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Prognosis of bearing damage performance to industrial system using nonlinear autoregressive with exogenous (NARX)

机译:非线性自回归与外源(NARX)对工业系统轴承损坏性能的预测

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Monitoring the engine's performance carried out with the aim of the business process can run smoothly without any process stalled because if the business process stops only a few seconds, the industry will experience a loss. In this research, monitoring of industrial systems using an intelligent system that will do the prognosis of damage to the components contained in the industrial machine is the bearing performance. The method used is the nonlinear autoregressive with exogenous (NARX), based on the linear ARX model, which is commonly used in time-series modeling. This model uses a recurrent dynamic network, with feedback connections and enclosing several layers of tissues. The results of this research remaining useful life (RUL) in machine tools industry. It means the fatal damage in a machine can be avoidable. It is also part of maintenance on industrial machinery, in order to become more efficient in costing and replacement of equipment to be effective.
机译:以业务流程为目标对引擎性能进行监控可以顺利运行,而不会导致任何流程停顿,因为如果业务流程仅停止几秒钟,行业将蒙受损失。在这项研究中,使用智能系统对工业系统进行监控,该系统将对工业机械中包含的组件进行损坏的预后预测是轴承的性能。使用的方法是基于线性ARX模型的外生非线性自回归(NARX)模型,该模型通常在时间序列建模中使用。该模型使用循环动态网络,该网络具有反馈连接并封装了几层组织。这项研究的结果在机床行业中仍然具有使用寿命(RUL)。这意味着可以避免机器上的致命伤害。它也是工业机械维护的一部分,以便在成本核算和设备更换方面更加有效。

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