Time-Series Classification in Smart Manufacturing Systems: An
  Experimental Evaluation of State-of-the-Art Machine Learning Algorithms
Mojtaba A. Farahani M. R. McCormick Ramy Harik Thorsten Wuest
Manufacturing is gathering extensive amounts of diverse data, thanks to the
growing number of sensors and rapid advances in sensing technologies. Among the
various data types available in SMS settings, time-series data plays a pivotal
role. Hence, TSC emerges is crucial in this domain. The objective of this study
is to fill this gap by providing a rigorous experimental evaluation of the SoTA
ML and DL algorithms for TSC tasks in manufacturing and industrial settings. We
first explored and compiled a comprehensive list of more than 92 SoTA
algorithms from both TSC and manufacturing literature. Following, we selected
the 36 most representative algorithms from this list. To evaluate their
performance across various manufacturing classification tasks, we curated a set
of 22 manufacturing datasets, representative of different characteristics that
cover diverse manufacturing problems. Subsequently, we implemented and
evaluated the algorithms on the manufacturing benchmark datasets, and analyzed
the results for each dataset. Based on the results, ResNet, DrCIF,
InceptionTime, and ARSENAL are the top-performing algorithms, boasting an
average accuracy of over 96.6% across all 22 manufacturing TSC datasets. These
findings underscore the robustness, efficiency, scalability, and effectiveness
of convolutional kernels in capturing temporal features in time-series data, as
three out of the top four performing algorithms leverage these kernels for
feature extraction. Additionally, LSTM, BiLSTM, and TS-LSTM algorithms deserve
recognition for their effectiveness in capturing features within time-series
data using RNN-based structures.