Fault diagnosis using novel AdaBoost based discriminant locality preserving projection with resamples

Fault diagnosis plays a pivotal role in ensuring the safety of process industries. However, due to the diversity of process faults and the high coupling of fault data, it becomes very difficult to achieve high accuracy in the fault diagnosis of complex industrial processes. To address this concern, in this article, a novel AdaBoost-based discriminant locality preserving projection (DLPP) with resamples (A-DLPPR) model is proposed. The proposed A-DLPPR model has two features: to address the problem of matrix decomposition in DLPP, the bootstrap method is utilized to generate groups of resample data, and to obtain high classification accuracy, the AdaBoost-based classification technique is adopted. Finally, an effective fault diagnosis model using the proposed A-DLPPR model can be established. To validate the effectiveness of the proposed A-DLPPR model, the Tennessee Eastman process (TEP) is selected, and case studies using different kinds of TEP faults are conducted. The simulation results indicate that the proposed A-DLPPR model can achieve higher fault diagnosis accuracy than some other models, which verifies that in the field of complex industrial processes, the proposed A-DLPPR method can be used as an effective model for fault diagnosis.