Dynamic unbalance detection of Cardan shaft in high-speed train applying double decomposition and double reconstruction method

Ding, Jianming; Lin, Jianhui; Yu, Shengwei

doi:10.1016/j.measurement.2015.05.016

Cited by 8 publications

(5 citation statements)

References 16 publications

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“…(6) After one iteration is completed, the fitness value f i for each sparrow and the average fitness value f avg for the sparrow population are recalculated. f i < f avg : at this point, the phenomenon of 'aggregation' occurs, and Gaussian variation is performed to equation (7). If the mutated individual performs well, then use it.…”

Section: Cssoa Flowmentioning

confidence: 99%

“…In order to address this challenge, researchers are proposed the empirical modal decomposition (EMD) [4]. This technique automatically breaks down the signal into a collection of essential modal functions (IMFs), and is found widespread application in various fields, including mechanical fault diagnosis [5][6][7][8], signal filtering [9,10], and biomedical signal processing [11], etc. According to Teng et al [12], their approach for diagnosing composite faults in wind turbine gearboxes involves integrating a novel modulation model with empirical wavelets.…”

Section: Introductionmentioning

confidence: 99%

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An improved fault diagnosis method for rolling bearings based on wavelet packet decomposition and network parameter optimization

Zhao,

Jiang,

Cheng

et al. 2023

Meas. Sci. Technol.

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The diagnosis of faults in rolling bearings plays a critical role in monitoring the condition and maintaining the performance of rotating machinery, while also preventing major accidents. In this article, a new approach to diagnosing faults in rolling bearings is proposed, using wavelet packet decomposition (WPD) for features extraction and the chaotic sparrow search optimization algorithms (CSSOA) to optimize the parameters of a deep belief network (DBN). Firstly, the WPD method is used for the decomposition of vibration signals in rolling bearings, which are decomposed into three layers, and reconstruction is performed on the nodes of the last layer based on the decomposition. Furthermore, the energy characteristics of the reconstructed nodes are then utilized as inputs to DBN, and the CSSOA is employed to optimize the hyperparameters of DBN. Ultimately, a fault diagnosis model combining WPD with optimizing parameters is presented. This model is validated on bearing datasets from Case Western Reserve University (CWRU) and Jiangnan University (JNU). Experimental results indicate that the average accuracy achieved when modeling with WPD-CSSOA-DBN on the CWRU dataset is 98.24%, with a root mean square error of 0.0713. On the JNU bearing dataset, the modeling achieves an average accuracy of 95.15% with a root mean square error of 0.1018. Compared to other methods, this approach demonstrates stronger feature extraction capabilities and outstanding rolling bearing fault diagnosis abilities.

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Section: Cssoa Flowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved fault diagnosis method for rolling bearings based on wavelet packet decomposition and network parameter optimization

Zhao,

Jiang,

Cheng

et al. 2023

Meas. Sci. Technol.

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show abstract

“…The term vibrational diagnostics includes the detection and identification of several anomalies such as bearing and gearbox faults 11 and imbalance or misalignment of rotating shafts. 22,23 This work focuses on the analysis of an unbalanced rotor and of a gear tooth spall in order to compare the results obtained by the application of the genuine signal processing techniques from both displacement and acceleration signals.…”

Section: Application On Vibrational Diagnosticsmentioning

confidence: 99%

A laser triangulation sensor for vibrational structural analysis and diagnostics

Soave

D’Elia

Mucchi

2020

Measurement and Control

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The industrial progress has reached a level in which it is necessary to understand the behavior of mechanical components and to monitor their conditions without disassembling them. Nowadays, a suitable methodology is based on vibrational analysis usually performed through acceleration signals measured directly on the system to be tested. However, in the last years, the industrial scenario has deeply changed due to the need for time reduction, in particular, for the control operations at the end of the productive line. The genuine methods based on acceleration measurements, for example, through piezoelectric accelerometers, came into conflict with the industrial need as the sensors used for the quality control have to be easily and fastly mounted and unmounted. A valid alternative is represented by the exploitation of laser triangulation sensors that are able to measure the dynamic displacement in a contactless way, strongly reducing the (un)mounting time. The target of this paper is to highlight pros and cons of the contactless displacement analysis through laser triangulation sensors with respect to the contact one through genuine accelerometers by means of a comparison between the results obtained both for experimental modal analysis and vibrational diagnostics of rotating machines.

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“…The right circular flexure hinge is superior to other types of flexure hinges as it is more precise in keeping the positions of the rotation centres during the deformation procedure. 11,12 According to the assumptions mentioned above, it ignores the parasitism in the other degrees of freedom while only considering the rotational compliance u z =M z and the axial flexibility Dx=F x to characterise the flexure hinge. Figure 6 shows the geometric relation of the right circular flexible hinge.…”

Section: Right Circular Flexure Hingesmentioning

confidence: 99%

“…The dynamical unbalance measure method is also a conventional method; [9][10][11] for instance, the vibration is sampled to determine the unbalance by sensing the Coriolis inertial force. The static unbalance measurement method is superior to dynamic measurement methods as it is easy to operate and performs advanced specifications.…”

Section: Introductionmentioning

confidence: 99%

Force transmission of flexure-based leverage mechanism for static unbalance measuring system under self-gravity effect

Wang

Liu

Guan

et al. 2017

Advances in Mechanical Engineering

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This article proposes a flexure-based leverage mechanism that improves the performance of the static unbalance measurement system in terms of sensitivity and precision. The primary function of the flexure-based leverage mechanism is to transform the objective unbalance force/moment from the double-frame platform to an electromagnetic force balance sensor. A model of the flexure-based leverage mechanism was established to investigate the displacement/force amplification based on the elastic model. The finite element method was used to verify the analytical solutions. Considering the gravity effects, the self-weight of the rigid bodies in this leverage mechanism has an effect on the reading in the sensors. However, the linearity characteristics are not sensitive to the self-weight of the proposed leverage mechanism and the stiffness of the flexure hinges. Virtual experiments conducted to verify the linearity and sensitivity of the system indicated that depending on the leverage function of the flexure-based leverage mechanism, the systematic sensitivity is less than 0.02 g/(g cm), the resolution is 0.0013 g cm, and the maximum linearity error is 2.7%.

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Dynamic unbalance detection of Cardan shaft in high-speed train applying double decomposition and double reconstruction method

Cited by 8 publications

References 16 publications

An improved fault diagnosis method for rolling bearings based on wavelet packet decomposition and network parameter optimization

An improved fault diagnosis method for rolling bearings based on wavelet packet decomposition and network parameter optimization

A laser triangulation sensor for vibrational structural analysis and diagnostics

Force transmission of flexure-based leverage mechanism for static unbalance measuring system under self-gravity effect

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