With the development of industrial Internet, smart manufacturing information systems (SMISs) are faced with more uncertainties, dynamics, and complexity. These problems bring more challenges to the reliability operation of SMISs. To solve the above problem, a prediction model based on phase space reconstruction, chaos analysis, and back propagation (BP) neural network is proposed to predict SMISs reliability. First, we decompose failure data series into some subdata series components with strong regularity by using C‐C algorithm and Cao algorithm. On this basis, we use the maximum Lyapunov index to identify chaotic characteristics of failure data series. And then, we establish BP neural network prediction model by using reconstructing failure data to predict SMISs failure behaviors. Finally, we use two groups of failure data series to verify the effectiveness of chaotic BP neural network model, and the experiment results verify that chaotic BP neural network model has more accurate prediction results compared with BP network, support vector machine, long short term memory networks (LSTM), and autoregressive model (AR).

中文翻译：

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基于相互依存网络的SMIS可靠性预测的混沌神经网络模型

随着工业互联网的发展，智能制造信息系统（SMIS）面临着更多的不确定性，动态性和复杂性。这些问题给SMIS的可靠性运行带来了更多挑战。为了解决上述问题，提出了一种基于相空间重构，混沌分析和BP神经网络的预测模型来预测SMIS的可靠性。首先，通过使用C‐C算法和Cao算法，将故障数据序列分解为规则性强的子数据序列组件。在此基础上，我们使用最大李雅普诺夫指数来识别故障数据序列的混沌特征。然后，通过重建故障数据来预测SMIS的故障行为，建立BP神经网络预测模型。最后，