Abstract Real-time diagnostics of modules in metal-cutting machines may be based on neural-network algorithms for simulation of the standard process, identification of defects, and the introduction of corrections in the cutting machine’s control system. The machining conditions in normal operation of the machine are recorded by means of a trained neural network with long short-term memory (LSTM network). In real-time operation, the difference between the standard neural-network model and the actual process characteristics is used to determine the type of defect and the module of the machine where it occurs on the basis of a second neural network, the classification unit.

中文翻译：

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通过循环 LSTM 神经网络实时诊断金属切削机床

摘要 金属切削机床中模块的实时诊断可能基于神经网络算法，用于模拟标准过程、识别缺陷以及在切削机床的控制系统中引入修正。机器正常运行时的加工条件是通过训练有素的具有长短期记忆的神经网络（LSTM 网络）来记录的。在实时操作中，标准神经网络模型与实际过程特性之间的差异被用来确定缺陷类型和机器的模块，基于第二个神经网络，即分类单元。