Autoregressive statistical pattern recognition algorithms for damage detection in civil structures

Yao, Ruigen; Pakzad, Shamim N.

doi:10.1016/j.ymssp.2012.02.014

Cited by 159 publications

(98 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, it is important to note that their damage sensitivity arises from the assumption of stationarity, which allows for the detection of nonlinear behaviour in the vibration data by inducing changes in the residual errors and/or model parameters [161]. Residual errors are assumed to be Gaussian and are assessed using Gaussian-based outlier detection techniques such as MSD [162,163] or Gaussian mixture models [164]. Multiple sensor locations may be used for the purposes of damage localisation; however, prior to fitting the models, the sensor data must be normalised in order to remove variable external condition influences such as traffic and temperature, so that all signals have similar statistical characteristics.…”

Section: Time Series Based Damage Sensitive Featuresmentioning

confidence: 99%

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

2017

View full text Add to dashboard Cite

Abstract:Traditionally, damage identification techniques in bridges have focused on monitoring changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship with structural stiffness and their spatial information content. However, their progression to real-world applications has not been without its challenges and shortcomings, mainly stemming from: (1) environmental and operational variations; (2) inefficient utilization of machine learning algorithms for damage detection; and (3) a general over-reliance on modal-based DSFs alone. The present paper provides an in-depth review of the development of modal-based DSFs and a synopsis of the challenges they face. The paper then sets out to addresses the highlighted challenges in terms of published advancements and alternatives from recent literature.

show abstract

Section: Time Series Based Damage Sensitive Featuresmentioning

confidence: 99%

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

2017

View full text Add to dashboard Cite

show abstract

“…Here we propose a new method for surge detection in CFD; it is based on autoregressive (AR) statistical pattern recognition algorithms [16] by monitoring model residual variances. As this paper was first written, we found a similar approach which was employed for early surge warning in axial compressors [17].…”

Section: Autoregressive Modelmentioning

confidence: 99%

Multipoint and Multiobjective Optimization of a Centrifugal Compressor Impeller Based on Genetic Algorithm

Liu

Lin

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The design of high efficiency, high pressure ratio, and wide flow range centrifugal impellers is a challenging task. The paper describes the application of a multiobjective, multipoint optimization methodology to the redesign of a transonic compressor impeller for this purpose. The aerodynamic optimization method integrates an improved nondominated sorting genetic algorithm II (NSGA-II), blade geometry parameterization based on NURBS, a 3D RANS solver, a self-organization map (SOM) based data mining technique, and a time series based surge detection method. The optimization results indicate a considerable improvement to the total pressure ratio and isentropic efficiency of the compressor over the whole design speed line and by 5.3% and 1.9% at design point, respectively. Meanwhile, surge margin and choke mass flow increase by 6.8% and 1.4%, respectively. The mechanism behind the performance improvement is further extracted by combining the geometry changes with detailed flow analysis.

show abstract

“…Novelty detection [1,5,6] and clustering analysis [7,8] are well-known unsupervised learning techniques for early damage detection.…”

Section: Introductionmentioning

confidence: 99%

An improvement on feature extraction via time series modeling for structural health monitoring based on unsupervised learning methods

2018

View full text Add to dashboard Cite

Autoregressive statistical pattern recognition algorithms for damage detection in civil structures

Cited by 159 publications

References 28 publications

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

Multipoint and Multiobjective Optimization of a Centrifugal Compressor Impeller Based on Genetic Algorithm

An improvement on feature extraction via time series modeling for structural health monitoring based on unsupervised learning methods

Contact Info

Product

Resources

About