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.

故障诊断对于确保过程工业的安全起着至关重要的作用。但是，由于过程故障的多样性和故障数据的高度耦合，在复杂的工业过程的故障诊断中很难达到很高的精度。为了解决这个问题，在本文中，提出了一种基于AdaBoost的带有重采样的判别局部性保留投影（DLPP）模型（A-DLPPR）模型。提出的A-DLPPR模型具有两个特点：为解决DLPP中矩阵分解的问题，采用自举法生成多组重采样数据，并获得较高的分类精度，采用基于AdaBoost的分类技术。最后，可以使用提出的A-DLPPR模型建立有效的故障诊断模型。为了验证所提出的A-DLPPR模型的有效性，选择了田纳西伊士曼过程（TEP），并使用不同种类的TEP故障进行了案例研究。仿真结果表明，所提出的A-DLPPR模型比其他模型具有更高的故障诊断精度，验证了在复杂工业过程领域，所提出的A-DLPPR方法可以作为有效的故障诊断模型。