Chatter prediction based on operational modal analysis and an adaptive complex Morlet filter

2020 ◽  
Vol 62 (8) ◽  
pp. 484-492
Author(s):  
Kai Yang ◽  
Guofeng Wang ◽  
Kaile Ma
Keyword(s):  
Modal Analysis ◽  
Machine Tools ◽  
Production Efficiency ◽  
Stability Boundary ◽  
Operational Modal Analysis ◽  
Modal Parameter ◽  
Local Character ◽  
Specific Frequency ◽  
Harmonic Components ◽  
The Stability

Chatter that occurs between a cutting tool and a workpiece greatly reduces the surface quality and production efficiency. Therefore, it is of great importance to predict and avoid chatter so as to guarantee the stability of the manufacturing process. To realise the accurate prediction of the stability boundary of machine tools, operational modal analysis (OMA) is increasingly receiving attention due to its adequate consideration of variations in working conditions in the industrial environment. However, because of the influence of harmonic components in the response signals, the accuracy in identifying the modal parameters is seriously compromised. In this paper, an adaptive complex Morlet filter (ACMF) is presented to remove the harmonic components by adaptively adjusting the centre frequency and bandwidth according to the local character of the ambient environment in a specific frequency range and filtering out harmonic components that are not strict integer multiples of the fundamental frequency owing to non-rigid periodic motion of the machine tool spindle. In order to show the effectiveness of the proposed method, milling experiments are carried out and experimental modal analysis (EMA) is utilised to make comparisons with the proposed method. Moreover, comparisons between the ACMF and two other typical filtering methods are made. The results indicate that the proposed method performs well in modal parameter recognition for machine tools.

Identify Modal Parameters of a Real Offshore Platform From the Response Excited by Natural Ice Loading

2016 ◽  
Author(s):  
Wenlong Yang ◽  
Lei Li ◽  
Qiang Fu ◽  
Yao Teng ◽  
Shuqing Wang ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Modal Analysis ◽  
Offshore Platform ◽  
Operational Modal Analysis ◽  
Modal Parameters ◽  
Offshore Platforms ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Stability Diagrams ◽  
The Stability

Experimental modal analysis (EMA) is widely implemented to obtain the modal parameters of an offshore platform, which is crucial to many practical engineering issues, such as vibration control, finite element model updating and structural health monitoring. Traditionally, modal parameters are identified from the information of both the input excitation and output response. However, as the size of offshore platforms becomes huger, imposing artificial excitation is usually time-consuming, expensive, sophisticated and even impossible. To address this problem, a preferred solution is operational modal analysis (OMA), which means the modal testing and analysis for a structure is in its operational condition subjected to natural excitation with output-only measurements. This paper investigate the applicability of utilizing response from natural ice loading for operational modal analysis of real offshore platforms. The test platform is the JZ20-2MUQ Jacket platform located in the Bohai Bay, China. A field experiment is carried out in winter season, when the platform is excited by floating ices. An accelerometer is installed on a leg and two segments of acceleration response are employed for identifying the modal parameters. In the modal parameter identification, specifically applied is the data-driven stochastic sub-space identification (SSI-data) method. It is one of the most advanced methods based on the first-order stochastic model and the QR algorithm for computing the structural eigenvalues. To distinguish the structural modal information, stability diagrams are constructed by identifying parametric models of increasing order. Observing the stability diagrams, the modal frequencies and damping ratios of four structural modes can be successfully identified from both segments. The estimated information from both segments are almost identical, which demonstrates the identification is trustworthy. Besides, the stability diagrams from SSI-data method are very clean, and the poles associated with structural modes can become stabilized at very low model order. The research in this paper is meaningful for the platforms serving in cold regions, where the ices could be widespread. Utilizing the response from natural ice loading for modal parameter identification would be efficient and cost-effective.

scholarly journals Identification of modal parameters of machine tools during cutting by operational modal analysis

Procedia CIRP ◽  
2018 ◽  
Vol 77 ◽  
pp. 473-476 ◽  
Author(s):  
Jan Berthold ◽  
Martin Kolouch ◽  
Volker Wittstock ◽  
Matthias Putz
Keyword(s):  
Modal Analysis ◽  
Machine Tools ◽  
Operational Modal Analysis ◽  
Modal Parameters

scholarly journals Detection and removal of harmonic components in operational modal analysis

2017 ◽  
Vol 19 (7) ◽  
pp. 5278-5289 ◽  
Author(s):  
Tong Wang ◽  
Zunping Xia ◽  
Lingmi Zhang
Keyword(s):  
Modal Analysis ◽  
Operational Modal Analysis ◽  
Harmonic Components

scholarly journals A new modal analysis method applied to changing machine tool using clustering

2020 ◽  
Vol 12 (10) ◽  
pp. 168781402096832
Author(s):  
Xuchu Jiang ◽  
Xinyong Mao ◽  
Yingjie Chen ◽  
Caihua Hao
Keyword(s):  
Machine Tool ◽  
Modal Analysis ◽  
Natural Frequency ◽  
Efficient Method ◽  
Machine Tools ◽  
Vibration Signal ◽  
Modal Parameters ◽  
Analysis Method ◽  
Modal Parameter ◽  
The Difference

The states of the machine tool, such as the components’ position and the spindle speed, play leading roles in the change of dynamic parameters. However, the traditional modal analysis method that modal parameters manually identified from vibration signal is greatly interfered by harmonics, and the process of eliminating interference is very inefficient and subjective. At present, there is a lack of a standard and efficient method to characterize modal parameter changes in different states of machine tools. This paper proposes a new machine tool modal classification analysis method based on clustering. The characteristics related to the modal parameters are extracted from the response signal in different states, and the clustering results are used to reflect the changes of machine tool modal parameters. After the amplitude of the frequency response function is normalized, the characteristics related to the natural frequency are acquired, and the clustering results further reflect the difference of the natural frequency of the signal. The new method based on clustering can be a standard and efficient method to characterize modal parameter changes in different states of machine tools.

scholarly journals Operational Modal Analysis Based on Subspace Algorithm with an Improved Stabilization Diagram Method

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Shiqiang Qin ◽  
Juntao Kang ◽  
Qiuping Wang
Keyword(s):  
Modal Analysis ◽  
Confidence Intervals ◽  
Singular Values ◽  
Operational Modal Analysis ◽  
Modal Parameter ◽  
Model Order Selection ◽  
Model Order ◽  
Diagram Method ◽  
Stochastic Subspace Identification ◽  
Stabilization Diagram

Subspace-based algorithms for operational modal analysis have been extensively studied in the past decades. In the postprocessing of subspace-based algorithms, the stabilization diagram is often used to determine modal parameters. In this paper, an improved stabilization diagram is proposed for stochastic subspace identification. Specifically, first, a model order selection method based on singular entropy theory is proposed. The singular entropy increment is calculated from nonzero singular values of the output covariance matrix. The corresponding model order can be selected when the variation of singular entropy increment approaches to zero. Then, the stabilization diagram with confidence intervals which is established using the uncertainty of modal parameter is presented. Finally, a simulation example of a four-story structure and a full-scale cable-stayed footbridge application is employed to illustrate the improved stabilization diagram method. The study demonstrates that the model order can be reasonably determined by the proposed method. The stabilization diagram with confidence intervals can effectively remove the spurious modes.

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