### Abstract

Data loss is a critical problem in structural health monitoring (SHM). Probability distributions play a highly important role in many applications. Improving the quality of distribution estimations made using incomplete samples is highly important. Missing samples can be compensated for by applying conventional missing data restoration methods; however, ensuring that restored samples roughly follow underlying distributions of true missing data remains a challenge. Another strategy involves directly restoring the probability density function (PDF) for a sensor when samples are missing by leveraging distribution information from another sensor with complete data using distribution regression techniques; existing methods include the conventional distribution-to-distribution regression (DDR) and distribution-to-warping function regression (DWR) methods. Due to constraints on PDFs and warping functions, the regression functions of both methods are estimated from the Nadaraya-Watson kernel estimator (a local linear smoothing technique) with relatively low degrees of precision. This article proposes a new indirect distribution-to-distribution regression approach to restoring distributions of missing SHM data via functional data analysis. PDFs are represented by ordinary functions after applying log-quantile-density (LQD) transformation. The representation function of the missing distribution is first restored from a functional regression model constructed in Reproducing Kernel Hilbert Space (RKHS) by solving an optimization problem and subsequently mapping back to the density space through inverse log-quantile-density transformation. The performance of the proposed approach is evaluated through application to field monitoring data. Test results indicate that the new method significantly outperforms conventional methods in general cases; however, in extrapolation cases, the new method is inferior to the distribution-to-warping function regression method.

Original language | English (US) |
---|---|

Pages (from-to) | 655-674 |

Number of pages | 20 |

Journal | Mechanical Systems and Signal Processing |

Volume | 121 |

DOIs | |

State | Published - Apr 15 2019 |

### Fingerprint

### Keywords

- Distribution-to-distribution regression
- Functional data analysis
- Log-quantile-density transformation
- Missing data
- Probability distribution
- Reproducing Kernel Hilbert Space
- Structural health monitoring

### ASJC Scopus subject areas

- Control and Systems Engineering
- Signal Processing
- Civil and Structural Engineering
- Aerospace Engineering
- Mechanical Engineering
- Computer Science Applications

### Cite this

**LQD-RKHS-based distribution-to-distribution regression methodology for restoring the probability distributions of missing SHM data.** / Chen, Zhicheng; Bao, Yuequan; Li, Hui; Spencer, B F.

Research output: Contribution to journal › Article

*Mechanical Systems and Signal Processing*, vol. 121, pp. 655-674. https://doi.org/10.1016/j.ymssp.2018.11.052

}

TY - JOUR

T1 - LQD-RKHS-based distribution-to-distribution regression methodology for restoring the probability distributions of missing SHM data

AU - Chen, Zhicheng

AU - Bao, Yuequan

AU - Li, Hui

AU - Spencer, B F

PY - 2019/4/15

Y1 - 2019/4/15

N2 - Data loss is a critical problem in structural health monitoring (SHM). Probability distributions play a highly important role in many applications. Improving the quality of distribution estimations made using incomplete samples is highly important. Missing samples can be compensated for by applying conventional missing data restoration methods; however, ensuring that restored samples roughly follow underlying distributions of true missing data remains a challenge. Another strategy involves directly restoring the probability density function (PDF) for a sensor when samples are missing by leveraging distribution information from another sensor with complete data using distribution regression techniques; existing methods include the conventional distribution-to-distribution regression (DDR) and distribution-to-warping function regression (DWR) methods. Due to constraints on PDFs and warping functions, the regression functions of both methods are estimated from the Nadaraya-Watson kernel estimator (a local linear smoothing technique) with relatively low degrees of precision. This article proposes a new indirect distribution-to-distribution regression approach to restoring distributions of missing SHM data via functional data analysis. PDFs are represented by ordinary functions after applying log-quantile-density (LQD) transformation. The representation function of the missing distribution is first restored from a functional regression model constructed in Reproducing Kernel Hilbert Space (RKHS) by solving an optimization problem and subsequently mapping back to the density space through inverse log-quantile-density transformation. The performance of the proposed approach is evaluated through application to field monitoring data. Test results indicate that the new method significantly outperforms conventional methods in general cases; however, in extrapolation cases, the new method is inferior to the distribution-to-warping function regression method.

AB - Data loss is a critical problem in structural health monitoring (SHM). Probability distributions play a highly important role in many applications. Improving the quality of distribution estimations made using incomplete samples is highly important. Missing samples can be compensated for by applying conventional missing data restoration methods; however, ensuring that restored samples roughly follow underlying distributions of true missing data remains a challenge. Another strategy involves directly restoring the probability density function (PDF) for a sensor when samples are missing by leveraging distribution information from another sensor with complete data using distribution regression techniques; existing methods include the conventional distribution-to-distribution regression (DDR) and distribution-to-warping function regression (DWR) methods. Due to constraints on PDFs and warping functions, the regression functions of both methods are estimated from the Nadaraya-Watson kernel estimator (a local linear smoothing technique) with relatively low degrees of precision. This article proposes a new indirect distribution-to-distribution regression approach to restoring distributions of missing SHM data via functional data analysis. PDFs are represented by ordinary functions after applying log-quantile-density (LQD) transformation. The representation function of the missing distribution is first restored from a functional regression model constructed in Reproducing Kernel Hilbert Space (RKHS) by solving an optimization problem and subsequently mapping back to the density space through inverse log-quantile-density transformation. The performance of the proposed approach is evaluated through application to field monitoring data. Test results indicate that the new method significantly outperforms conventional methods in general cases; however, in extrapolation cases, the new method is inferior to the distribution-to-warping function regression method.

KW - Distribution-to-distribution regression

KW - Functional data analysis

KW - Log-quantile-density transformation

KW - Missing data

KW - Probability distribution

KW - Reproducing Kernel Hilbert Space

KW - Structural health monitoring

UR - http://www.scopus.com/inward/record.url?scp=85057782975&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85057782975&partnerID=8YFLogxK

U2 - 10.1016/j.ymssp.2018.11.052

DO - 10.1016/j.ymssp.2018.11.052

M3 - Article

AN - SCOPUS:85057782975

VL - 121

SP - 655

EP - 674

JO - Mechanical Systems and Signal Processing

JF - Mechanical Systems and Signal Processing

SN - 0888-3270

ER -