An improved stochastic subspace identification for operational modal analysis

Zhang, Guowen; Tang, Baoping; Tang, Guangwu

doi:10.1016/j.measurement.2012.01.012

Cited by 71 publications

(29 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hence, ensemble average autocorrelation [23]- [25] based stochastic subspace identification [18], [26]- [28] (EAAC-SSI) is employed to supress noise and determine the optimal band for demodulation analysis and the main steps of the method are shown in the first portion of Fig. 1.…”

Section: Auto-correlated Ensemble Average Based Stochastic Subspamentioning

confidence: 99%

Early detection of rolling bearing faults using an auto-correlated envelope ensemble average

Tang

et al. 2017

2017 23rd International Conference on Automation and Computing (ICAC)

View full text Add to dashboard Cite

Abstract-Bearings have been inevitably used in broad applications of rotating machines. To increase the efficiency, reliability and safety of machines, condition monitoring of bearings is significant during the operation. However, due to the influence of high background noise and bearing component slippages, incipient faults are difficult to detect. With the continuous research on the bearing system, the modulation effects have been well known and the demodulation based on optimal frequency bands is approved as a promising method in condition monitoring. For the purpose of enhancing the performance of demodulation analysis, a robust method, ensemble average autocorrelation based stochastic subspace identification (SSI), is introduced to determine the optimal frequency bands. Furthermore, considering that both the average and autocorrelation functions can reduce noise, auto-correlated envelope ensemble average (AEEA) is proposed to suppress noise and highlight the localised fault signature. In order to examine the performance of this method, the slippage of bearing signals is modelled as a Markov process in the simulation study. Based on the analysis results of simulated bearing fault signals with white noise and slippage and an experimental signal from a planetary gearbox test bench, the proposed method is robust to determine the optimal frequency bands, suppress noise and extract the fault characteristics.

show abstract

Section: Auto-correlated Ensemble Average Based Stochastic Subspamentioning

confidence: 99%

Early detection of rolling bearing faults using an auto-correlated envelope ensemble average

Tang

et al. 2017

2017 23rd International Conference on Automation and Computing (ICAC)

View full text Add to dashboard Cite

show abstract

“…The AR model requires an appropriate model order and algorithm to function properly; RDT functionality is limited by the number of the main modals contained in the structure [12]. SSI and NExT-ERA are problematic in terms of their spurious mode [13,14]. The Hilbert-Huang Transform (HHT) proposed in 1998 is an adaptive scheme well-suited to nonlinear, nonstationary time series analysis; however, the mode mixing effect [15] which emerges when dealing with signals over multifrequencies in each frequency band severely limits its application to flutter test data processing due to the inherent density of modal problems.…”

Section: Introductionmentioning

confidence: 99%

Flutter Test Data Processing Based on Improved Hilbert-Huang Transform

Zheng

Liu

Duan

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Flutter tests are conducted primarily for the purpose of modal parameter estimation and flutter boundary prediction, the accuracy of which is severely affected by the acquired data quality, structural modal density, and nonstationary conditions. An improved Hilbert-Huang Transform (HHT) algorithm is presented in this paper which mitigates the typical mode mixing effect via modulation. The algorithm is validated by theory, by numerical simulation, and per actual flight flutter test data. The results show that the proposed method could extract the flutter model parameters and predict the flutter speed more accurately, which is feasible for the current flutter test data processing.

show abstract

“…Regarding RDT, the accuracy of the technique might be affected when non-stationary signals are processed [22]. On the other hand, although SSI and NExT-ERA can deal better with noise-corrupted signals, the detection of closely-spaced modes usually requires selecting a higher order in the estimated model, which might generate false or spurious frequency components [23,24], making necessary the utilization of further stages to detect and separate the real frequencies from the fictitious ones.…”

Section: Introductionmentioning

confidence: 99%

A Two-Step Strategy for System Identification of Civil Structures for Structural Health Monitoring Using Wavelet Transform and Genetic Algorithms

et al. 2017

View full text Add to dashboard Cite

Nowadays, the accurate identification of natural frequencies and damping ratios play an important role in smart civil engineering, since they can be used for seismic design, vibration control, and condition assessment, among others. To achieve it in practical way, it is required to instrument the structure and apply techniques which are able to deal with noise-corrupted and non-linear signals, as they are common features in real-life civil structures. In this article, a two-step strategy is proposed for performing accurate modal parameters identification in an automated manner. In the first step, it is obtained and decomposed the measured signals using the natural excitation technique and the synchrosqueezed wavelet transform, respectively. Then, the second step estimates the modal parameters by solving an optimization problem employing a genetic algorithm-based approach, where the micropopulation concept is used to improve the speed convergence as well as the accuracy of the estimated values. The accuracy and effectiveness of the proposal are tested using both the simulated response of a benchmark structure and the measurements of a real eight-story building. The obtained results show that the proposed strategy can estimate the modal parameters accurately, indicating than the proposal can be considered as an alternative to perform the abovementioned task.

show abstract

An improved stochastic subspace identification for operational modal analysis

Cited by 71 publications

References 9 publications

Early detection of rolling bearing faults using an auto-correlated envelope ensemble average

Early detection of rolling bearing faults using an auto-correlated envelope ensemble average

Flutter Test Data Processing Based on Improved Hilbert-Huang Transform

A Two-Step Strategy for System Identification of Civil Structures for Structural Health Monitoring Using Wavelet Transform and Genetic Algorithms

Contact Info

Product

Resources

About