A novel process monitoring and fault detection approach based on statistics locality preserving projections

He, Fei; Xu, Jun

doi:10.1016/j.jprocont.2015.11.004

Cited by 76 publications

(67 citation statements)

References 27 publications

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“…One of these solutions is to use filters, such as extended Kalman filters and particle filters, but the details of the filters are not discussed here. Moreover, He and Xu used statistics pattern analysis to identify the non‐Gaussian statistical properties of the process data, which is also based on high‐order statistics. Lou et al transformed a non‐Gaussian process into a Gaussian process via pre‐summation of the process data and then applied PCA for process monitoring.…”

Section: Issues In Process Monitoringmentioning

confidence: 99%

Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

et al. 2018

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Multivariate statistical process monitoring (MSPM) methods are significant for improving production efficiency and enhancing safety. However, to the authors’ best knowledge, there is no survey paper providing statistics of published papers over the past decade. In this paper, several issues related to MSPM methods are reviewed and studied. First, the annual publication numbers of journal articles concerning MSPM are provided to show the active development of this important research field and to point out several promising directions in the future. Second, the annual numbers of patents are also shown to demonstrate the practicality of different MSPM methods. Particularly, this paper also lists and analyzes the number of MSPM‐related publications in China. The statistics indicate that Chinese researchers and engineers may have different viewpoints from those of other countries, which results in different development trends of MSPM in China.

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Section: Issues In Process Monitoringmentioning

confidence: 99%

Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

et al. 2018

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“…However, the process information of the data track has great loss, and the data correlation of point to point is reduced, which will lead to a decrease in the reliability of the data. To improve the performance of fault detection in unequal length batch process and reduce the complexity of the algorithm, the statistics pattern analysis (SPA) algorithm is used to pre‐process the semiconductor data. The mean and variance of each unequal length batch are calculated directly, according to Equations and .…”

Section: Simulation and Analysismentioning

confidence: 99%

Fault detection based on difference locality preserving projections for the semiconductor process

Guo

Zhong

2018

Journal of Chemometrics

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Some existing fault detection methods for the semiconductor process, such as principal component analysis and locality preserving projections (LPP), are linear algorithms, so they degrade the performance of fault detection in a nonlinear process. In addition, they are not effective for fault detection in a multimodal process. To solve the problems caused by nonlinear and multimodal characteristics in the semiconductor process, a new difference (DIF) pre-processing strategy is proposed to normalize the nonlinear and multimodal data. After detailed analysis of DIF, a new method called DIF-LPP is developed for fault detection in the semiconductor process. The nonlinear and multimodal data can be transformed into data sets that follow a Gaussian and single mode distribution, respectively. The proposed method contains a model without prior knowledge of the nonlinear and multimodal process. To demonstrate the proposed method's effectiveness, it is applied to 2 numerical examples and the semiconductor process. Simulation results verify that the proposed method is effective for fault detection in the nonlinear and multimodal process.

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“…The squared prediction error statistic is a measurement of the variation in residual space and is used to measure the goodness of fit of the new sample to the model; it is defined as follows [20]:…”

Section: Selection Of Parametermentioning

confidence: 99%

Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding

She

Tian

Liang

et al. 2018

Shock and Vibration

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The dimension reduction methods have been proved powerful and practical to extract latent features in the signal for process monitoring. A linear dimension reduction method called nonlocal orthogonal preserving embedding (NLOPE) and its nonlinear form named nonlocal kernel orthogonal preserving embedding (NLKOPE) are proposed and applied for condition monitoring and fault detection. Different from kernel orthogonal neighborhood preserving embedding (KONPE) and kernel principal component analysis (KPCA), the NLOPE and NLKOPE models aim at preserving global and local data structures simultaneously by constructing a dual-objective optimization function. In order to adjust the trade-off between global and local data structures, a weighted parameter is introduced to balance the objective function. Compared with KONPE and KPCA, NLKOPE combines both the advantages of KONPE and KPCA, and NLKOPE is also more powerful in extracting potential useful features in nonlinear data set than NLOPE. For the purpose of condition monitoring and fault detection, monitoring statistics are constructed in feature space. Finally, three case studies on the gearbox and bearing test rig are carried out to demonstrate the effectiveness of the proposed nonlinear fault detection method.

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A novel process monitoring and fault detection approach based on statistics locality preserving projections

Cited by 76 publications

References 27 publications

Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

Fault detection based on difference locality preserving projections for the semiconductor process

Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding

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