A Fault Feature Extraction Method for Gearbox with Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelets Neighboring Coefficients

Pang, Xinyu; Cheng, Baoan; Zhao-jian, Yang; Li, Feng

doi:10.5545/sv-jme.2018.5441

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…The existence of noise and other interference information in the vibration signal makes it difficult to extract fault features [9]. There are three categories features used in rotating machinery fault diagnosis, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…time domain features, frequency domain features and time and frequency domain features [10,11]. These features extracted by these methods can also be divided into sparse features, such as kurtosis [12], spectrum kurtosis [13], and non-sparse features, like information entropy [9]. However, rotating machineries are easy to be disturbed during operations.…”

Section: Introductionmentioning

confidence: 99%

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A deep condition feature learning approach for rotating machinery based on MMSDE and optimized SAEs

Pan

et al. 2020

Meas. Sci. Technol.

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The failure of rotating machinery affects the quality of the product and the entire production process. However, it usually suffers the subsequent deficiency that the hyperparameters of the fault diagnosis model require constant debugging. This paper proposes a deep condition feature learning approach for rotating machinery based on modified multi-scale symbolic dynamic entropy (MMSDE) and optimized stacked auto-encoders (SAEs). Firstly, MMSDE has been used to extract fault characteristics of the original vibration signal, because such methods do not rely on prior knowledge and experience. MMSDE conducts multi-scale analysis on the original vibration signal and calculates the entropy of the multi-scale signal. The multi-scale fault characteristics are obtained. Then, Bayesian optimization-based SAEs are applied to select feature samples and classify the fault status in mechanical fault diagnosis without debugging. The effectiveness of the proposed method is verified by using open-source data and experimental data. Multiple working conditions are also considered and investigated.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deep condition feature learning approach for rotating machinery based on MMSDE and optimized SAEs

Pan

et al. 2020

Meas. Sci. Technol.

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“…The gear is the most important part of mechanical equipment and is mainly responsible for the transmission of motion and power. On-line monitoring and fault diagnosis of gear is crucial in ensuring the safe operation of mechanical equipment [1] and [2]. At present, the vibration signal analysis is the mainstream of gear fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Research on a Noise Reduction Method Based on DTCWT and the Cyclic Singular Energy Difference Spectrum

Chen

Gang

Liu

et al. 2020

SV-JME

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The gear is the most important part of the transmission system of mechanical equipment, and the monitoring and diagnosis of it can improve the reliability of mechanical equipment. However, mechanical equipment generally works in harsh working conditions. The gear vibration signal is subjected to strong noise interference in working conditions, which brings great challenges for the effective diagnosis of gear faults. This paper proposed a noise reduction method based on the dual-tree complex wavelet transform (DTCWT) and cyclic singular energy difference spectrum. First, the gear vibration signal containing strong noise interference is decomposed into a series of signal components with different frequency characteristics by using the time-frequency analysis ability of DTCWT. Then, cyclic singular energy difference spectrum is proposed based on the idea of a cascaded cycle and the successive elimination of noise interference to process each signal component with different frequency characteristics, and the termination conditions of cyclic singular energy difference spectrum can be set according to the noise interference distribution characteristics in different frequency bands. The final noise reduction of the original gear vibration signal can be realized based on signal reconstruction after the noise reduction processing of each signal components with different frequency bands. Finally, experiments are carried out to verify the effectiveness of the proposed method, which is effective and suitable for the noise reduction of the vibration signal.

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Research on Typical Fault Diagnosis of Gear based on GHM Multiwavelet Transform and MCKD Algorithm

Song

2024

ENG

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Background: Gear is the key part of the transmission part of mechanical equipment, which has a high failure rate under complex working conditions or long-term operation, directly affecting the reliability of mechanical equipment. To realize online monitoring of gears and predict gear faults, whether the diagnosis of typical gear faults is accurate and timely is a key link, so it is necessary to study the diagnosis of typical gear faults. Objective: This paper aims to propose a method that can effectively diagnose typical faults of gears and is easy to program, which is beneficial to realize online monitoring of rotating machinery equipment faults. Method: A typical gear fault diagnosis method based on GHM multiwavelet transform and maximum correlation kurtosis deconvolution algorithm (MCKD) is proposed in this paper. The measured vibration signal is decomposed into components with multiple scale spaces and frequency bands through the GHM multiwavelet transform. Then, the MCKD algorithm is run to suppress the noise of GHM multiwavelet transform coefficients, and the periodic components containing fault information are retained and reconstructed. The typical faults such as gear pitting and broken teeth are tested. Results: GHM multiwavelet transform combined with MCKD algorithm can accurately locate the frequency band containing fault information from the vibration signal with multiple modulations, double sideband, and multi-frequency interference and effectively extract the fault frequency and its frequency multiplication. The root means a square error of the extracted fault frequency is 0.08. This method has a good noise suppression effect and high accuracy in extracting fault frequency. Conclusion: The method proposed in this paper is helpful in realizing automatic programming of gear fault diagnosis and is of great significance in realizing real-time monitoring and online diagnosis of rotating machinery equipment faults. More related patents will appear in the future.

show abstract

A Fault Feature Extraction Method for Gearbox with Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelets Neighboring Coefficients

Cited by 3 publications

References 23 publications

A deep condition feature learning approach for rotating machinery based on MMSDE and optimized SAEs

A deep condition feature learning approach for rotating machinery based on MMSDE and optimized SAEs

Research on a Noise Reduction Method Based on DTCWT and the Cyclic Singular Energy Difference Spectrum

Research on Typical Fault Diagnosis of Gear based on GHM Multiwavelet Transform and MCKD Algorithm

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