Primary-Auxiliary Statistical Local Kernel Principal Component Analysis and Its Application to Incipient Fault Detection of Nonlinear Industrial Processes

Deng, Xiaogang; Cai, Peipei; Deng, Jiawei; Cao, Yanlong; Song, Zhihuan

doi:10.1109/access.2019.2937886

Cited by 7 publications

(3 citation statements)

References 50 publications

(53 reference statements)

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“…A nonlinear continuous stirred tank reactor (CSTR) is considered to validate the performance of proposed KPCA-KD FD scheme. The CSTR problem has been used in many fault detection based problems over the last few years [29], [50], [51]. The CSTR process involves a non-isothermal, irreversible first order reaction of the form:…”

Section: A Continuous Stirred Tank Reactor (Cstr)mentioning

confidence: 99%

Kantorovich Distance Based Fault Detection Scheme for Non-Linear Processes

2022

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Fault detection is necessary for safe operation in modern process plants. The kernel principal component analysis (KPCA) technique has been widely utilized for monitoring non-linear processes because it enhances dimension reduction and fault detection in non-linear space. In this paper, an improved nonlinear fault detection strategy based on Kantorovich distance (KD) and kernel principal component analysis is proposed. The KD statistic is based on the optimal mass transport theory where the distance between two distributions is computed with respect to a cost function. The addressed fault detection problem models the data using the KPCA framework and utilizes the ability of the KD statistical indicator to detect faults. The detection stage involves comparing the residuals of training fault-free data and testing faulty data using the KD statistic. Additionally, the reference threshold for the KD statistic is computed using the kernel density estimation (KDE) approach as compared to the previously utilized three-sigma rule approach. The detection performance is illustrated with the help of three benchmark case studies: a continuous stirred tank reactor (CSTR) process, Tennessee Eastman (TE) process and an experimental distillation column process. The performance analysis suggests the superiority of the KPCA-KD fault detection scheme in monitoring various sensor faults. Moreover, comparison with traditional statistical indicators of PCA and KPCA schemes shows that the proposed scheme enhances fault detection and achieves an improved detection rate in monitoring different categories of faults.INDEX TERMS Kantorovich distance, kernal principal component analysis, continuous stirred tank reactor process, Tennessee Eastman process, experimental distillation column process, fault detection.

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Section: A Continuous Stirred Tank Reactor (Cstr)mentioning

confidence: 99%

Kantorovich Distance Based Fault Detection Scheme for Non-Linear Processes

2022

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“…, and the number of principal elements is determined by the cumulative variance contribution rate (CPV) method, where  is the Gaussian radial basis kernel function parameter [19] . The fault detection based on the KPCA method is essentially a nonlinear extension of the PCA method.…”

Section: A Basic Principles Of Fault Detection Based On Kernel Princmentioning

confidence: 99%

Fault Diagnosis of Wind Turbine Pitch System Based on Multiblock KPCA Algorithm

Xin

2021

IEEE Access

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When the wind turbine pitch system is in operation, due to the strong coupling of the internal structure of the system, it is difficult to accurately locate the fault only relying on prior knowledge. And when using the data-driven contribution graph method for fault location, due to the influence of the fault variable, the contribution value of the non-fault variable will become larger, which will cause a tailing effect and misdiagnosis. In this paper, a multiblock kernel principal component algorithm (MBKPCA) is proposed. In this algorithm, the variables of the pitch system operation process are divided into several variable blocks based on the historical fault data and perform faults based on the kernel principal component algorithm for each variable block diagnosis. Taking historical data of an area in North China as an actual calculation example, the proposed block kernel principal component analysis algorithm is compared with the traditional kernel principal component analysis algorithm. The results show that the MBKPCA can effectively reduce the "tailing effect" of the fault variable on the non-fault variable when the contribution graph method is used to identify the fault source, and the method can accurately locate the fault source and achieves higher detection accuracy. INDEX TERMS fault diagnosis， kernel principal component algorithm，wind turbine pitch system， characteristic variables

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“…SLA was originally proposed by Basseville [37] for inspecting the process parameter changes. In recent years, some researchers have introduced it into the chemical process fault detection and demonstrated its effectiveness [38][39][40]. In this paper, we will perform the statistical local analysis on the CVA model.…”

Section: Slcvaknn Model Assisted By Statistical Local Analysismentioning

confidence: 99%

Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method

et al. 2021

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Aiming at the characteristics of dynamic correlation, periodic oscillation, and weak disturbance symptom of power transmission system data, this paper proposes an enhanced canonical variate analysis (CVA) method, called SLCVAkNN, for monitoring the disturbances of power transmission systems. In the proposed method, CVA is first used to extract the dynamic features by analyzing the data correlation and establish a statistical model with two monitoring statistics T2 and Q. Then, in order to handling the periodic oscillation of power data, the two statistics are reconstructed in phase space, and the k-nearest neighbor (kNN) technique is applied to design the statistics nearest neighbor distance DT2 and DQ as the enhanced monitoring indices. Further considering the detection difficulty of weak disturbances with the insignificant symptoms, statistical local analysis (SLA) is integrated to construct the primary and improved residual vectors of the CVA dynamic features, which are capable to prompt the disturbance detection sensitivity. The verification results on the real industrial data show that the SLCVAkNN method can detect the occurrence of power system disturbance more effectively than the traditional data-driven monitoring methods.

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Primary-Auxiliary Statistical Local Kernel Principal Component Analysis and Its Application to Incipient Fault Detection of Nonlinear Industrial Processes

Cited by 7 publications

References 50 publications

Kantorovich Distance Based Fault Detection Scheme for Non-Linear Processes

Kantorovich Distance Based Fault Detection Scheme for Non-Linear Processes

Fault Diagnosis of Wind Turbine Pitch System Based on Multiblock KPCA Algorithm

Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method

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