Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS

Zhang, Yingwei; Ma, Chi

doi:10.1016/j.ces.2010.10.008

Cited by 166 publications

(78 citation statements)

References 28 publications

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“…In terms of achievable fault alarming rates given in Table 3, we observe that both the KPCA-based and LKPCAbased schemes provide similar good fault detection performance for Faults IDV(1), IDV(2), IDV(4), IDV(6), IDV(7), IDV(8), IDV(12), IDV(13), IDV (14), and IDV (18). However, the two methods both perform poorly for Faults IDV(3), IDV (9), and IDV (15). The previous works [15,41] have also found that these faults prove to be difficult for data-driven detection methods because there are no observable changes in the mean or the variance of these fault data sets.…”

Section: Fault Detection Performancementioning

confidence: 61%

“…A, B and C feed compositions (stream 4) Random variation IDV (9) D feed temperature (stream 2) Random variation IDV (10) C feed temperature (stream 4) Random variation IDV (11) Reactor cooling water inlet temperature Random variation IDV (12) Condenser cooling water inlet temperature Random variation IDV (13) Reaction kinetics Slow shift IDV (14) Reactor cooling water valve Sticking IDV (15) Condenser cooling water valve Sticking IDV (16)- (20) Unknown Unknown IDV (21) Valve position constant (stream 4) Constant position fault detection time and fault alarming rate. Fault detection time is defined as the first sample number after previous eight consecutive samples have exceeded the confidence limit, while fault alarming rate is defined as the percentage of the alarming samples in all the fault samples.…”

Section: Idv(8)mentioning

confidence: 99%

“…Cho et al [11] and Choi et al [12] formulated two fault identification strategies using KPCA. In order to analyse multiscale data, multi-scale KPCA methods were studied by combining the wavelet analysis and ensemble empirical mode decomposition [13][14][15][16]. To improve the computation efficiency, Tian et al [17] and Cui et al [18] used feature sample selection to reduce the computational complexity of calculating the kernel matrix in KPCA.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis

Deng

Tian

Chen

2013

Chemometrics and Intelligent Laboratory Systems

111

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a b s t r a c t a r t i c l e i n f oTraditional kernel principal component analysis (KPCA) concentrates on the global structure analysis of data sets but omits the local information which is also important for process monitoring and fault diagnosis. In this paper, a modified KPCA, referred to as the local KPCA (LKPCA), is proposed based on local structure analysis for nonlinear process fault diagnosis. In order to extract data feature better, local structure analysis is integrated within the KPCA, and this results in a new optimisation objective which naturally involves both global and local structure information. With the application of usual kernel trick, the optimisation problem is transformed into a generalised eigenvalue decomposition on the kernel matrix. For the purpose of fault detection, two monitoring statistics, known as the T 2 and Q statistics, are built based on the LKPCA model and confidence limit is computed by kernel density estimation. In order to identify fault variables, contribution plots for monitoring statistics are constructed based on the idea of sensitivity analysis to locate the fault variables. Simulation using the Tennessee Eastman benchmark process shows that the proposed method outperforms the traditional KPCA, in terms of fault detection performance. The results obtained also demonstrate the potential of the proposed fault identification approach.

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Section: Fault Detection Performancementioning

confidence: 61%

Section: Idv(8)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis

Deng

Tian

Chen

2013

Chemometrics and Intelligent Laboratory Systems

111

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“…The Gussian kernel and its parameter above 2 5 are the best choice for rotating machinery feature vector dimensionality reduction by using KPCA.…”

Section: Resultsmentioning

confidence: 99%

“…It has a better performance than PCA for handling the nonlinear problem. Rotating mechanical failures often show a nonlinear behavior, the method based on KPCA is more suitable for rotating machinery fault feature dimensionality reduction [5][6].…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization in KPCA for Rotating Machinery Feature Vector Dimensionality Reduction

Jiang

Tang

2011

AEF

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Abstract. This research aims at revealing the rules that the impact of kernel function and its parameters on the performance of kernel principle component analysis (KPCA) for dimensionality reduction. KPCA was performed on nine databases by using different kernel functions and a series of equal space kernel parameters. The relation charters between kernel parameters and the number of kernel principle components were constituted. It found that the Gussian kernel and its parameter above 2 5 are the best choice for rotating machinery feature vector dimensionality reduction by using KPCA. This study presents a reference and gist for the application of KPCA in rotating machinery fault diagnostic case.

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Data‐Variant Kernel Analysis

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Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS

Cited by 166 publications

References 28 publications

Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis

Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis

Parameter Optimization in KPCA for Rotating Machinery Feature Vector Dimensionality Reduction

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