Vibrations of Rotating Machinery

Matsushita, Osami; Kobayashi, Masao; Keogh, Patrick; Kanki, Hiroshi

doi:10.1007/978-4-431-55453-0

Cited by 21 publications

(14 citation statements)

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“…Based on [2][3][4][5][6], following differential equation can be used for common rotating systems, additional considering actuator forces:…”

Section: Formulation In the Time Domainmentioning

confidence: 99%

“…The damping matrix 𝐃 (n,n) (Ω) contains the damping coefficient and the stiffness matrix 𝐂 (n,n) (Ω) the stiffness coefficients of the system. The matrices 𝐃 (n,n) (Ω) and 𝐂 (n,n) (Ω) may also depend on the rotor angular frequency Ω, if, for example, the rotor is supported in sleeve bearings, referring to [2][3][4][5][6].…”

Section: Formulation In the Time Domainmentioning

confidence: 99%

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Generalized mathematical formulation for active vibration control of rotating machines with voltage‐driven electrodynamic actuators between machine feet and steel frame foundation

Werner

2023

Z Angew Math Mech

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In the paper, a generalized mathematical formulation for active vibration control of rotating machines with voltage‐driven electrodynamic actuators between machine feet and steel frame foundation is derived. This generalized mathematical formulation is based on a state space description in the Laplace domain, where the state space vector is lead back, for considering the actuator forces. The aim of the paper is that this generalized mathematical formulation can be used now for arbitrarily models of rotating machines—1D‐, 2D‐, and 3D‐models—with voltage‐driven electrodynamic actuators between machine feet and steel frame foundation, and where the vertical vibration acceleration at each machine foot is lead back to a separate controller. Of course, this mathematical formulation can also be adopt easily to other boundary conditions. As an example, the generalized mathematical formulation is used here for a 1D‐model of a rotating machine, to calculate the poles and the frequency response functions.

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“…Based on [2][3][4][5][6], following differential equation can be used for common rotating systems, additional considering actuator forces:…”

Section: Formulation In the Time Domainmentioning

confidence: 99%

Section: Formulation In the Time Domainmentioning

confidence: 99%

Generalized mathematical formulation for active vibration control of rotating machines with voltage‐driven electrodynamic actuators between machine feet and steel frame foundation

Werner

2023

Z Angew Math Mech

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“…There are four types of unbalance (Matsushita, 2017): static, quasi-static, coupled, and dynamic. This classification considers the relative position between the axis of rotation and mass.…”

Section: Unbalance Rotor Problemmentioning

confidence: 99%

“…In quasi-static unbalance, the mass axis intercepts the axis of rotation at a point other than the center of gravity. This unbalance is considered a special case of dynamic unbalance (Matsushita, 2017). Therefore, quasi-static unbalance classification can be not considered.…”

Section: Figure 1 Different Kinds Of Rotor Unbalanced Failurementioning

confidence: 99%

Rotor Unbalance Kind and Severity Identification by Current Signature Analysis with Adaptative Update to Multiclass Machine Learning Algorithms

Avila

Schaberle

Youssef

et al. 2021

SET

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The health of a rotating electric machine can be evaluated by monitoring electrical and mechanical parameters. As more information is available, it easier can become the diagnosis of the machine operational condition. We built a laboratory test bench to study rotor unbalance issues according to ISO standards. Using the electric stator current harmonic analysis, this paper presents a comparison study among Support-Vector Machines, Decision Tree classifies, and One-vs-One strategy to identify rotor unbalance kind and severity problem – a nonlinear multiclass task. Moreover, we propose a methodology to update the classifier for dealing better with changes produced by environmental variations and natural machinery usage. The adaptative update means to update the training data set with an amount of recent data, saving the entire original historical data. It is relevant for engineering maintenance. Our results show that the current signature analysis is appropriate to identify the type and severity of the rotor unbalance problem. Moreover, we show that machine learning techniques can be effective for an industrial application.

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“…Axis orbit has been used for an operating condition classification; nevertheless, it is still not a well-known path. The advantage of using orbits in comparison to other vibration measurement systems is the simplicity of the instrumentation involved as well as the information multiplicity contained in the orbit (Matsushita et al, 2017). Furthermore, Carbajal-Hernandez et al (2014) show that orbital analysis, when properly treated, can be used to understand the nonlinear behavior of vibrations such as rotor unbalance detection in electrical induction motors.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of rotating machine unbalance using machine learning algorithms on vibration orbital features

Jablon

Avila

Borba³

et al. 2020

Journal of Vibration and Control

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The diagnosis of failures in rotating machines has been subject to studies because of its benefits to maintenance improvement. Condition monitoring reduces maintenance costs, increases reliability and availability, and extends the useful life of critical rotating machinery in industry ambiance. Machine learning techniques have been evolving rapidly, and its applications are bringing better performance to many fields. This study presents a new strategy to improve the diagnosis performance of rotating machines using machine learning strategies on vibration orbital features. The advantage of using orbits in comparison to other vibration measurement systems is the simplicity of the instrumentation involved as well as the information multiplicity contained in the orbit. On the other hand, rolling element bearings are prevalent in industrial machinery. This type of bearing has less orbital oscillation and is noisier than sliding contact bearings. Therefore, it is more difficult to extract useful information. Practical results on an industry motor workbench with rolling element bearings are presented, and the algorithm robustness is evaluated by calculating diagnosis accuracy using inputs with different signal-to-noise ratios. For this kind of noisy scenario where signal analysis is naturally tough, the algorithm classifies approximately 85% of the time correctly. In a completely harsh environment, where the signal-to-noise ratio can be smaller than −25 dB, the accuracy achieved is close to 60%. These statistics show that the strategy proposed can be robust for rotating machine unbalance condition diagnosis even in the worst scenarios, which is required for industrial applications.

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Vibrations of Rotating Machinery

Cited by 21 publications

References 0 publications

Generalized mathematical formulation for active vibration control of rotating machines with voltage‐driven electrodynamic actuators between machine feet and steel frame foundation

Generalized mathematical formulation for active vibration control of rotating machines with voltage‐driven electrodynamic actuators between machine feet and steel frame foundation

Rotor Unbalance Kind and Severity Identification by Current Signature Analysis with Adaptative Update to Multiclass Machine Learning Algorithms

Diagnosis of rotating machine unbalance using machine learning algorithms on vibration orbital features

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