Automated Modal Parameter Estimation for Coupled Rotor-Foundation Systems Using Seal Forces as Excitation Source

Paulsen, Thomas T.; Damsgaard, Sebastian Vikær; Clemmensen, Line Katrine Harder; Santos, Ilmar

doi:10.2139/ssrn.4377087

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…However, it was demonstrated by [4] that OMA is a promising tool when the excitation conditions are adequate, extracting values very close to those obtained with well-established Experimental Modal Analysis (EMA) methods, and having the advantages of dismissing the use of excitation equipment, not taking the machine out of operation, reducing costs, and enabling continuous monitoring. More recent studies carried out by [5] showed that even the turbulent gas flow within the seals of a turbomachine can be used to excite the system and identify the modal parameters via OMA, demonstrating OMA's potential for condition monitoring. Moreover, in [6] it was verified that a hybrid approach based on both OMA and EMA was able to diagnose a vibration problem in a large rotating machine used in the power generation industry.…”

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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Section: Introductionmentioning

confidence: 99%