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中文摘要：
在现代化工过程中,故障在局部出现后往往会通过物流连接、控制系统的作用下传递到整个流程。识别故障的传播机理,诊断出故障的根本原因对于生产的经济性或安全性都具有重大的意义。时滞分析是一种基于数据的算法,具有不依赖机理模型,获得的结果易于解读等特性,被认为是一种很实用的故障识别算法。将小波降噪技术、全流程节点划分等方法与时滞分析算法相结合,可以较好地克服化工过程在线数据的强噪声特点以及模型中冗余时滞的干扰,改进的算法具有更好的鲁棒性,具有化工全流程诊断的能力。通过对TE模型的故障定位研究,结果显示该方法可对异常工况进行故障隔离,并能给出扰动传播路径,有助于故障机理的研究。
英文摘要：
In modern chemical processes,disturbances often occur at a local equipment or stream.It will affect the performance of the whole process due to connectivity of streams and complex control system.To identify the root cause of the fault and the path in which the disturbance propagates is of great significance for the safety or economics of chemical production.The algorithm of time-delay analysis is regarded as a practical method for fault identification with some good aspects,such as model-free causal analysis,fully data driven methods,simple interpretation.An improved time-delay approach,which combined wavelet denoising and node-divide techniques was proposed.The new method could overcome the strong noise characteristics of the data from chemical production.And it could reduce the interference of redundancy delay which was inherent of time-delay algorithm used for plant-wide analysis.The approach was applied to the Tennessee Eastman（TE）model and the result proved its good quality for industrial application.
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