Abnormal state detection of building structures based on symbolic time series analysis and negative selection

Li, Rongshuai; Mita, Akira; Zhou, Jin

doi:10.1002/stc.1555

Cited by 3 publications

(2 citation statements)

References 21 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Partitioning of the random variables in the PKLD method is based upon maximum entropy approach. 31 To implement this process, the residual vector of the current state ( ê uL ) is arranged in ascending order in such a way that it begins with the minimum residual sequence and ends with the maximum one. Accordingly, the arranged vector ê uL is divided into c partitions H 1 , H 2 , … , H c through the maximum entropy approach in the following form 31…”

Section: Pkld Methodsmentioning

confidence: 99%

Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods

Entezami

Shariatmadar

Karamodin

2018

Structural Health Monitoring

View full text Add to dashboard Cite

Feature extraction by time-series analysis and decision making through distance-based methods are powerful and efficient statistical pattern recognition techniques for data-driven structural health monitoring. The motivation of this article is to propose an innovative residual-based feature extraction approach based on AutoRegressive modeling and a novel statistical distance method named as Partition-based Kullback–Leibler Divergence for damage detection and localization by using randomly high-dimensional damage-sensitive features under environmental and operational variability. The key novel element of the proposed feature extraction approach is to establish a two-stage offline and online learning algorithms for extracting the residuals of AutoRegressive model as the main damage-sensitive features. This technique brings the great benefit of reducing the computational time and storage space for feature extraction in long-term monitoring conditions. The major contribution of Partition-based Kullback–Leibler Divergence method is to exploit a partitioning strategy for dividing random features into individual partitions and utilize numerical information of partitioning in distance calculation rather than directly applying random samples. Dealing with the major challenging issue of using the high-dimensional features in decision making and applicability to both correlated and uncorrelated random datasets are the main advantages of Partition-based Kullback–Leibler Divergence method. The accuracy and reliability of the proposed approaches are experimentally validated by two well-known benchmark structures. The stationarity and linearity of measured vibration responses for using in AutoRegressive modeling are evaluated by two hypothesis tests. Comparative studies are also conducted to demonstrate the superiority of the proposed methods over some exciting state-of-the-art techniques. Results show that the methods presented here succeed in detecting and locating damage and make time-saving and efficient tools for feature extraction and damage diagnosis.

show abstract

Section: Pkld Methodsmentioning

confidence: 99%

Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods

Entezami

Shariatmadar

Karamodin

2018

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…Symbolic time series analysis (STSA) aims at symbolizing the time series data. The basic process of STSA is converting the original time series signals into sequences of discrete symbols via partition function, and then we can use statistical features of the symbols to describe the dynamic statuses of a system [26]. STSA includes the symbolization of original time series and the quantitative analysis of symbolic series.…”

Section: Symbolic Entropymentioning

confidence: 99%

Bearing Performance Degradation Assessment Using Lifting Wavelet Packet Symbolic Entropy and SVDD

Zhang

Guo

Zhang

et al. 2016

Shock and Vibration

View full text Add to dashboard Cite

Bearing performance degradation assessment is of great significance for proactive maintenance and near-zero downtime. For this purpose, a novel assessment method is proposed based on lifting wavelet packet symbolic entropy (LWPSE) and support vector data description (SVDD). LWPSE is presented for feature extraction by jointing use of lifting wavelet packet transform and symbolic entropy. Firstly, the LWPSEs of bearing signals from normal bearing condition are extracted to train an SVDD model by fitting a tight hypersphere around normal samples. Then, the relative distance from the LWPSEs of testing signals to the hypersphere boundary is calculated as a quantitative index for bearing performance degradation assessment. The feasibility and efficiency of the proposed method were validated by the life-cycle data obtained from NASA’s prognostics data repository and the comparison with Hidden Markov Model (HMM). Finally, the assessment results were verified by the envelope spectrum analysis method based on empirical mode decomposition and Hilbert envelope demodulation.

show abstract

Deterministically generated negative selection algorithm for damage detection in civil engineering systems

Barontini

Perera

Masciotta

et al. 2019

Engineering Structures

View full text Add to dashboard Cite

Abnormal state detection of building structures based on symbolic time series analysis and negative selection

Cited by 3 publications

References 21 publications

Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods

Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods

Bearing Performance Degradation Assessment Using Lifting Wavelet Packet Symbolic Entropy and SVDD

Deterministically generated negative selection algorithm for damage detection in civil engineering systems

Contact Info

Product

Resources

About