Shewhart Control Charts for Individuals with Time-Ordered Data

Struct Control Health Monit

Hamida

et al. 2018

The objective of this paper is to propose a new damage detection technique based on multiscale partial least squares (MSPLS) and optimized exponentially weighted moving average (OEWMA) generalized likelihood ratio test (GLRT) to enhance monitoring of structural systems. The developed technique attempts to combine the advantages of the exponentially weighted moving average (EWMA) and GLRT charts with those of multiscale input-output model partial least square (PLS) and multi-objective optimization. The damage detection problem is addressed so that the data are first modeled using the MSPLS method and then the damages are detected using the OEWMA-GLRT chart. The idea behind the developed OEWMA-GLRT is to compute an optimal statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data and selects the EWMA parameters that minimizes the (MDR), the false alarm rate (FAR) and the average run length (ARL 1 ). This helps provide a more accurate estimation of the GLRT statistic and provide a stronger memory that enables better decision making with respect to damage detection. The performance of the developed technique is assessed and compared with PLS-based GLRT, PLS-based OEWMA, and PLS-based OEWMA-GLRT techniques using two illustrative examples, synthetic data and simulated International Association for Structural Control-American society of Civil engineers (IASC-ASCE) benchmark structure. The results demonstrate the effectiveness of the MSPLS-based OEWMA-GLRT technique over the PLS-based GLRT, PLS-based OEWMA, and PLS-based OEWMA-GLRT methods in terms of MDR, FAR, and ARL 1 values. KEYWORDS damage detection, exponentially weighted moving average, generalized likelihood ratio test, multiscale, partial least squares, structural health monitoring Struct Control Health Monit. 2019;26:e2287.wileyonlinelibrary.com/journal/stc

Section: Oewma‐glrt Detection Chart Descriptionmentioning

confidence: 99%

Multivariate statistical process control-based hypothesis testing for damage detection in structural health monitoring systems

Chaabane

Struct Control Health Monit

Hamida

et al. 2018

“…Univariate methods have been also used to improve the failure detection capabilities. These methods include Shewhart chart, EWMA chart, cumulative sum chart, and GLRT chart . In previous studies, PCA‐based GLRT, PLS‐based GLRT, kernel PCA–based GLRT, and kernel PLS–based GLRT techniques have been investigated for failure detection in different applications.…”

Section: Related Work On Failure Detection In Pv Systemsmentioning

confidence: 99%

“…Other univariate‐ and multivariate‐based failure detection techniques have been developed for PV systems monitoring . The multivariate detection techniques are mainly based on partial least squares (PLS) and principal component analysis (PCA), while the univariate techniques include cumulative sum, Shewhart, and exponentially weighted moving average (EWMA) charts . In previous studies, the authors have proved that the classical GLRT chart–based equal variances present a better detection efficiency against the EWMA and Shewhart statistics, which is due to the ability of the GLRT to minimize the false alarm rate (FAR).…”

Section: Introductionmentioning

confidence: 99%

Enhanced generalized likelihood ratio test for failure detection in photovoltaic systems

Int Trans Electr Energ Syst

Hajji

Trabelsi

et al. 2018

Summary In this paper, a new multiscale weighted generalized likelihood ratio test (MS‐WGLRT) chart is proposed for enhanced failure detection in photovoltaic systems. The main weakness of the classical generalized likelihood ratio test chart is in dealing with residual samples while ignoring their natural variances. By taking into consideration the nature variance of the detection residual and applying a multiscale representation, the proposed technique allows the reduction in false alarm and missed detection rates compared with the classical generalized likelihood ratio test chart. The multiscale representation of data is an efficient data analysis and feature extraction tool that has a great impact on the effectiveness of failure detection. The effectiveness of the proposed method is evaluated on a simulated photovoltaic data where the developed chart is used for detecting single and multiple failures (eg, bypass, mix, and shading failures). The simulation results show that the multiscale weighted generalized likelihood ratio test method offers better performance compared with the classical generalized likelihood ratio chart.

“…The data‐driven approaches, such as partial least squares (PLS), and principal component analysis (PCA), have been widely applied for industrial monitoring. The model‐based methods, mainly the Shewhart chart, exponentially weighted moving average (EWMA) chart, cumulative sum (CUSUM) chart, and generalized likelihood ratio test (GLRT) chart, have been also used to improve the FD capabilities.…”

Section: Introductionmentioning

confidence: 99%

Monitoring biological processes using univariate statistical process control

Al-Khazraji

Teh³

et al. 2018

Can J Chem Eng

Biological modelling is a challenging task specifically when state variables are difficult or even impossible to be measured. Consequently, monitoring quality of biological process will be impacted negatively due to the lack of an accurate model capable of reflecting precisely the process dynamics. Moreover, the faults in such systems cannot be detected robustly. The current work proposes a novel approach that combines state estimation with process monitoring techniques. The developed approach, named as particle filter (PF)-based multiscale maximum double exponentially weighted moving average (MS-M-DEWMA) chart, includes two main phases. In the first phase, the PF technique is applied to estimate the unknown nonlinear states of the biological processes. In the second phase, the statistical univariate chart, MS-M-DEWMA is adopted to address fault detection in biological processes. Therefore, in this work, we propose a monitoring approach capable of detecting shifts in mean and/or variance in biological systems (pre-defined structure obtained using material and energy balances) where the variables are estimated using state estimation techniques. The detection chart MS-M-DEWMA is applied to the residuals computed using the PF. The advantages of PF-based MS-M-DEWMA method are threefold: (i) extract features and decorrelate measurements using dynamical multiscale representation; (ii) estimate the state of nonlinear biological processes using the PF technique; and (iii) enhance monitoring of biological processes through detecting shifts of both variance and mean using MS-M-DEWMA chart. The proposed approach is validated using a Cad system in E. coli (CSEC) model.