Visible to the public A Robust Functional-Data-Analysis Method for Data Recovery in Multichannel Sensor Systems

TitleA Robust Functional-Data-Analysis Method for Data Recovery in Multichannel Sensor Systems
Publication TypeJournal Article
Year of Publication2014
AuthorsJian Sun, Haitao Liao, Upadhyaya, B.R.
JournalCybernetics, IEEE Transactions on
Date PublishedAug
Keywordsasynchronous data sampling, Bandwidth, condition monitoring, coolant-flow loop, correlated signals, correlation methods, critical equipment, Data analysis, Data models, data recovery, Eigenvalues and eigenfunctions, experimental flow-control loop, failure prevention, fault diagnosis, fault prognosis, FPCA, functional data analysis, functional principal component analysis, grand median functions, integrated systems, limited data transmission, multichannel sensor systems, multimodular integral pressurized water reactor, multivariate functional regression, nonskewed signal recovery, Predictive models, principal component analysis, regression analysis, reliability enhancement, robust data-recovery method, robust functional-data-analysis method, Robustness, sensor fusion, sensor readings, sensor signal smoothing, Sensor systems, skewed distributions, smoothing methods, statistical distributions, strongly skewed signal recovery, turbofan engine data

Multichannel sensor systems are widely used in condition monitoring for effective failure prevention of critical equipment or processes. However, loss of sensor readings due to malfunctions of sensors and/or communication has long been a hurdle to reliable operations of such integrated systems. Moreover, asynchronous data sampling and/or limited data transmission are usually seen in multiple sensor channels. To reliably perform fault diagnosis and prognosis in such operating environments, a data recovery method based on functional principal component analysis (FPCA) can be utilized. However, traditional FPCA methods are not robust to outliers and their capabilities are limited in recovering signals with strongly skewed distributions (i.e., lack of symmetry). This paper provides a robust data-recovery method based on functional data analysis to enhance the reliability of multichannel sensor systems. The method not only considers the possibly skewed distribution of each channel of signal trajectories, but is also capable of recovering missing data for both individual and correlated sensor channels with asynchronous data that may be sparse as well. In particular, grand median functions, rather than classical grand mean functions, are utilized for robust smoothing of sensor signals. Furthermore, the relationship between the functional scores of two correlated signals is modeled using multivariate functional regression to enhance the overall data-recovery capability. An experimental flow-control loop that mimics the operation of coolant-flow loop in a multimodular integral pressurized water reactor is used to demonstrate the effectiveness and adaptability of the proposed data-recovery method. The computational results illustrate that the proposed method is robust to outliers and more capable than the existing FPCA-based method in terms of the accuracy in recovering strongly skewed signals. In addition, turbofan engine data are also analyzed to verify the capability of the proposed method in recovering non-skewed signals.

Citation Key6670785