Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory

Huang, Wentao; Kong, Fanzhao; Zhao, Xuezeng

doi:10.1007/s10845-015-1174-x

Cited by 17 publications

(4 citation statements)

References 24 publications

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“…With the development of scientific and technological informatization level, the intelligent products of modern communication and network technology play an increasingly significant role in the aging service industry; especially, in the areas of telemedicine health management and home care service information platforms. The trend of intelligence and Informatization are presented in past studies [39][40][41][42][43]. For instance, smart elderly care greatly expands the scope of demand response with information technology enriches the breadth and depth of services and injects fresh blood into the entire industry [41].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The trend of intelligence and Informatization are presented in past studies [39][40][41][42][43]. For instance, smart elderly care greatly expands the scope of demand response with information technology enriches the breadth and depth of services and injects fresh blood into the entire industry [41]. This study is not only to understand whether the willingness accepts smart elderly care services, but also analyze to deepen the study and is to collect the willingness to adopt such aspects as the usefulness and ease of use of smart services.…”

Section: Literature Reviewmentioning

confidence: 99%

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Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Zhou

Tan

et al. 2020

Symmetry

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The neighborhood rough set theory was adopted for attributes reduction and the weight distribution of condition attributes based on the concept of importance level. Smart elderly care coverage rate is low in China. A decisive role in the adoption of smart elderly care is still a problem that needs to be addressed. This study contributes to the adoption of smart elderly care was selected as the decision attribute. The remaining attributes are used as conditional attributes and the multi-level symmetric attribute set for assessing acceptance of smart elderly care. Prior studies are not included smart elderly care adoption attributes in multi-levels; hence, this problem needs to be addressed. The results of this study indicate that the condition attribute of gender has the greatest influence on the decision attribute. The condition attribute of living expenses for smart elderly care has the second largest impact on decision attribute. Children’s support for the elderly decency of the novel elderly care system and the acceptance of non-traditional elderly care methods belong to the primary condition attribute of traditional concept. The result indicates traditional concepts have a certain impact on the adoption of smart elderly care and a condition attribute of residence also has a slight influence on the symmetric decision attribute. The sensitivity analysis shows the insights for uncertainties and provides as a basis for the analysis of the attributes in the smart elderly care service adoption.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Zhou

Tan

et al. 2020

Symmetry

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“…Then, two extraction methods are compared: (1) NTFT with classical kernel function and standard Gaussian window as shown in equations (17) and (18); (2) wavelet packet decomposition and reconstruction, which is one of the generally used time–frequency analysis methods. 28–30…”

Section: Normal Time–frequency Transform (Ntft) Theory and Inaction Mmentioning

confidence: 99%

Extraction of the instantaneous speed fluctuation based on normal time–frequency transform for hydraulic system

Tian

2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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The instantaneous speed fluctuation is the most direct manifestation of system dynamics behavior, which reflects the running status of the hydraulic system. It is crucial to mine the information contained in instantaneous speed fluctuation and extract them accurately. This paper first introduces the instantaneous speed measurement of the hydraulic system, and it is found that the instantaneous speed signal with an equal angle sampling is a harmonic signal. Next, causes of instantaneous speed fluctuation for hydraulic system are derived by kinetics analysis, where the working mechanism of the axial piston motor and the inevitable misalignment of the shafts are concerned. Then the normal time–frequency transform and the inaction method are introduced, and its high accuracy for harmonic signal extraction in additive noise is verified by comparing with the wavelet packet method with simulation analysis. Finally, the validity of the cause analysis and the extraction method are verified by experiments under different working conditions. The experimental results show that the frequencies of the instantaneous speed fluctuation components can be displayed automatically by normal time–frequency transform spectrum and their waveforms can be extracted accurately by the inaction method. It is also found that the change of the rotating speed and torque can be presented by the variety of the magnitude of the instantaneous speed fluctuation components at the 7th order and the 14th order, respectively. Cause analysis and accurate extraction of the instantaneous speed fluctuation will provide theoretical support and data support for the performance evaluation, state monitoring, and fault diagnosis of the hydraulic system.

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“…Based on vibration signal processing, the traditional fault diagnosis methods extract fault components from the initial noise signal by using rich professional knowledge. At present, signal processing methods, such as empirical mode decomposition (EMD) [2], wavelet packet transform [3], 2 of 17 and morphological filter [4], are commonly used for time-frequency analysis. For separating bearing fault ingredients in a wind turbine gearbox, Dong et al [5] applied improved convolutional neural network with anti-interference to rolling bearing performance degradation assessment.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network

et al. 2020

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Mechanical equipment fault detection is critical in industrial applications. Based on vibration signal processing and analysis, the traditional fault diagnosis method relies on rich professional knowledge and artificial experience. Achieving accurate feature extraction and fault diagnosis is difficult using such an approach. To learn the characteristics of features from data automatically, a deep learning method is used. A qualitative and quantitative method for rolling bearing faults diagnosis based on an improved convolutional deep belief network (CDBN) is proposed in this study. First, the original vibration signal is converted to the frequency signal with the fast Fourier transform to improve shallow inputs. Second, the Adam optimizer is introduced to accelerate model training and convergence speed. Finally, the model structure is optimized. A multi-layer feature fusion learning structure is put forward wherein the characterization capabilities of each layer can be fully used to improve the generalization ability of the model. In the experimental verification, a laboratory self-made bearing vibration signal dataset was used. The dataset included healthy bearings, nine single faults of different types and sizes, and three different types of composite fault signals. The results of load 0 kN and 1 kN both indicate that the proposed model has better diagnostic accuracy, with an average of 98.15% and 96.15%, compared with the traditional stacked autoencoder, artificial neural network, deep belief network, and standard CDBN. With improved diagnostic accuracy, the proposed model realizes reliable and effective qualitative and quantitative diagnosis of bearing faults.

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Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory

Cited by 17 publications

References 24 publications

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Extraction of the instantaneous speed fluctuation based on normal time–frequency transform for hydraulic system

Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network

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