Data‐driven virtual sensing and dynamic strain estimation for fatigue analysis of offshore wind turbine using principal component analysis

<div> <div> <div> <p>Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H- pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (> 90% C) by incorporating 2,6-dimethyl- 4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development. </p> </div> </div> </div>

Download Full-text

Erratum to “Data‐driven dimension reduction in functional principal component analysis identifying the change‐point in functional data”

Statistical Analysis and Data Mining The ASA Data Science Journal ◽

10.1002/sam.11510 ◽

2021 ◽

Keyword(s):

Principal Component Analysis ◽

Dimension Reduction ◽

Functional Data ◽

Change Point ◽

Principal Component ◽

Component Analysis ◽

Functional Principal Component Analysis ◽

Data Driven ◽

Functional Principal Component

Download Full-text

Optimal sensor placement for parameter estimation and virtual sensing of strains on an offshore wind turbine considering sensor installation cost

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2021.108787 ◽

2022 ◽

Vol 169 ◽

pp. 108787

Author(s):

Azin Mehrjoo ◽

Mingming Song ◽

Babak Moaveni ◽

Costas Papadimitriou ◽

Eric Hines

Keyword(s):

Parameter Estimation ◽

Wind Turbine ◽

Sensor Placement ◽

Offshore Wind ◽

Offshore Wind Turbine ◽

Optimal Sensor Placement ◽

Virtual Sensing ◽

Installation Cost

Download Full-text

Physical-oriented and machine learning-based emission modeling in a diesel compression ignition engine: Dimensionality reduction and regression

International Journal of Engine Research ◽

10.1177/14680874211070736 ◽

2022 ◽

pp. 146808742110707

Author(s):

Aran Mohammad ◽

Reza Rezaei ◽

Christopher Hayduk ◽

Thaddaeus Delebinski ◽

Saeid Shahpouri ◽

...

Keyword(s):

Principal Component Analysis ◽

Support Vector Machine ◽

Factor Analysis ◽

Dimensionality Reduction ◽

Principal Component ◽

Component Analysis ◽

Data Driven ◽

Support Vector ◽

Emission Models ◽

Emission Modeling

The development of internal combustion engines is affected by the exhaust gas emissions legislation and the striving to increase performance. This demands for engine-out emission models that can be used for engine optimization for real driving emission controls. The prediction capability of physically and data-driven engine-out emission models is influenced by the system inputs, which are specified by the user and can lead to an improved accuracy with increasing number of inputs. Thereby the occurrence of irrelevant inputs becomes more probable, which have a low functional relation to the emissions and can lead to overfitting. Alternatively, data-driven methods can be used to detect irrelevant and redundant inputs. In this work, thermodynamic states are modeled based on 772 stationary measured test bench data from a commercial vehicle diesel engine. Afterward, 37 measured and modeled variables are led into a data-driven dimensionality reduction. For this purpose, approaches of supervised learning, such as lasso regression and linear support vector machine, and unsupervised learning methods like principal component analysis and factor analysis are applied to select and extract the relevant features. The selected and extracted features are used for regression by the support vector machine and the feedforward neural network to model the NOx, CO, HC, and soot emissions. This enables an evaluation of the modeling accuracy as a result of the dimensionality reduction. Using the methods in this work, the 37 variables are reduced to 25, 22, 11, and 16 inputs for NOx, CO, HC, and soot emission modeling while maintaining the accuracy. The features selected using the lasso algorithm provide more accurate learning of the regression models than the extracted features through principal component analysis and factor analysis. This results in test errors RMSETe for modeling NOx, CO, HC, and soot emissions 19.22 ppm, 6.46 ppm, 1.29 ppm, and 0.06 FSN, respectively.

