Fully automated precise operational modal identification

He, Min; Li, Jun; Zhang, Yang; Liu, Yongjian

doi:10.1016/j.engstruct.2021.111988

Cited by 37 publications

(18 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is safer to over-specify the model order to make sure that all physical modes are extracted. However, this also leads to the inclusion of mathematical (spurious) modes in the final set of identified parameters [8,76]. Automatic modal parameter identification studies are carried out to separate the mathematical (spurious) modes from the physical ones, thereby clearing the stabilization diagram [16,77].…”

Section: Validation Criteriamentioning

confidence: 99%

“…According to [76], the criteria for the separation of poles in stabilization diagrams can be divided into hard criteria, soft criteria, and modal uncertainty. The hard validation criteria give strict numerical values that the identified entities must satisfy.…”

Section: Validation Criteriamentioning

confidence: 99%

“…When exciting a structure with a force other than the perfect white noise, the identified modal parameters inevitably contain an uncertainty. This is called the modal uncertainty and represents the scatter of identified parameters [76]. These criteria work by taking into account the relatively large scatter of values of spurious modal data compared with the data of physical poles.…”

Section: Validation Criteriamentioning

confidence: 99%

See 2 more Smart Citations

Statistical Structural Integrity Control of Composite Structures Based on an Automatic Operational Modal Analysis — a Review

2022

View full text Add to dashboard Cite

The operational modal analysis (OMA), as a passive technique, has found a practical application in the structural health monitoring (SHM) of structures in service subjected to dynamic loadings. The state of structural integrity is judged by exploring the changes in values of the modal parameters estimated by the OMA. However, the entire framework of on-line damage identification and continuous monitoring is rather complex, involving several key building blocks. This work reviews the main steps in achieving the functionality of automatic damage identification in composite structures with a particular focus on wind turbine blades. The sensor instrumentation, extraction of damage-sensitive features from measured response signals, removal of environmental influence, automatic classification of physical and spurious modes of the system, and application of a statistical control to obtain the information on the possible structural damage are discussed. The merits and limitations of the OMA-based SHM approach for composite wind turbine blades are indicated.

show abstract

Section: Validation Criteriamentioning

confidence: 99%

Section: Validation Criteriamentioning

confidence: 99%

Section: Validation Criteriamentioning

confidence: 99%

See 1 more Smart Citation

Statistical Structural Integrity Control of Composite Structures Based on an Automatic Operational Modal Analysis — a Review

2022

View full text Add to dashboard Cite

show abstract

“…17 Very recently, Cheema et al 18 applied a Dirichlet process Gaussian mixture model (DP-GMM) clustering approach to discern true physical modes from the mathematically spurious modes on a cable-stayed bridge in New South Wales, Australia. He et al 19 applied a modified version of fuzzy C-means (FCM) clustering to the Fourth Nanjing Yangtze River Bridge. Zeng and Hoon Kim 20 tested a self-adaptive clustering approach with a weighted multi-term distance on the Z24 and Downing Hall benchmarks.…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based automatic operational modal analysis: A structural health monitoring application to masonry arch bridges

Civera

Mugnaini

Fragonara

2022

Structural Contr & Hlth

View full text Add to dashboard Cite

Structural health monitoring (SHM) is one of the main research topics in civil, mechanical and aerospace engineering. In this regard, modal parameters and their trends over time can be used as features and indicators of damage occurrence and growth. However, for practical reasons, output-only techniques are particularly suitable for the system identification (SI) of large civil structures and infrastructures, as they do not require a controlled source of input force.In this context, these approaches are typically referred to as operational modal analysis (OMA) techniques. However, the interpretation of the OMA identifications is a labour-intensive task, which could be better automated with artificial intelligence and machine learning (ML) techniques. In particular, clustering and cluster analysis can be used to group unlabelled datasets and interpret them. In this study, a novel multi-stage clustering algorithm for automatic OMA (AOMA) is tested and validated for SHM applications-specifically, for damage detection and severity assessment-to a masonry arch bridge. The experimental case study involves a 1:2 scaled model, progressively damaged to simulate foundation scouring at the central pier.

show abstract

“…24 He et al proposed a modified fuzzy C-means clustering algorithm with an iterative graph partitioning method, which can be used to identify the closely spaced modes. 25 Mao et al applied the principal component analysis to the automated identification to reduce the noise components and proposed a flexible method to determine the cutoff numbers for the hierarchical clustering. 26 Zeng et al defined a novel distance with the uncertainty of modal parameters and proposed an improved self-adaptive clustering based on the weighted distance, which effectively enhanced the robustness of the identification process.…”

Section: Introductionmentioning

confidence: 99%

A single‐mode recursive validation method for modal identification of linear time‐varying structures based on prior knowledge

Yue

Liu

2021

Struct Control Health Monit

View full text Add to dashboard Cite

Discerning the spurious modes for time-invariant or extremely slow timevarying structures with time-consuming tools (stabilization diagram and clustering), which is the main task of the automated modal identification, has been developed in last years. However, there is still a challenge the recursive identification of the linear time-varying structures. This study presents a single-mode recursive validation method for the recursive identification of the linear timevarying structures. Since one major issue is that the properties of the physical modes are time-varying, the proposed method extracts the information about the current state of the time-varying structures from the nonstationary vibration responses via deep learning method for the separation between physical and spurious modes. For the sake of effective elimination to spurious modes and control of computation efforts, the proposed method utilizes the application-dependent prior knowledge rather than iterations or highdimensional optimizations, with robustness to hyperparameters. It can be combined with any parametric system identification method. A time-varying stiffness numerical example and a time-varying mass distribution experimental example illustrate the performance of the proposed modal validation method under various time-varying processes.

show abstract

Fully automated precise operational modal identification

Cited by 37 publications

References 6 publications

Statistical Structural Integrity Control of Composite Structures Based on an Automatic Operational Modal Analysis — a Review

Statistical Structural Integrity Control of Composite Structures Based on an Automatic Operational Modal Analysis — a Review

Machine learning‐based automatic operational modal analysis: A structural health monitoring application to masonry arch bridges

A single‐mode recursive validation method for modal identification of linear time‐varying structures based on prior knowledge

Contact Info

Product

Resources

About