A Review on Application of Dynamic Parameters of Journal Bearing for Vibration and Condition Monitoring

Babu, T. Narendiranath; Raj, T. Manvel; Lakshmanan, T.

doi:10.1017/jmech.2015.6

Cited by 14 publications

(3 citation statements)

References 125 publications

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“…In this particular study, the gated recurrent units outperformed the others with an R 2 value of 93%. For a more comprehensive review, interested readers are advised to refer to review papers covering bearing failure modes and their detection [27][28][29][30], data acquisition for fault detection [30][31][32][33], and machine learning applications for condition monitoring [34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders

Ates,

Höfchen,

Witt

et al. 2023

Sensors

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Predictive maintenance is considered a proactive approach that capitalizes on advanced sensing technologies and data analytics to anticipate potential equipment malfunctions, enabling cost savings and improved operational efficiency. For journal bearings, predictive maintenance assumes critical significance due to the inherent complexity and vital role of these components in mechanical systems. The primary objective of this study is to develop a data-driven methodology for indirectly determining the wear condition by leveraging experimentally collected vibration data. To accomplish this goal, a novel experimental procedure was devised to expedite wear formation on journal bearings. Seventeen bearings were tested and the collected sensor data were employed to evaluate the predictive capabilities of various sensors and mounting configurations. The effects of different downsampling methods and sampling rates on the sensor data were also explored within the framework of feature engineering. The downsampled sensor data were further processed using convolutional autoencoders (CAEs) to extract a latent state vector, which was found to exhibit a strong correlation with the wear state of the bearing. Remarkably, the CAE, trained on unlabeled measurements, demonstrated an impressive performance in wear estimation, achieving an average Pearson coefficient of 91% in four different experimental configurations. In essence, the proposed methodology facilitated an accurate estimation of the wear of the journal bearings, even when working with a limited amount of labeled data.

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Section: Introductionmentioning

confidence: 99%

Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders

Ates,

Höfchen,

Witt

et al. 2023

Sensors

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“…A physical model is a real object and a mathematical model is defined as a system of hypotheses expressed by a system of equations and inequalities. The mechanical oscillation of structural nodes is a dynamic event with mass points performing a reciprocating motion around an equilibrium position [12,13]. A solid body is, in technical diagnostics, presented as a whole replaced by a material point, and the motion of all body parts is the same at a given moment.…”

Section: Introductionmentioning

confidence: 99%

Experimental Measurement of Dynamic Characteristics of Structural Units

Coranic,

Mascenik

2023

Processes

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The aim of this study is to investigate and optimize the dynamic properties of an entire structural unit. Using modal analysis and experimental measurements of the propulsion system, natural frequencies with close agreement were identified. The drive was able to work within the frequency range during start-up and normal operation, but due to various influences, including the inherent oscillations of structural elements, complex dynamic phenomena occurred. The presence of a conveyor with rubber and plastic wheels also affected the results. Important information on the input shaft, tooth frequency, driveline oscillation and output shaft was obtained. Research has identified resonant frequencies and increased drive oscillation that are created by the interaction between the input shaft and tooth frequency. The significant frequency of bent screws in the conveyor pipe affects the shafts and the drive screw, which in turn causes problems with material fatigue and microcracks. Corrective measures include the possibility of replacing or balancing the screw and increasing the diameter of the pipe. Regular monitoring and diagnostics have a preventive nature and serve to minimize serious consequences. Implementing a controller with a PID system offers the potential to suppress oscillations and improve dynamic and strength characteristics, while accurate calibration of this implementation is of key importance.

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“…However, accurately estimating the dynamic parameters through theoretical models is still a challenge because it is difficult to accurately model every phenomenon affecting the dynamic behavior of the bearings. This problem has led to the development of novel numerical and experimental techniques for dynamic parameter identification [4,6,7]. Tiwari and Chougale [8] developed an algorithm to estimate the dynamic parameters of active magnetic bearings as well as the residual rotor unbalance.…”

Section: Introductionmentioning

confidence: 99%

An Algebraic Approach for Identification of Rotordynamic Parameters in Bearings with Linearized Force Coefficients

et al. 2021

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In this work, a novel methodology for the identification of stiffness and damping rotordynamic coefficients in a rotor-bearing system is proposed. The mathematical model for the identification process is based on the algebraic identification technique applied to a finite element (FE) model of a rotor-bearing system with multiple degree-of-freedom (DOF). This model considers the effects of rotational inertia, gyroscopic moments, shear deformations, external damping and linear forces attributable to stiffness and damping parameters of the supports. The proposed identifier only requires the system’s vibration response as input data. The performance of the proposed identifier is evaluated and analyzed for both schemes, constant and variable rotational speed of the rotor-bearing system, and numerical results are obtained. In the presented results, it can be observed that the proposed identifier accurately determines the stiffness and damping parameters of the bearings in less than 0.06 s. Moreover, the identification procedure rapidly converges to the estimated values in both tested conditions, constant and variable rotational speed.

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A Review on Application of Dynamic Parameters of Journal Bearing for Vibration and Condition Monitoring

Cited by 14 publications

References 125 publications

Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders

Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders

Experimental Measurement of Dynamic Characteristics of Structural Units

An Algebraic Approach for Identification of Rotordynamic Parameters in Bearings with Linearized Force Coefficients

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