Recursive PCA for adaptive process monitoring

We propose an adaptive and domain-specific service monitoring approach to detect partner service errors in a cost-effective manner. Hereby, we not only consider generic errors such as file not found or connection timed out, but also take domain-specific errors into account. The detection of each type of error entails a different monitoring cost in terms of the consumed resources. To reduce costs, we adapt the monitoring frequency for each service and for each type of error based on the measured error rates and a cost model. We introduce an industrial case study from the broadcasting and content-delivery domain for improving the user-perceived reliability of Smart TV systems. We demonstrate the effectiveness of our approach with real data collected to be relevant for a commercial TV portal application. We present empirical results regarding the trade-off between monitoring overhead and error detection accuracy.Our results show that each service is usually subject to various types of errors with different error rates and exploiting this variation can reduce monitoring costs by up to 30% with negligible compromise on the quality of monitoring.iii

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“…The monitoring frequency is adapted based on the detected changes. A similar approach was proposed for adaptive process monitoring [24] as well.…”

Section: Chapter V Related Workmentioning

confidence: 99%

Adaptive Domain-Specific Service Monitoring

Ünsal¹,

Sazara²,

Aktemur

et al. 2014

Lecture Notes in Computer Science

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“…The time-varying characteristics of WWTPs include their mean, variance, and correlation among their measurements (Rosen and Lennox, 2001). When a time-invariant PCA model is used to monitor processes with time-varying behaviors, false alarms often result, significantly compromising the reliability of the monitoring system (Li et al, 2000).…”

Section: Adaptive Mpcamentioning

confidence: 99%

“…However, the cross-validation approach that was used for the MPCA model indicated is not suitable, because old data are not representative for the current process. Therefore, the number of significant principal components is calculated recursively using the cumulative percent variance (CPV) method (Li et al, 2000). The CPV is a measure of the percent variance captured by the first R principal components: where the l represents the eigenvalues of X and V ‫ס‬ EE T /(I − 1).…”

Section: Adaptive Mpcamentioning

confidence: 99%

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Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis

Lee

Vanrolleghem²

2003

Biotech & Bioengineering

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Abstract:Multiway principal component analysis (MPCA) for the analysis and monitoring of batch processes has recently been proposed. Although MPCA has found wide applications in batch process monitoring, it assumes that future batches behave in the same way as those used for model identification. In this study, a new monitoring algorithm, adaptive multiblock MPCA, is developed. The method overcomes the problem of changing process conditions by updating the covariance structure recursively. A historical set of operational data of a multiphase batch process was divided into local blocks in such a way that the variables from one phase of a batch run could be blocked in the corresponding blocks. This approach has significant benefits because the latent variable structure can change for each phase during the batch operation. The adaptive multiblock model also allows for easier fault detection and isolation by looking at the relationship between blocks and at smaller meaningful block models, and it therefore helps in the diagnosis of the disturbance. The proposed adaptive multiblock monitoring method is successfully applied to a sequencing batch reactor for biological wastewater treatment.

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“…The T 2 statistic measures the variation of principal components, and the Q statistic measures the variation of nonprincipal components. Later on, many improvements have been studied, such as the multiway PCA for batch processes (Nomikos and MacGregor, 1994), dynamic PCA introducing dynamic behavior into the PCA model (Ku et al, 1995), multiscale PCA based on wavelet analysis (Bakshi, 1998), recursive PCA (Li et al, 2000), dynamic PCA for batch monitoring with time-lagged windows (Chen and Liu, 2002), kernel PCA for nonlinear process monitoring (Cho et al, 2005;Lee et al, 2004;Schölkopf et al, 1998), and Robust multiscale PCA (Wang and Romagnoli, 2005). Qin (2003) reviewed several fault detection indices associated with T 2 statistic and Q statistic and compared the reconstruction-based approach and the contribution-based approach with simulation and industrial examples.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection in Chemical Processes Using Discriminant Analysis and Control Chart

Pei

Yamashita

Yoshida

et al. 2008

J. Chem. Eng. Japan

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This paper presents a fault detection methodology based on the Fisher discriminant analysis (FDA) and individuals control charts (XmR control charts). As the first step, FDA is used to find the optimal discriminant direction between the normal operation data and the fault data. In the next step, XmR control charts on the discriminant direction are used to monitor the process. To reduce the amount of false alarms, we also used a variable selection technique based on the contribution plot of FDA. The performance of the proposed technique is demonstrated through application to the monitoring of the Tennessee Eastman challenge process.

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Recursive PCA for adaptive process monitoring

Cited by 753 publications

References 19 publications

Adaptive Domain-Specific Service Monitoring

Adaptive Domain-Specific Service Monitoring

Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis

Fault Detection in Chemical Processes Using Discriminant Analysis and Control Chart

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