Structural damage detection using principal component analysis of frequency response function data

Esfandiari, Akbar; Nabiyan, Mansureh-Sadat; Rofooei, Fayaz R.

doi:10.1002/stc.2550

Cited by 31 publications

(24 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike modal data, Frequency Response Functions (FRFs) are obtained over a wide range of frequencies, providing more information about damage, and have been widely used as input in optimisation-based model-updating problems [ 187 , 188 ]. Nevertheless, FRFs have also been utilised to obtain damage sensitive features in damage detection problems.…”

Section: Shm Of Composite Structuresmentioning

confidence: 99%

Structural Health Monitoring in Composite Structures: A Comprehensive Review

Hassani

Mousavi

Gandomi

2021

Sensors

102

View full text Add to dashboard Cite

This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented.

show abstract

Section: Shm Of Composite Structuresmentioning

confidence: 99%

Structural Health Monitoring in Composite Structures: A Comprehensive Review

Hassani

Mousavi

Gandomi

2021

Sensors

102

View full text Add to dashboard Cite

show abstract

“…The recorded data at the four nodes are concatenated and taken as an input. PCA is performed on input variables to reduce the dimensionality since some of them can be irrelevant 2,26 . It transforms the dataset into compressed form while retaining the essence of the original dataset, 32 resulting in a fewer uncorrelated variables 10 .…”

Section: Numerical Studiesmentioning

confidence: 99%

“…Structures have a finite life span. Processes such as corrosion, overloads, wears and erosion would deteriorate structures with time until they cannot be used 1,2 . The deficiencies in these structures may lead to catastrophic consequences associated with substantial economic losses, casualties and disruptions in the daily activities if proper monitoring is not carried out 3 .…”

Section: Introductionmentioning

confidence: 99%

Development and application of random forest technique for element level structural damage quantification

Chencho

Hao

et al. 2020

Struct Control Health Monit

View full text Add to dashboard Cite

Summary This paper presents element level structural damage quantification using an ensemble‐based machine learning technique, namely, random forest technique, with acceleration responses from structures. The ensemble‐based approach provides a better prediction than an individual model. Random forest is a machine learning algorithm which has several decision trees to perform a task. The proposed approach develops a random forest as a regressor to predict multiple output variables, which is the vector of elemental level damage quantification results of the structure. Damage severity is identified as the reduction in the elemental stiffness parameters. The acceleration responses for single‐element and multiple element damage cases are generated and further processed to feed as input to the random forest. The acceleration responses from the sensor nodes are concatenated, and principal component analysis (PCA) is applied to reduce the uncorrelated input dimension. The proposed approach can provide good damage identification results efficiently with much less computational demand and time, compared to the neural network training methods and deep learning models. Moreover, fewer sensors are needed to measure acceleration responses to localise damage and quantify the damage level. To demonstrate the proposed method, a simply supported beam is used as an example in numerical studies. Different levels of noise in the acceleration responses and uncertainty in the finite element modelling are considered. Experimental studies on a steel frame structure with 70 elements are also conducted to investigate the performance of the proposed approach for structural damage quantification. Good identification results are obtained with an efficient training process.

show abstract

“…Structural inspection [1], structural damage detection [2] or structural health monitoring [3] are roughly equivalent terms used for fault diagnosis of mechanical structures. Structural inspection ideally answers the following three questions: (i) Is there any faults?…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Analysis of Resonance Frequencies for Structural Inspection of an Engine Cylinder Block

Mohammadzaheri

Amouzadeh

Doustmohammadi

et al. 2021

Fuzzy Information and Engineering

View full text Add to dashboard Cite

A new inspection technique for complex mechanical structures is proposed in this paper, where a fuzzy inference system carries out structural inspection. The inputs to the fuzzy inference system are the elements of a fault signature, an array of numbers prepared with use of below 5 kHz resonance frequencies of faultless and a number of faulty specimens.Advantage: Below 5 kHz resonance frequencies are easier and less expensive to obtain compared to higher frequency ones.Limit: Due to high expenses of experiments, reliable finite element models were alternatively used to obtain resonance frequencies of the faulty specimens.Results: The developed fuzzy inference system in this research accurately located an under-surface fault in an engine cylinder block.

show abstract

Structural damage detection using principal component analysis of frequency response function data

Cited by 31 publications

References 48 publications

Structural Health Monitoring in Composite Structures: A Comprehensive Review

Structural Health Monitoring in Composite Structures: A Comprehensive Review

Development and application of random forest technique for element level structural damage quantification

Fuzzy Analysis of Resonance Frequencies for Structural Inspection of an Engine Cylinder Block

Contact Info

Product

Resources

About