Download Full-text

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

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2020.3029900 ◽

2020 ◽

pp. 1-1

Author(s):

Ping Wu ◽

Riccardo Ferrari ◽

Yichao Liu ◽

Jan-Willem van Wingerden

Keyword(s):

Principal Component Analysis ◽

Fault Detection ◽

Principal Component ◽

Component Analysis ◽

Data Driven ◽

Kernel Principal Component Analysis ◽

Canonical Variate ◽

Incipient Fault ◽

Incipient Fault Detection

Download Full-text

A Data-driven Study of RR Lyrae Near-IR Light Curves: Principal Component Analysis, Robust Fits, and Metallicity Estimates

The Astrophysical Journal ◽

10.3847/1538-4357/aab4fd ◽

2018 ◽

Vol 857 (1) ◽

pp. 55 ◽

Cited By ~ 10

Author(s):

Gergely Hajdu ◽

István Dékány ◽

Márcio Catelan ◽

Eva K. Grebel ◽

Johanna Jurcsik

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Light Curves ◽

Data Driven ◽

Rr Lyrae ◽

Near Ir ◽

Ir Light

Download Full-text

Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

Applied Sciences ◽

10.3390/app9040783 ◽

2019 ◽

Vol 9 (4) ◽

pp. 783 ◽

Cited By ~ 3

Author(s):

Silvio Simani ◽

Paolo Castaldi

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Operating Conditions ◽

Fault Detection And Isolation ◽

Offshore Wind ◽

Data Driven ◽

Offshore Wind Turbine ◽

Wind Turbine System ◽

High Degree ◽

Offshore Plants

Fault diagnosis of wind turbine systems is a challenging process, especially for offshore plants, and the search for solutions motivates the research discussed in this paper. In fact, these systems must have a high degree of reliability and availability to remain functional in specified operating conditions without needing expensive maintenance works. Especially for offshore plants, a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance. Therefore, this paper presents viable fault detection and isolation techniques applied to a wind turbine system. The design of the so-called fault indicator relies on an estimate of the fault using data-driven methods and effective tools for managing partial knowledge of system dynamics, as well as noise and disturbance effects. In particular, the suggested data-driven strategies exploit fuzzy systems and neural networks that are used to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, which approximate dynamic relations with arbitrary accuracy. The designed fault diagnosis schemes were verified and validated using a high-fidelity simulator that describes the normal and faulty behavior of a realistic offshore wind turbine plant. Finally, by accounting for the uncertainty and disturbance in the wind turbine simulator, a hardware-in-the-loop test rig was used to assess the proposed methods for robustness and reliability. These aspects are fundamental when the developed fault diagnosis methods are applied to real offshore wind turbines.

Download Full-text

Damage detection in nonlinear civil structures using kernel principal component analysis

Advances in Structural Engineering ◽

10.1177/1369433220913207 ◽

2020 ◽

Vol 23 (11) ◽

pp. 2414-2430

Author(s):

Khaoula Ghoulem ◽

Tarek Kormi ◽

Nizar Bel Hadj Ali

Keyword(s):

Principal Component Analysis ◽

Damage Detection ◽

Structural Model ◽

Principal Component ◽

Original Data ◽

Component Analysis ◽

Correlated Data ◽

Data Driven ◽

Kernel Principal Component Analysis ◽

Structural Systems

In the general framework of data-driven structural health monitoring, principal component analysis has been applied successfully in continuous monitoring of complex civil infrastructures. In the case of linear or polynomial relationship between monitored variables, principal component analysis allows generation of structured residuals from measurement outputs without a priori structural model. The principal component analysis has been widely used for system monitoring based on its ability to handle high-dimensional, noisy, and highly correlated data by projecting the data onto a lower dimensional subspace that contains most of the variance of the original data. However, for nonlinear systems, it could be easily demonstrated that linear principal component analysis is unable to disclose nonlinear relationships between variables. This has naturally motivated various developments of nonlinear principal component analysis to tackle damage diagnosis of complex structural systems, especially those characterized by a nonlinear behavior. In this article, a data-driven technique for damage detection in nonlinear structural systems is presented. The proposed method is based on kernel principal component analysis. Two case studies involving nonlinear cable structures are presented to show the effectiveness of the proposed methodology. The validity of the kernel principal component analysis–based monitoring technique is shown in terms of the ability to damage detection. Robustness to environmental effects and disturbances are also studied.

Download Full-text