight the trade-off between accuracy and computational expense. While ARSENAL and DrCIF achieve high accuracy, their runtimes are notably high. Feature engineering methods offer competitive accuracy with very low runtimes, and ResNet strikes a compelling balance by surpassing benchmarks in accuracy while maintaining reasonable computational costs. These findings can aid in adopting appropriate tools to successfully harness manufacturing big data to derive actionable insights for process improvements and optimization. The novelty of this research lies in addressing the specific challenges of classification tasks on high-resolution time-series data within the manufacturing domain, exploring diverse techniques, applying them to a case study, and providing a nuanced analysis of algorithmic performance with practical implications.