Manufacturing is transformed towards smart manufacturing, entering a new data-driven era fueled by digital technologies. The resulting Smart Manufacturing Systems (SMS) gather 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, Time-Series Classification (TSC) emerges as a crucial task in this domain. Over the past decade, researchers have introduced numerous methods for TSC, necessitating not only algorithmic development and analysis but also validation and empirical comparison. This dual approach holds substantial value for practitioners by streamlining choices and revealing insights into models’ strengths and weaknesses. The objective of this study is to fill this gap by providing a rigorous experimental evaluation of the state-of-the-art Machine Learning (ML) and Deep Learning (DL) algorithms for TSC tasks in manufacturing and industrial settings. We first explored and compiled a comprehensive list of more than 92 state-of-the-art algorithms from both TSC and manufacturing literature. Following this, we methodologically 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 emerged as 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 collected from manufacturing systems for TSC tasks, 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 manufacturing time-series data using RNN-based structures.

中文翻译：

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智能制造系统中的时间序列分类：最先进机器学习算法的实验评估


制造业向智能制造转型，进入数字技术驱动的数据驱动新时代。由于传感器数量的不断增加和传感技术的快速进步，由此产生的智能制造系统（SMS）收集了大量不同的数据。在短信设置中可用的各种数据类型中，时间序列数据起着至关重要的作用。因此，时间序列分类（TSC）成为该领域的一项关键任务。在过去的十年中，研究人员引入了多种 TSC 方法，不仅需要算法开发和分析，还需要验证和实证比较。这种双重方法通过简化选择并揭示对模型优缺点的见解，对从业者具有巨大的价值。本研究的目的是通过对制造和工业环境中 TSC 任务的最先进机器学习 (ML) 和深度学习 (DL) 算法进行严格的实验评估来填补这一空白。我们首先从 TSC 和制造文献中探索并编制了超过 92 种最先进算法的综合列表。接下来，我们从方法论上从这个列表中选出了 36 个最具代表性的算法。为了评估它们在各种制造分类任务中的表现，我们整理了一组 22 个制造数据集，代表涵盖不同制造问题的不同特征。随后，我们在制造基准数据集上实施和评估了算法，并分析了每个数据集的结果。根据结果​​，ResNet、DrCIF、InceptionTime 和 ARSENAL 成为表现最好的算法，平均准确率超过 96。所有 22 个制造 TSC 数据集中的 6%。这些发现强调了卷积核在捕获从 TSC 任务的制造系统收集的时间序列数据中的时间特征方面的鲁棒性、效率、可扩展性和有效性，因为排名前四的算法中的三个利用这些内核进行特征提取。此外，LSTM、BiLSTM 和 TS-LSTM 算法因其在使用基于 RNN 的结构捕获制造时间序列数据中的特征方面的有效性而值得认可。