Imitation model of unbalanced rotor on fluid-film bearings

Babin, Alexander; Polyakov, Roman

doi:10.21595/vp.2020.21529

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…The test rig is a rotor-bearing system with a plain fluid-film bearing and a multisensory measurement system. 26,27 The photograph of the test rig is presented in Figure 1. A bronze fluid-film bearing 20 mm wide and 40.2 mm diameter is used.…”

Section: The Test Rig and The Test Resultsmentioning

confidence: 99%

“…The test rig is a rotor-bearing system with a plain fluidfilm bearing and a multisensory measurement system. 26,27 The photograph of the test rig is presented in Figure 1 The National Instruments modules NI 9269 and NI 9205 are used as converters for the control and the measurement systems, respectively (see Figure 3). Analog output module NI 9269 is used to control the shaft speed and the flow rate in the bearing.…”

Section: Introductionmentioning

confidence: 99%

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Fault diagnosis systems for rotating machines operating with fluid-film bearings

Stebakov

Kornaev

Popov

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part J:

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The paper deals with the application of deep learning methods to rotating machines fault diagnosis. The main challenge is to design a fault diagnosis system connected with multisensory measurement system that will be sensitive and accurate enough in detecting weak changes in rotating machines. The experimental part of the research presents the test rig and results of high-speed multisensory measurements. Six states of a rotating machine, including a normal one and five states with loosened mounting bolts and small unbalancing of the shaft, are under study. The application of deep network architectures including multilayer perceptron, convolutional neural networks, residual networks, autoencoders and their combination was estimated. The deep learning methods allowed to identify the most informative sensors, then solve the anomaly detection and the multiclass classification problems. An autoencoder based on ResNet architecture demonstrated the best result in anomaly detection. The accuracy of the proposed network is up to 100% while the accuracy of an expert is up to 65%. A one-dimensional convolutional neural network combined with a multilayer perceptron that contains a pretrained encoder demonstrated the best result in multiclass classification. The detailed fault detection accuracy with the determination of the specific fault is 83.3%. The combinations of known deep network architectures and application of the proposed approach of pretraining of the encoders together with using a block of inputs for one prediction demonstrated high efficiency.

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Section: The Test Rig and The Test Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%