Artificial neural network for tilting pad journal bearing characterization

Gheller, Edoardo; Chatterton, Steven; Panara, Daniele; Turini, Gabriele; Pennacchi, Paolo

doi:10.1016/j.triboint.2023.108833

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Condition-based maintenance (CBM) is a maintenance program that collects process data in real-time (called condition monitoring—CM) to intercept phenomena that can lead to failures so that operators and manufacturers can make informed maintenance decisions to maintain machine availability, reliability, and performance. CBM can be applied to a variety of systems: general mechanical systems with shafts, gears, bearings [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ], aircrafts [ 12 ], actuators [ 13 , 14 ], wind turbines [ 15 , 16 , 17 ] etc. CBM is generally based on diagnostics and/or prognostics: in the former, anomalies in process data caused by incipient failures are identified; in the latter, failures are predicted before they occur.…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring of Pneumatic Drive Systems Based on the AI Method Feed-Forward Backpropagation Neural Network

Tiboni,

Remino

2024

Sensors

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Machine condition monitoring is used in a variety of industries as a very efficient strategy for equipment maintenance. This paper presents a study on monitoring a pneumatic system using a feed-forward backpropagation neural network as a classifier and compares the results obtained with different sensor signals and associated extracted features as input for classification. The vibrations of the body of a pneumatic cylinder are acquired using both common industrial sensors and low-cost sensors integrated into an Arduino board. Pressure sensors for both chambers and a position sensor are also used. Power spectral density (PSD) is used to extract features from the acceleration signals, as well as statistical indices. Statistical indices are considered for pressure and position sensors. The results, which are based on experimental data obtained on a test bench, show that a feed-forward neural network makes it possible to identify the operating states with a good degree of reliability. Even with low-cost instrumentation, it is possible to realize reliable condition monitoring based on vibrations. This last result is particularly important as it can help to further increase the uptake of this maintenance approach in the industrial environment.

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Section: Introductionmentioning

confidence: 99%

Condition Monitoring of Pneumatic Drive Systems Based on the AI Method Feed-Forward Backpropagation Neural Network

Tiboni,

Remino

2024

Sensors

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“…Compared with the traditional radial sliding bearing, the tilting pad radial sliding bearing has multiple bearing tiles, each tile can independently swing around the fulcrum, and has high stability [1][2][3]. Edoardo et al [4] established a thermohydrodynamic model to predict the static parameters such as stiffness and damping coefficient and minimum oil film thickness of tilting pad journal bearings by training neural networks. Benti et al [5] studied the influence of the number of tiles on the dynamic characteristics of the tilting pad journal bearing, and compared the experimental and numerical results.…”

Section: Introductionmentioning

confidence: 99%

Lubrication Performance Analysis of Tilting Pad Journal Bearing with Dynamic and Static Pressure

Zou,

Chen,

et al. 2024

Advances in Transdisciplinary Engineering

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The application of bearing technology in the field of intelligent equipment and special robots continues to promote technological innovation and industrial upgrading, such as the improvement of bearing materials and the development of intelligent bearings, which helps to improve the performance and operation efficiency of equipment. In this paper, the model of dynamic and static pressure tilting pad journal bearing is established, and the influence of eccentricity and preload on the static characteristics of dynamic and static pressure tilting pad journal bearing is studied. The results show that along the rotation direction of the journal, the oil film thickness decreases gradually, the static pressure of the pad increases gradually, and the lower pad is the main bearing pad; With the increase of eccentricity and preload coefficient, the bearing load coefficient and friction coefficient also increase.

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