Energy prediction of machine tools plays an irreplaceable role in energy planning, management, and conservation in the manufacturing industry. In the era of big machinery data, data-driven energy prediction models of machine tools have achieved remarkable results in the identification of energy consumption patterns and prediction of energy consumption conditions. However, existing data-driven studies towards the energy consumption of machine tools focus on the utilization of handcrafted-feature learning methods, which are inefficient and exhibit poor generalization. Moreover, considering variations in energy consumption characteristics among different machine tools, it is impractical to identify energy consumption features manually for energy model development. Therefore, this paper proposes a novel data-driven energy prediction approach using deep learning algorithms. Here, deep learning is used in an unsupervised manner to extract sensitive energy consumption features from raw machinery data, and in a supervised manner to develop the prediction model between the extracted features and the energy consumption of machine tools. The experiments conducted on a milling machine and a grinding machine are exploited and compared with those conducted in conventional studies. The results show that the proposed method can improve the energy prediction performance from 19.14% to 74.13% for the grinding machine and from 64.89% to 85.61% for the milling machine, and it achieves a better performance than the conventional methods in terms of effectiveness and generalization.

机床的能源预测在制造业的能源计划，管理和节约中起着不可替代的作用。在大型机械数据时代，数据驱动的机床能量预测模型在识别能耗模式和预测能耗状况方面取得了显著成果。但是，现有的有关机床能耗的数据驱动研究集中于利用手工功能学习方法，这些方法效率低下，并且泛化能力很差。此外，考虑到不同机床之间的能耗特征的变化，为能源模型开发手动识别能耗特征是不切实际的。因此，本文提出了一种使用深度学习算法的新型数据驱动能量预测方法。在这里，深度学习以无监督的方式从原始机械数据中提取敏感的能耗特征，并以监督的方式在提取的特征和机床能耗之间建立预测模型。利用在铣床和磨床上进行的实验，并将其与常规研究中的进行比较。结果表明，所提出的方法可以将磨床的能量预测性能从19.14％提高到74.13％，将铣床的能量预测性能从64.89％提高到85.61％，并且在有效性和效率上均优于常规方法。概括。深度学习以无监督的方式从原始机械数据中提取敏感的能耗特征，并以监督的方式开发提取的特征与机床能耗之间的预测模型。利用在铣床和磨床上进行的实验，并将其与常规研究中的进行比较。结果表明，所提出的方法可以将磨床的能量预测性能从19.14％提高到74.13％，将铣床的能量预测性能从64.89％提高到85.61％，并且在有效性和效率上均优于常规方法。概括。深度学习以无监督的方式从原始机械数据中提取敏感的能耗特征，并以监督的方式开发提取的特征与机床能耗之间的预测模型。利用在铣床和磨床上进行的实验，并将其与常规研究中的进行比较。结果表明，所提出的方法可以将磨床的能量预测性能从19.14％提高到74.13％，将铣床的能量预测性能从64.89％提高到85.61％，并且在有效性和效率上均优于常规方法。概括。并以监督的方式在提取的特征和机床能耗之间建立预测模型。利用在铣床和磨床上进行的实验，并将其与常规研究中的进行比较。结果表明，所提出的方法可以将磨床的能量预测性能从19.14％提高到74.13％，将铣床的能量预测性能从64.89％提高到85.61％，并且在有效性和效率上均优于常规方法。概括。并以监督的方式在提取的特征和机床能耗之间建立预测模型。利用在铣床和磨床上进行的实验，并将其与常规研究中的进行比较。结果表明，所提出的方法可以将磨床的能量预测性能从19.14％提高到74.13％，将铣床的能量预测性能从64.89％提高到85.61％，并且在有效性和效率上均优于常规方法。概括。