The Enkurgram: A characteristic frequency extraction method for fluid machinery based on multi-band demodulation strategy

Wu, Kelin; Chu, Ning; Wu, Dazhuan; Antoni, Jérôme

doi:10.1016/j.ymssp.2020.107564

Cited by 35 publications

(6 citation statements)

References 46 publications

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“…The signal characteristics of a multi-wideband in fluid machinery hindered the popularization of some narrowband demodulation methods, such as Kurtogram [ 49 ]. Such being the case, Wu et al (2021) fused a multiple demodulation band selection, Enkurgram [ 50 ], by combining the energy factor and the shape factor. The fusion method presented excellent demodulation ability in simulation analysis and fluid machinery applications.…”

Section: Methodsmentioning

confidence: 99%

Application of Digital Twins and Metaverse in the Field of Fluid Machinery Pumps and Fans: A Review

Yang

et al. 2022

Sensors

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Digital twins technology (DTT) is an application framework with breakthrough rules. With the deep integration of the virtual information world and physical space, it becomes the basis for realizing intelligent machining production lines, which is of great significance to intelligent processing in industrial manufacturing. This review aims to study the application of DTT and the Metaverse in fluid machinery in the past 5 years by summarizing the application status of pumps and fans in fluid machinery from the perspective of DTT and the Metaverse through the collection, classification, and summary of relevant literature in the past 5 years. The research found that in addition to relatively mature applications in intelligent manufacturing, DTT and Metaverse technologies play a critical role in the development of new pump products and technologies and are widely used in numerical simulation and fault detection in fluid machinery for various pumps and other fields. Among fan-type fluid machinery, twin fans can comprehensively use technologies, such as perception, calculation, modeling, and deep learning, to provide efficient smart solutions for fan operation detection, power generation visualization, production monitoring, and operation monitoring. Still, there are some limitations. For example, real-time and accuracy cannot fully meet the requirements in the mechanical environment with high-precision requirements. However, there are also some solutions that have achieved good results. For instance, it is possible to achieve significant noise reduction and better aerodynamic performance of the axial fan by improving the sawtooth parameters of the fan and rearranging the sawtooth area. However, there are few application cases of the Metaverse in fluid machinery. The cases are limited to operating real equipment from a virtual environment and require the combination of virtual reality and DTT. The application effect still needs further verification.

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Section: Methodsmentioning

confidence: 99%

Application of Digital Twins and Metaverse in the Field of Fluid Machinery Pumps and Fans: A Review

Yang

et al. 2022

Sensors

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“…The simulation signal of rolling bearings with inner race fault and outer race fault can be expressed as follows [4,[30][31][32][33]:…”

Section: Simulation Analysismentioning

confidence: 99%

Compound fault diagnosis of rolling bearings based on improved tunable Q-factor wavelet transform

Zhou²,

Gao

et al. 2021

Meas. Sci. Technol.

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In order to solve the difficulty of compound fault diagnosis of rolling bearings, a novel rolling bearings fault diagnosis method based on improved tunable Q-factor wavelet transform (TQWT) is proposed in this paper. Firstly, a new evaluation index of signal decomposition called KR is defined by summing kurtosis and root mean square (RMS) with weight. KR is the compromise between impulse factor and energy factor, which can better represent the fault characteristics of sub-bands obtained by TQWT. Secondly, the KR is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Thirdly, the bearing vibration signal is decomposed by the improved TQWT and the sub-bands are sorted descending according to the KR. Finally, the Hilbert envelope analysis is carried out and the fault types are determined by comparing the fault characteristic frequencies obtained from the Hilbert envelopes to the fault characteristic frequencies calculated by formula. The proposed fault diagnosis method is fully evaluated by simulation and experiments. The results demonstrate that the KR takes advantage of kurtosis and RMS and can be better used to optimize the parameter of TQWT. And the compound fault features of rolling bearings can be accurately separated into different sub-bands by the improved TQWT, which is helpful to improve the accuracy of compound fault diagnosis of rolling bearings.

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“…To address the intricacies of fault diagnosis, a variety of advanced methodologies have been explored, enriching the diagnostic landscape. Among these, techniques such as the short-time Fourier transform (STFT), autogram, encurgram, IESFOgram, infogram, and curtogram stand out [7][8][9][10][11][12]. The autogram, for instance, is renowned for its adeptness in selecting optimal demodulation bands, showcasing remarkable efficacy in noisy environments and thus outperforming traditional kurtosis-based methods.…”

Section: Introductionmentioning

confidence: 99%

Graph Convolutional Network Based on CQT Spectrogram for Bearing Fault Diagnosis

Yan,

Liao,

Zhang

et al. 2024

Machines

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In this paper, a graph convolutional network is constructed and applied for bearing fault diagnosis. Specifically, the constant-Q transform (CQT) is first adopted for spectral analysis of vibration signals, where the frequencies are distributed in the logarithmic scale. Varied frequency resolutions can be obtained to satisfy the spectral resolution requirement and reduce signal dimension. Afterwards, the CQT spectrum is modeled by a graph, where nodes are frequency bins and edges reflect the inner relationship of different bins. There are edges between the fundamental and harmonic components. Then, a two-layer graph convolutional network (GCN) is utilized to assess the significance of vibration sources within the mixed signals. Finally, the bearing faults are determined according to the output of the GCN. To the best of our knowledge, this is the first work to model the vibration signal in this graph structure. The advantage of this approach lies in the simplification of edge definitions, facilitating shared connectivity relationships between the fundamental frequency and harmonics. Its performance was compared with another state-of-the-art fault diagnosis model. Experimental results demonstrate that the proposed model obtains higher accuracy, and it is more effective in extracting discriminative features.

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The Enkurgram: A characteristic frequency extraction method for fluid machinery based on multi-band demodulation strategy

Cited by 35 publications

References 46 publications

Application of Digital Twins and Metaverse in the Field of Fluid Machinery Pumps and Fans: A Review

Application of Digital Twins and Metaverse in the Field of Fluid Machinery Pumps and Fans: A Review

Compound fault diagnosis of rolling bearings based on improved tunable Q-factor wavelet transform

Graph Convolutional Network Based on CQT Spectrogram for Bearing Fault Diagnosis

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