A Novel Balancing Method for Rotor Using Unsupervised Deep Learning

Zhong, Shun; Li, Liqing; Chen, Huizheng; Lu, Zhenyong

doi:10.1155/2021/1800164

Cited by 7 publications

(6 citation statements)

References 40 publications

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“…To apply equation ( 3), a method to learn the mapping from system states to bearing forces directly, while the unbalanced forces to be learned unsupervised has been proposed. 28 The overview of the method and the architecture of the unsupervised network are shown in Figure 1. As Figure 1(a) illustrated, there are three steps for the whole balancing method.…”

Section: Methodsmentioning

confidence: 99%

“…Flow chart of the balancing method 28 – (a) with the unsupervised deep learning network – (b), where Conv3–128 means convolutional layer with 128 filters of size 1 × 3.…”

Section: Methodsmentioning

confidence: 99%

“…That means no extra data or prior knowledge are needed for rotor balancing. Based on the truth, the author proposed a novel balancing method 28 with deep learning technology. To validate the deep learning based method and compare it with the conventional balancing method, a testing rig is carefully designed and established in this work.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On experiments of a novel unsupervised deep learning based rotor balancing method

Zhong

Chen

et al. 2022

Measurement and Control

Self Cite

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Rotor dynamic balancing is essential in rotor industrial. The conventional balancing methods, including the influence coefficients method and modal balancing method, are effective, but lack economy and sufficient usage of the data. To overcome the disadvantages of the conventional balancing methods, a balancing method using unsupervised deep learning without weight trails had been proposed. The proposed network could identify the unbalanced forces from the data observed from just one run of the rotor and without labels. To validate the novel balancing method, an experimental rig is well-designed and established. Experimental validation and comparison with influence coefficients method are conducted. The experimental results show that the proposed balancing method gives consideration to both cost and accuracy. Compared with influence coefficients method, no extra weight trail process is needed and balancing performances are comparative. The experimental rig can be used for proving the scheme and for further same kind of research.

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Section: Methodsmentioning

confidence: 99%

“…Flow chart of the balancing method 28 – (a) with the unsupervised deep learning network – (b), where Conv3–128 means convolutional layer with 128 filters of size 1 × 3.…”

Section: Methodsmentioning

confidence: 99%

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On experiments of a novel unsupervised deep learning based rotor balancing method

Zhong

Chen

et al. 2022

Measurement and Control

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“…The highspeed dynamic balancing performance of flexible rotors was increased by Deng et al [29], Zhao et al [30], and Siegl and Markert [31] in their investigation of the transient dynamic balance approach. A rotor balancing technique employing unsupervised deep learning was presented by Zhong et al [32]. Chen et al [33], established a dynamic model of rotor-casing, used simulation data to obtain the fault data set of unbalanced position, and used a deep convolutional network to classify the unbalanced position faults.…”

Section: Introductionmentioning

confidence: 99%

Flexible rotor unbalance fault location method based on transfer learning from simulation to experiment data

Han,

Wang,

Zhang

et al. 2023

Meas. Sci. Technol.

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In the study of the high-speed dynamic balance of flexible rotors, rotor unbalance positioning is a challenging topic. Particularly for slender rotors, the axial position of the unbalance has an important influence on the high-speed dynamic balance. The unbalance at different axial positions is not the same or even opposite in different rotor mode vibration behaviors. If the unbalance position of a rotor can be identified, the actual unbalance of the rotor can be reduced from the root. This balance method has the same effect in each vibration mode of the rotor; hence, low-speed dynamic balance can be realized to replace high-speed dynamic balance, considerably saving on costs. Deep learning based on few labeled samples can achieve good results for the identification of unbalanced positions; however, there are infinite potential positions of unbalance in the actual rotor. It is difficult to collect sufficient labeled samples to train a reliable intelligent diagnostic model. Fortunately, a large number of rotor vibration datasets labeled with different unbalance positions are available using the rotor dynamic model, and the unbalance position data calculated using the dynamic model contain diagnostic knowledge related to the rotor unbalance position data measured in the rig. Hence, inspired by transfer learning, this study proposed a transfer learning method using dynamic model simulation and experiment data for flexible rotor unbalance fault location. Cross-domain deep transfer recognition of rotor unbalance position was realized.

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“…Generally, rotor balancing methods include the influence coefficient method (ICM), modal balancing method (MBM), and comprehensive balancing method [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Modal Balancing Approach for a Flexible Rotor Using a Vibration Response While the Rotor Is Speeding Up

Hou

Cao

2022

Shock and Vibration

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The modal balancing method (MBM) is an effective method for reducing the vibration caused by the unbalancing of a rotor. A rotor’s modal parameters, especially its modal shapes, need to be accurately calculated in this method. This paper proposes an optimized modal balancing approach for flexible rotors. The vibration modes of the rotor are first obtained with experimental modal analysis based on the rotor’s response signals while the rotor is speeding up. The rotor balancing strategy is subsequently optimized by the sensitivity analysis of the mode shapes. Orthogonal trial masses are obtained based on the orthogonality of each vibration mode, and the correction masses are finally calculated by using the influence coefficients of the trial masses. An experimental result is shown to demonstrate and validate that the proposed approach is able to achieve superior accuracy compared to the conventional MBM.

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A Novel Balancing Method for Rotor Using Unsupervised Deep Learning

Cited by 7 publications

References 40 publications

On experiments of a novel unsupervised deep learning based rotor balancing method

On experiments of a novel unsupervised deep learning based rotor balancing method

Flexible rotor unbalance fault location method based on transfer learning from simulation to experiment data

An Optimized Modal Balancing Approach for a Flexible Rotor Using a Vibration Response While the Rotor Is Speeding Up

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