An innovative approach for conducting experimental modal analysis (EMA) in running harmonic for structural modal identification

Jannifar, A.; Zubir, Mohd Nashrul Mohd; Kazi, S.N.

doi:10.1016/j.measurement.2020.107795

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…Thus, the binary classification of rim technical condition as fit to use or damaged was proposed. The technical condition of the automotive rim can be examined using non-destructive vibration analysis performed in laboratory conditions [39]. Suggestions to improve the analysis include modifying the measuring system using a wheel balancer to a rim condition measurement system that supports run-out and vibration modes at four rim points and recording them in a dedicated database.…”

Section: Introductionmentioning

confidence: 99%

Steel Automotive Wheel Rims—Data Fusion for the Precise Identification of the Technical Condition and Indication of the Approaching End of Service Life

Borecki,

Rychlik,

Zan

et al. 2024

Materials

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Steel automotive wheel rims are subject to wear and tear, down to the end of their service life. Manufacturers use standard destructive tests to determine the probable lifetime of the car wheel rim. With this approach, to predict the remaining use time, it is necessary to know the initial parameters of the wheel rim, actual mileage, and its use characteristics, which is difficult information to obtain in the real world. Moreover, this work shows that a vehicle’s technical condition can affect the rim’s remaining service time. This work describes a new method of precise binary identification of the technical condition of steel car wheel rims using the dispersion of damping factors which result from experimental modal analysis. This work also proposes a new method of indicating the approaching end of wheel rim service life with limited parameters: run-out, average of damping factors, and dispersion of damping factors. The proposed procedure requires two sequential examinations of the rim in standard periods related to the average annual mileage of the vehicle. On this basis, it is possible to indicate the approaching end of the life of the steel rims about 10,000 km in advance.

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Section: Introductionmentioning

confidence: 99%

Steel Automotive Wheel Rims—Data Fusion for the Precise Identification of the Technical Condition and Indication of the Approaching End of Service Life

Borecki,

Rychlik,

Zan

et al. 2024

Materials

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“…These methods can be classified into two types. The first type is based on the short-term time-invariant assumption [6][7][8], in which TV systems are analyzed. These methods utilize various signal processing tools to identify parameters within a short-term interval, achieving the purpose of identification through overall fitting.…”

Section: Introductionmentioning

confidence: 99%

Kernel Regression Residual Decomposition-Based Polynomial Frequency Modulation Integral Algorithm to Identify Physical Parameters of Time-Varying Systems under Random Excitation

Liu

Shi

2023

Applied Sciences

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The physical parameters (stiffness, damping) of time-varying (TV) systems under random excitation provide valuable information for their working condition but they are often overwhelmed by noise interference. To overcome this problem, this paper presents a novel multi-level kernel regression residual decomposition method, which can not only effectively separate each modal component from the raw vibration acceleration signal, but also eliminate noise interference. Additionally, the multiple degree-of-freedom (DOF) parameter identification problem is transformed into a single DOF parameter identification problem. Combined with the derived polynomial frequency modulation integral algorithm and the cross-correlation theory based on the fractional Fourier ambiguity function, a physical parameter identification method is proposed. The method provides a new idea in modeling TV systems and identifying physical parameters under random excitation. To demonstrate the effectiveness of the proposed method, numerical simulations are conducted with three different cases of variation (variation, quadratic variation, and periodic variation) in time. Moreover, its robustness is evaluated by adding different signal-to-noise ratio levels of noise (20 dB, 50 dB, 100 dB) to the input vibration acceleration signal. The analysis results confirm the performance of the proposed method for the parameter identification of TV systems under random excitation.

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“…In Operational modal analysis (OMA), the input is unknown and is usually modeled as a broadband stochastic process. So, the identification process is more complex when comparing with experimental modal analysis (Jannifar et al, 2020; Ondra and Titurus, 2019). Therefore, a reasonable method must be used to extract modal information from random response data without load information.…”

Section: Introductionmentioning

confidence: 99%

Operational modal parameter identification with correlated colored noise excitation

Lǚ

Chen

2021

Journal of Vibration and Control

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Operational modal analysis refers to the modal analysis of a structure in its operating state. The advantage of operational modal analysis is that only the output vibration signal of a system is used. The classical operational modal analysis algorithm is based on the white noise excitation assumption, and it is considered that there is no correlation between the excitations; several identification methods have been developed in time and frequency domains. But excitations are not completely independent with each other and not pure white. In this article, the matrix theory is used to prove that the operational modal analysis algorithm can still be used to identify modal parameters when the excitation is correlated. In the simulation, five kinds of colored noise excitations are applied to the cantilever beam with correlated excitations, which shows that the idea proposed in this article is rational. In the experiment, the foundation excitation of colored noise is added to the cantilever beam, which can be regarded as applying several related excitations. It also shows the rationality of this idea.

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An innovative approach for conducting experimental modal analysis (EMA) in running harmonic for structural modal identification

Cited by 9 publications

References 29 publications

Steel Automotive Wheel Rims—Data Fusion for the Precise Identification of the Technical Condition and Indication of the Approaching End of Service Life

Steel Automotive Wheel Rims—Data Fusion for the Precise Identification of the Technical Condition and Indication of the Approaching End of Service Life

Kernel Regression Residual Decomposition-Based Polynomial Frequency Modulation Integral Algorithm to Identify Physical Parameters of Time-Varying Systems under Random Excitation

Operational modal parameter identification with correlated colored noise excitation

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