DPLS-MSOM Modeling for Visual Industrial Fault Diagnosis and Monitoring Based on Variation Data from Normal to Anomalous

作者：Tang, Jiawei; Yan, Xuefeng^*

来源：Industrial & Engineering Chemistry Research, 2017, 56(49): 14518-14529.

DOI：10.1021/acs.iecr.7b02590

• SELF-ORGANIZING MAP
• PARTIAL LEAST-SQUARES
• CHEMICAL-PROCESSES
• CLASSIFICATION
• REDUCTION
• BENCHMARK

Process variables obtain significant dynamic variation characteristics from the moment faults introduced through a continuous period of time. Therefore, these data possess not only strong corresponding fault information but also robust dynamic characteristics that can be effectively used for fault diagnosis. Accordingly, the dynamic partial least squares (DPLS) and self-organizing map (SOM) combination methodology is utilized for visual fault diagnosis and monitoring. Different types of fault data (from normal to anomalous) are collected and trained by DPLS in the form of a dynamic data matrix to reveal the most discriminated orientations for the different statuses. A multilayer SOM is then trained to model the relationship with different status data under such orientations than the regular positions on a two-dimensional map. The trained map can be used to decide the real operating state of the online observations. Excellent experimental results of the Tennessee Eastman chemical process have demonstrated that DPLS-MSOM is quite qualified for nonlinear and random faults. This method provides new innovation in fault diagnosis on the basis of dynamic analysis of normal to anomalous data.

• 出版日期2017-12-13
• 单位华东理工大学