A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Pilario, Karl Ezra S.; Shafiee, Mahmood; Cao, Yi; Lao, Liyun; Yang, Shuang–Hua

doi:10.3390/pr8010024

Cited by 89 publications

(63 citation statements)

References 322 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We further note that the performance of the NARX can be improved in these experiments by training it with more data than just 1001 samples. The kernel-based SIMs, on the other hand, may become too slow for prediction when trained with larger data sets [41]. Nevertheless, our proposed FR-MKCVA is intended for scenarios where training data has a moderate sample size but is high-dimensional.…”

Section: Fr-mkcva: Testing Phasementioning

confidence: 99%

“…For these scenarios, we have shown that certain improvements in the kernel design can produce better kernel-based SIMs. This calls for more research into kernel design in the future, such as exploring deep and ensemble kernel architectures [41]. By improving model generalization ability, industrial activities that depend on these models can perform better.…”

Section: Fr-mkcva: Testing Phasementioning

confidence: 99%

See 1 more Smart Citation

A Kernel Design Approach to Improve Kernel Subspace Identification

Pilario

Cao

Shafiee

2021

IEEE Trans. Ind. Electron.

Self Cite

View full text Add to dashboard Cite

Subspace identification methods, such as canonical variate analysis (CVA), are non-iterative tools suitable for the state-space modelling of multi-input, multioutput (MIMO) processes, e.g. industrial processes, using input-output data. To learn nonlinear system behavior, kernel subspace techniques are commonly used. However, the issue of kernel design must be given more attention because the type of kernel can influence the kind of nonlinearities that the model can capture. In this paper, a new kernel design is proposed for CVA based identification, which is a mixture of a global and local kernel to enhance generalization ability and includes a mechanism to vary the influence of each process variable into the model response. During validation, model hyper-parameters were tuned using random search. The overall method is called Feature-Relevant Mixed Kernel Canonical Variate Analysis (FR-MKCVA). Using an evaporator case study, the trained FR-MKCVA models show a better fit to observed data than those of single-kernel CVA, linear CVA, and neural net models under both interpolation and extrapolation scenarios. This work provides a basis for future exploration of deep and diverse kernel designs for system identification.

show abstract

Section: Fr-mkcva: Testing Phasementioning

confidence: 99%

Section: Fr-mkcva: Testing Phasementioning

confidence: 99%

A Kernel Design Approach to Improve Kernel Subspace Identification

Pilario

Cao

Shafiee

2021

IEEE Trans. Ind. Electron.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, FGMM-BIP index also uses the Bayesian inference technique and a unified monitoring indicator, similar to the FKF. PCAbased T 2 and SPE are extensively applied to detecting faulty operation in process industries [31].…”

Section: Comparison Studymentioning

confidence: 99%

Anomaly Detection and Mode Identification in Multimode Processes Using the Field Kalman Filter

Cong

Tan

Ottewill³

et al. 2021

IEEE Trans. Contr. Syst. Technol.

View full text Add to dashboard Cite

A process plant can have multiple modes of operation due to varying demand, availability of resources or the fundamental design of a process. Each of these modes is considered as normal operation. Anomalies in the process are characterised as deviations away from normal operation. Such anomalies can be indicative of developing faults which, if left unresolved, can lead to failures and unplanned downtime. The Field Kalman Filter (FKF) is a model-based approach, which is adopted in this paper for monitoring a multimode process. Previously, the FKF has been applied in process monitoring to differentiate normal operation from known faulty modes of operation. This paper extends the FKF so that it may detect occurrences of anomalies and differentiate them from the various normal modes of operation. A method is proposed for offline training an FKF monitoring model and on-line monitoring. The off-line part comprises training an FKF model based on Multivariate Autoregressive State-Space (MARSS) models fitted to historical process data. A monitoring indicator is also introduced. On-line monitoring, on the basis of the FKF for anomaly detection and mode identification, is demonstrated using a simulated multimode process. The performance of the proposed method is also demonstrated using data obtained from a pilotscale multiphase flow facility. The results show that the method can be applied successfully for anomaly detection and mode identification.

show abstract

“…Among the kernel-based methods, KPCA is a powerful technique, widely applied in process monitoring and fault diagnosis [1], [3], [39]- [41]. However, the commonly used Gaussian radial basis function (RBF) may suffer from overfitting problem, due to its lack of extrapolation ability, particularly while an inappropriate kernel width is selected [28], [42], [43]. The combination of RBF and polynomial kernels can provide enhanced modeling performance [44].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Incipient Fault Detection via Canonical Variate Dissimilarity and Mixed Kernel Principal Component Analysis

Ferrari

Liu

et al. 2021

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

Incipient fault detection plays a crucial role in preventing the occurrence of serious faults or failures in industrial processes. In most industrial processes, linear, and nonlinear relationships coexist. To improve fault detection performance, both linear and nonlinear features should be considered simultaneously. In this article, a novel hybrid linear-nonlinear statistical modeling approach for data-driven incipient fault detection is proposed by closely integrating recently developed canonical variate dissimilarity analysis and mixed kernel principal component analysis (MKPCA) using a serial model structure. Specifically, canonical variate analysis (CVA) is first applied to estimate the canonical variables (CVs) from the collected process data. Linear features are extracted from the estimated CVs. Then, the canonical variate dissimilarity (CVD) which quantifies model residuals in the CVA state-subspace is calculated using the estimated CVs. To explore the nonlinear features, the nonlinear principal components are extracted as nonlinear features through performing MKPCA on CVD. Fault detection indices are formed based on Hotelling's T 2 as well as Q statistics from the extracted linear and nonlinear features. Moreover, kernel density estimation is utilized to determine the control limits. The effectiveness of the proposed method is demonstrated by the comparisons with other relevant methods via simulations based on a closed-loop continuous stirred-tank reactor process. Index Terms-Canonical variate analysis (CVA), dissimilarity analysis, incipient fault detection, kernel principal component analysis (KPCA), mixed kernel.

show abstract

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Cited by 89 publications

References 322 publications

A Kernel Design Approach to Improve Kernel Subspace Identification

A Kernel Design Approach to Improve Kernel Subspace Identification

Anomaly Detection and Mode Identification in Multimode Processes Using the Field Kalman Filter

Data-Driven Incipient Fault Detection via Canonical Variate Dissimilarity and Mixed Kernel Principal Component Analysis

Contact Info

Product

Resources

About