In various electronic circuits with privacy and security constraints, only a single status monitoring signal is obtained, which renders domain knowledge inapplicable. To address this challenge, the data-driven univariate time series classification (TSC) method is a suitable and effective solution for diagnosing the system’s fault type. The current feature-based TSC method is interpretable by extracting a descriptive statistics-based feature collection, but its TSC performance is limited due to poor extensibility and feature redundancy. Therefore, in this paper, a novel and extensible feature-based TSC algorithm with ensemble structure and enhancement framework Multiview Enhanced Characteristics (Mecha) is proposed, which consists of three components. In the diverse feature extractor, the global and local patterns are enhanced via shuffling mapping with dilation and interleaving mechanisms, improving the feature diversity and expressiveness. In the ensemble feature selector, diverse and stable multiview features are adaptively generated by multiple filters and intersections based on the feature stability and diversity scores. In the heterogeneous ensemble classifier, the ridge regression with cross-validation and extremely randomized trees classifiers are integrated via hard voting to enhance classifier diversity. Finally, the state-of-the-art feature-based TSC performance and application effectiveness of the proposed Mecha without domain knowledge is verified on public UCR datasets and a practical turntable current dataset.
[1] Changchun He, Xin Huo, Baohan Mi, and Songlin Chen. "Mecha: Multiview Enhanced Characteristics via Series Shuffling for Time Series Classification and Its Application to Turntable Circuit", IEEE Transactions on Circuits and Systems I: Regular Papers, 2025.
https://ieeexplore.ieee.org/document/11068941
Critical difference diagram comparing the accuracy of Mecha with existing representative feature-based TSC algorithms on 112 equal-length UCR datasets.
