MANAGEMENT OF TIME-VARYING NORMAL STATES IN DYNAMIC SYSTEMS AND ITS APPLICATION

动态系统时变正规态的管理及其应用

• 批准号: 06650862
• 负责人: TSUGE Yoshifumi
• 金额: $ 1.15万
• 依托单位: KYUSHU UNIVERSITY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for General Scientific Research (C)
• 财政年份: 1994
• 资助国家: 日本
• 起止时间: 1994 至 1995
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-06650862/
• 关键词: Process Systems; Autoregressive Exogenous Model; Neural Network; Sequential Probability Ratio Test; Fault Diagnosis; Fault Detection

Management system for prediction and supervision of a state in a dynamic system using time series data obtained in plant operations has been discussed. The following functions were considered to be required to the system :Function (1) : Prediction of an uncertainly time-varying normal state in the future.Function (2) : Judgment of whether a present state is under normal operation of not.Function (3) : Setting of upper and lower limits of alarms according to operational conditions.Function (4) : Extraction of characteristics of abnormal states for fault diagnosis.From the experiments and simulations applying the proposed method to a tank-pipeline process, the following can be concluded that :1.Autoregressive exogenous model (ARX model) , which is a linear model, can be used for Function (1) , even if normal values of state variables in the weakly nonlinear process (tank-pipeline process) are uncertainly time-varying due to input fluctuations.2.3 types of neural networks (feedfoward, external reccurent, general reccurent) , which are nonlinear model, can be also used for Function (1). Then the general reccurent neural network gives the best prediction.3.Sequential probability ratio test (SPRT) based on the error residual between the measured value and the corresponding value predicated by Function (1) can be used for Function (2).4.Function (4) can be realized by the improvement of Function (3) so that 2 SPRT are simultaneously performed : one test examines whether the error residual is normal or higher than normal, the other examines whether it is normal or lower than normal.5.Function (3) remains as a future work, but may be feasible by the extension of Function (4).

本文讨论了利用装置运行中获得的时间序列数据对动态系统状态进行预测和监督的管理系统。我们认为系统需要以下功能：功能(1)：对未来不确定时变常态的预测。功能(2)：判断当前状态是否处于正常运行状态。功能(3)：根据运行情况设置告警上下限。功能(4)：提取异常状态特征进行故障诊断。将该方法应用于某储罐-管道过程的实验和仿真，可以得出以下结论：函数(1)可以采用自回归外生模型（ARX模型），它是一种线性模型，即使弱非线性过程（罐-管道过程）中状态变量的正态值由于输入波动而不确定时变。2.3非线性模型的神经网络类型（前馈、外循环、一般循环）也可以用于函数(1)。一般递归神经网络给出了最好的预测结果。对于函数(2)，可以使用基于实测值与函数(1)预测的对应值之间的误差残差的序列概率比检验（SPRT）。通过对函数(3)的改进，可以实现函数(4)，使2次SPRT同时进行：一次检验误差残差是否正常或高于正常，另一次检验误差残差是否正常或低于正常。函数(3)仍然是未来的工作，但可以通过扩展函数(4)来实现。

项目成果

Yoshifumi Tsuge: "Fault Detection and Diagnosis in a Continuous Process with Uncertainly Normal Situations" Kagaku Kogaku Ronbunshu. Vol.21. 565-572 (1995)

Yoshifumi Tsuge：“在不确定的正常情况下连续过程中的故障检测和诊断” Kagaku Kogaku Ronbunshu。

柘植義文: "不確定な正常状態で運転される連続プロセスの異常の検出と診断" 化学工学論文集. 21. 565-572 (1995)

Yoshifumi Tsuge：“在不确定的正常条件下运行的连续过程中异常的检测和诊断”《化学工程杂志》21. 565-572 (1995)。