Yu Zhang

Dynamic kernel principal component analysis (DKPCA) has been frequently implemented for nonlinear and dynamic process monitoring of complex industrial processes. However, traditional DKPCA focuses only on the global structural analysis of data sets and strongly neglects the local information, which is equally essential for process detection and identification. In this paper, an improved DKPCA, referred to as the local DKPCA (LDKPCA), is proposed based on local preserving projections (LPP) for nonlinear dynamic process fault diagnosis. The method combines the advantages of LPP and DKPCA by utilizing the local structure feature to maintain the geometric structure of the data in a unified framework. To achieve a highly comprehensive feature extraction, the local characteristics are fused in DKPCA to produce an optimization objective. The neighbouring points of the new objective function projection in the feature space are still maintained in proximity, and the variance information is retained simultaneously. For the purpose of fault detection, two statistics, known as the T-2 and squared prediction error (SPE) statistics, are constructed, based on the LDKPCA model, and used to monitor the latent variable space and the residual space, respectively. In addition, the sensitivity analysis is brought in for fault identification of the two statistics. Based on the experimental analysis using the shaft breakage data of an offshore oilfield electric submersible pump (ESP), the proposed method outperforms the conventional DKPCA in terms of fault monitoring performance. The experimental results demonstrate the potential of the method in nonlinear dynamic process fault diagnosis.

Electric submersible pump (ESP) in offshore oilfields is one of the important artificial lifting methods to achieve high and stable production. The complexity of the ESP system and the long pumping cycle result in data having the typical characteristics of "a large amount of data and a small amount of information". Therefore, the scarcity of valid samples causes a major challenge for ESP fault diagnosis. To address these practical problems, we propose an intelligent virtual sample generation method that introduces the idea of multi-distribution mega trend diffusion (MD-MTD) into conditional generative adversarial networks (MCGAN-VSG). In the MCGAN-VSG method, the acceptable diffusion range of the sample attributes is first obtained by estimating the samples using the triangular probability distribution model constructed in MD-MTD. Secondly, the Borderline-SMOTE and uniform distribution were added to describe the small sample properties, and suitable output samples are generated to fill the information gap between samples for resampling with Bootstrap. Thirdly, CGAN is used to generate the input samples corresponding to the output samples. Finally, the accuracy of the classification model is improved by generating a large number of virtual samples with an extremely limited number of fault samples. In order to verify the advantages of the proposed MCGAN-VSG, the quality of the input and output virtual samples generated via the method is investigated through a two-dimensional standard function. The proposed method is further applied to the fault diagnosis of ESP in an offshore oil field, and the effectiveness of MCGAN-VSG is verified with actual industrial data. The MCGAN-VSG was compared with most advanced methods such as MTD, TTD, Bootstrap and MD-MTD, and the experimental results show that the proposed method is superior to all other methods.