An Extension Sample Classification-Based Extreme Learning Machine Ensemble Method for Process Fault Diagnosis

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.