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Probabilistic principal component analysis‐based anomaly detection for structures with missing data
Author(s) -
Ma Zhi,
Yun ChungBang,
Wan HuaPing,
Shen Yanbin,
Yu Feng,
Luo Yaozhi
Publication year - 2021
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2698
Subject(s) - outlier , anomaly detection , probabilistic logic , principal component analysis , structural health monitoring , missing data , data mining , anomaly (physics) , computer science , robust principal component analysis , reliability engineering , engineering , artificial intelligence , machine learning , structural engineering , physics , condensed matter physics
Summary Structures are subjected to various kinds of structural deterioration and damage with use over long periods of service life. For the safety assurance of structures, it is very important to have a long‐term monitoring system and continuous assessments of structural integrity using the measured data. The objective of this paper is to develop an anomaly detection algorithm for a long‐term structural health monitoring (SHM) system based on probabilistic principal component analysis (PPCA). Static stress data were measured and used in this monitoring system. A baseline PPCA model is built under various environmental loading conditions. Then, newly monitored data are projected onto the principal vectors. Anomaly indices and their probability distributions are evaluated to determine the presence of structural damage indicated by outliers. This method is also capable of dealing with incomplete data and recovering the missing data. First, numerical simulation studies of a revolving auditorium are carried out to validate the proposed PPCA‐based method. Then, real monitoring data collected from the SHM system are used to detect the presence and locations of anomalies in the revolving auditorium.