Digital Signal Processing: A Computer-Based Approach
Digital Signal Processing: A Computer-Based Approach
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions
IEEE Transactions on Computers
Unsupervised Multiway Data Analysis: A Literature Survey
IEEE Transactions on Knowledge and Data Engineering
ACM Computing Surveys (CSUR)
Feature Extraction and Selection from Vibration Measurements for Structural Health Monitoring
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
Novelty detection in projected spaces for structural health monitoring
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Proceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems
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Structural health monitoring aims to detect damages in man-made engineering structures by monitoring changes in their vibration response. Unsupervised learning algorithms can be used to obtain a model of the undamaged condition and detect which new samples of the structure are not in agreement with it. However, in real structures with a sensor network configuration, the number of candidate features usually becomes large. Therefore, complexity increases and it is necessary to perform feature selection and/or dimensionality reduction to achieve good detection accuracy. In this paper, we propose to exploit the three-way structure of data and apply a true multi-way data analysis algorithm: Parallel Factor Analysis. A simple model is obtained and used to train novelty detectors. The methods are tested both with real and simulated structural data to assess that the three-way analysis can be successfully used in structural health monitoring. Furthermore, the usefulness of the approach for feature selection is also analyzed.