The use of operational modal analysis in the process of modal parameters identification in a rotating machine supported by roller bearings

Storti, Gustavo Chaves; Machado, Tiago

doi:10.1007/s12206-021-0105-3

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…Moreover, the algorithm identifies some extra modes near the foundation mode of 157.4 Hz when data from Tests 1 and 2 are employed. Investigations performed with the same test rig by [ 25 ] detected a mode associated to the bearings housing near the frequency of 155 Hz, which would explain these extra identified modes. Therefore, the proposed algorithm demonstrated a good ability to identify the foundation’s modes.…”

Section: Resultsmentioning

confidence: 99%

“…In order to investigate it, Ref. [ 25 ] performed the identification of a rotating system through OMA techniques and revealed that different test conditions influence the extracted parameters, ranging from non-identification to precise identification of modal parameters, which characterizes challenges to the automatic algorithm proposed here. Hence, it was decided to use more than one test condition.…”

Section: Methodsmentioning

confidence: 99%

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Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques

Dreher

Storti

Machado

2023

Sensors

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Many parameters can be used to express a machine’s condition and to track its evolution through time, such as modal parameters extracted from vibration signals. Operational Modal Analysis (OMA), commonly used to extract modal parameters from systems under operating conditions, was successfully employed in many monitoring systems, but its application in rotating machinery is still in development due to the distinct characteristics of this system. To implement efficient monitoring systems based on OMA, it is essential to automatically extract the modal parameters, which several studies have proposed in the literature. However, these algorithms are usually developed to deal with structures that have different characteristics when compared to rotating machinery, and, therefore, work poorly or do not work with this kind of system. Thus, this paper proposes, and has as its main novelty in, a new automated algorithm to carry out modal parameter identification on rotating machinery through OMA. The proposed technique was applied in two different datasets to enable the evaluation of the robustness to different systems and test conditions. It is revealed that the proposed algorithm is suitable for the accurate extraction of frequencies and damping ratios from the stabilization diagram, for both the rotor and the foundation, and only one user defined parameter is required.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques

Dreher

Storti

Machado

2023

Sensors

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“…In the simple harmonic response analysis, the displacement and force vectors of the object are in the form of simple harmonic waves [21]:…”

Section: Bearing Inner Racewaymentioning

confidence: 99%

Study on Contact Force and Vibration Characteristics of Composite Cylindrical Roller Bearing

Bian

Zeng

et al. 2022

International Journal of Rotating Machinery

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The composite cylindrical roller is composed of a hollow cylindrical roller and a filler body and is a new type of structure roller bearing. In order to explore the influence of different parameters on the contact characteristics and vibration characteristics of bearings, finite element models of static contact, modal analysis, and harmonic response analysis of composite cylindrical roller bearings were established based on ABAQUS software. The effects of filling rate, radial force, and the number of rollers on parameters such as contact force, contact stress, and natural frequency were studied. The results show that when the filling rate of the cylindrical roller increases from 0% to 70%, the natural frequency of bearing and the peak frequency of its harmonic response decrease, the force distribution in the contact area is also more uniform, and the maximum contact stress of the roller is reduced by 29.1%; the radial force has no effect on the peak frequency of the harmonic response of the bearing, but the increase of the radial force will increase the peak value of the response displacement, and the contact force and stress of the rollers will also increase. When the number of rollers increases from 11 to 15, the natural frequency and the peak frequency of harmonic response increase, the peak displacement decreases, the contact force distribution of the rollers in the bearing area is more uniform, and the maximum contact stress of the roller is reduced by 21.1%. The research result can provide a theoretical reference for the structural optimization and engineering application of elastic composite cylindrical roller bearings.

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“…This subject has already been addressed by many authors, who proposed techniques to identify the modal parameters even in the presence of harmonic excitation, such as the work presented by [3]. However, research carried out by [4] demonstrated that the white noise excitation is determinant in the identification of the rotating machines' modal parameters through OMA, an identification that is impossible in cases when, for example, the machine is operating without sufficient white noise excitation.…”

Section: Introductionmentioning

confidence: 99%

Vibration Signal Evaluation Based on K-Means Clustering as a Pre-Stage of Operational Modal Analysis for Structural Health Monitoring of Rotating Machines

Dreher,

Storti,

Machado

2023

Energies

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Rotating machines are key components in energy generation processes, and faults can lead to shutdowns or catastrophes encompassing economic and social losses. Structural Health Monitoring (SHM) of structures in operation is successfully performed via Operational Modal Analysis (OMA), which has advantages over traditional methods. In OMA, white noise inputs lead to the accurate extraction of modal parameters without taking the system out of operation. However, this excitation condition is not easy to attain for rotating machines used in power generation, and OMA can provide inaccurate information. This research investigates the applicability of machine learning as a pre-stage of OMA to differentiate adequate from inadequate excitations and prevent inaccurate extraction of modal parameters. Data from a rotor system was collected under different conditions and OMA was applied. In a training stage, measurements were characterized by statistical features and K-means was used to determine which features provided information about the excitation condition, that is, which excitation was adequate to extract the rotor’s modal parameters via OMA. In a testing stage, data were successfully classified as adequate or not adequate for OMA, achieving 100% accuracy and revealing the technique’s potential to support SHM of rotating machines. The technique is extendable to other monitoring systems based on OMA.

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The use of operational modal analysis in the process of modal parameters identification in a rotating machine supported by roller bearings

Cited by 13 publications

References 15 publications

Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques

Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques

Study on Contact Force and Vibration Characteristics of Composite Cylindrical Roller Bearing

Vibration Signal Evaluation Based on K-Means Clustering as a Pre-Stage of Operational Modal Analysis for Structural Health Monitoring of Rotating Machines

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