An Extension Sample Classification‐Based Extreme Learning Machine Ensemble Method for Process Fault Diagnosis
Yuan Xu, Yan Jing Chen, Qun Xiong Zhu · Chemical Engineering & Technology · 2014
Abstract In order to achieve higher accuracy and faster response in complex process fault diagnosis, an extension sample classification‐based extreme learning machine ensemble (ESC‐ELME) method is proposed. In the realization process, the extension sample classification is used to divide the fault types. For each fault type, a specific extreme learning machine (ELM) is established and trained independently. Then, all specific ELMs are integrated to determine which fault is happened by the majority voting method. The proposed ESC‐ELME method is compared with the traditional ELM and a duty‐oriented hierarchical artificial neural network in fault diagnosis of the Tennessee Eastman process. The results demonstrate that the proposed method provides higher diagnosis accuracy and faster response.