Complexity-entropy causality plane (CECP) and ordinal transition network (OTN) are both crucial tools to reveal the characteristics of time series and distinguish complex systems. However, when the parameters of the system to be distinguished have a wide range of values, the distinguishing function of CECP is weakened. Therefore, we propose a new measure called transition Fisher information (TFI) based on the probability transition matrix in OTN. The TFI is combined with conditional entropy of ordinal patterns and complexity measure to form a novel three-dimensional graph, called transition-based complexity-entropy causality diagram(TB-CECD). These three statistics depict the complex system from different angles. Through simulation experiments, we prove that even if the parameters of complex systems are wide-ranging, the systems of different properties can be assigned to different areas of the graph. Moreover, we find that the trace of the transition probability matrix can be seen as a function of time delay and used to reflect the periodic information of the system. For applications, the proposed methods are applied to vehicle dynamic response data to diagnose periodic short-wave defects such as rail corrugation. The financial time series and Electroencephalographic (EEG) time series are also researched.

复杂度-熵因果平面（CECP）和序数过渡网络（OTN）都是揭示时间序列特征和区分复杂系统的关键工具。然而，当要区分的系统的参数具有宽范围的值时，CECP的区分功能被削弱。因此，我们基于OTN中的概率转移矩阵，提出了一种称为转移费舍尔信息（TFI）的新度量。TFI与序数模式的条件熵和复杂性度量相结合，形成了一种新颖的三维图，称为基于过渡的复杂性-熵因果图（TB-CECD）。这三个统计数据从不同角度描述了复杂的系统。通过仿真实验，我们证明即使复杂系统的参数范围很广，可以将具有不同属性的系统分配给图形的不同区域。此外，我们发现过渡概率矩阵的轨迹可以看作是时间延迟的函数，并且可以用来反映系统的周期性信息。对于应用，所提出的方法被应用于车辆动态响应数据以诊断周期性的短波缺陷，例如轨道波纹。还研究了金融时间序列和脑电图（EEG）时间序列。所提出的方法被应用于车辆动态响应数据，以诊断周期性的短波缺陷，例如轨道波纹。还研究了金融时间序列和脑电图（EEG）时间序列。所提出的方法被应用于车辆动态响应数据，以诊断周期性的短波缺陷，例如轨道波纹。还研究了金融时间序列和脑电图（EEG）时间序列。