Application of a New Enhanced Deconvolution Method in Gearbox Fault Diagnosis

Wang, Junyuan; Jingtai, Wang; Du, Wenhua; Zhang, Jiping; Wang, Zhijian; Wang, Guanjun; Li, Tao

doi:10.3390/app9245313

Cited by 9 publications

(6 citation statements)

References 47 publications

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“…Spectrum analysis shows that the frequency band of the vibration excitation caused by water pulsation (0.5 Hz) is relatively wide. The wide-peak power spectrum is the typical characteristic of the chaotic system [25,26]. The pipeline vibration excitation produced by the unit's operation (20 Hz, 40 Hz, and 60 Hz) corresponds to the peak power spectrum and has high periodicity.…”

Section: The Analysis Of Multi-time-scale Chaotic Characteristics Based On Ivmdmentioning

confidence: 99%

Chaotic Characteristic Analysis of Vibration Response of Pumping Station Pipeline Using Improved Variational Mode Decomposition Method

Jiang

Zhang

et al. 2021

Applied Sciences

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The measured vibrational responses of the pumping station pipeline in the irrigation site were chosen to confirm the chaotic characteristics of the pumping station pipeline vibration and to determine the vibrational excitation that makes it chaotic. First, the chaotic properties of the pipeline vibration responses were investigated using a saturation correlation dimension and the maximum Lyapunov exponent. The vibration excitation with chaotic features was obtained using an improved variational mode decomposition (IVMD) method to examine the multi-time-scale chaotic characteristics of the pipeline vibration responses. The results show that the vibrational responses of each measuring point of the pipeline under different operating conditions have clear chaotic characteristics, where the chaotic characteristics of the axial points and bifurcated pipe points are relatively strong. The vibration of the operating conditions and measurement points affected by the unit’s operation and flow state change is further complicated. The intrinsic mode function (IMF) produces a low-dimensional chaotic attractor after the IVMD disrupts the vibration response. Still, the vibration excitation of the remaining components on behalf of the units does not have chaotic properties, implying that water pulsation excitation makes the pumping station pipeline vibrations chaotic. The vibration excitation caused by the unit’s operation covers the chaotic characteristics of the pipeline vibration and increases its uncertainty. The outcomes of this study provide a theoretical basis for further exploration of the vibration characteristics of pumping station pipelines, and a new method of chaos analysis is proposed.

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Section: The Analysis Of Multi-time-scale Chaotic Characteristics Based On Ivmdmentioning

confidence: 99%

Chaotic Characteristic Analysis of Vibration Response of Pumping Station Pipeline Using Improved Variational Mode Decomposition Method

Jiang

Zhang

et al. 2021

Applied Sciences

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“…This paper selects three sets of bearing fault data from the bearing data center of Western Reserve University to verify the diagnosis process. The experimental table is shown in Figure 8 [ 29 , 30 ].…”

Section: Data Analysis and Verificationmentioning

confidence: 99%

A Fault Diagnosis Method of Bogie Axle Box Bearing Based on Spectrum Whitening Demodulation

Zheng

Song

et al. 2020

Sensors

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The axle box bearing of bogie is one of the key components of the rail transit train, which can ensure the rotary motion of wheelsets and make the wheelsets adapt to the conditions of uneven railways. At the same time, the axle box bearing also exposes most of the load of the car body. Long-time high-speed rotation and heavy load make the axle box bearing prone to failure. If the bearing failure occurs, it will greatly affect the safety of the train. Therefore, it is extremely important to monitor the health status of the axle box bearing. At present, the health status of the axle box bearing is mainly monitored by vibration information and temperature information. Compared with the temperature data, the vibration data can more easily detect the early fault of the bearing, and early warning of the bearing state can avoid the occurrence of serious fault in time. Therefore, this paper is based on the vibration data of the axle box bearing to carry out adaptive fault diagnosis of bearing. First, the AR model predictive filter is used to denoise the vibration signal of the bearing, and then the signal is whitened in the frequency domain. Finally, the characteristic value of vibration data is extracted by energy operator demodulation, and the fault type is determined by comparing with the theoretical value. Through the analysis of the constructed simulation signal data, the characteristic parameters of the data can be effectively extracted. The experimental data collected from the bearing testbed of high-speed train are analyzed and verified, which further proves the effectiveness of the feature extraction method proposed in this paper. Compared with other axle box bearing fault diagnosis methods, the innovation of the proposed method is that the signal is denoised twice by using AR filter and spectrum whitening, and the adaptive extraction of fault features is realized by using energy operator. At the same time, the steps of setting parameters in the process of feature extraction are avoided in other feature extraction methods, which improves the diagnostic efficiency and is conducive to use in online monitoring system.

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“…Ma and Feng [ 19 ] redesigned the objective function of the MOMEDA algorithm based on the planetary bearing vibration signal characteristics and verified the effectiveness of the proposed method by numerical simulation and experimental analysis. Wang et al [ 20 ] improved the ability of the MOMEDA algorithm to capture fault features by constructing an autoregressive mean shift model to improve noise immunity. Xiang et al [ 21 ] combined MOMEDA and 1.5-dimensional Teager kurtosis spectrum analysis to effectively achieve feature extraction of composite bearing faults.…”

Section: Introductionmentioning

confidence: 99%

Compound Fault Diagnosis of a Wind Turbine Gearbox Based on MOMEDA and Parallel Parameter Optimized Resonant Sparse Decomposition

Feng

Zhang

Hong

et al. 2022

Sensors

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Wind turbines usually operate in harsh environments. The gearbox, the key component of the transmission chain in wind turbines, can easily be affected by multiple factors during the operation process and develop compound faults. Different types of faults can occur, coupled with each other and staggered interference. Thus, a challenge is to extract the fault characteristics from the composite fault signal to improve the reliability and the accuracy of compound fault diagnosis. To address the above problems, we propose a compound fault diagnosis method for wind turbine gearboxes based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and parallel parameter optimized resonant sparse decomposition (RSSD). Firstly, the MOMEDA is applied to the preprocess, setting the deconvolution period with different fault frequency types to eliminate the interference of the transmission path and environmental noise, while decoupling and separating the different types of single faults. Then, the RSSD method with parallel parameter optimization is applied for decomposing the preprocessed signal to obtain the low resonance components, further suppressing the interference components and enhancing the periodic fault characteristics. Finally, envelope demodulation of the enhanced signal is applied to extract the fault features and identify the different fault types. The effectiveness of the proposed method was verified using the actual data from the wind turbine gearbox. In addition, a comparison with some existing methods demonstrates the superiority of this method for decoupling composite fault characteristics.

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Application of a New Enhanced Deconvolution Method in Gearbox Fault Diagnosis

Cited by 9 publications

References 47 publications

Chaotic Characteristic Analysis of Vibration Response of Pumping Station Pipeline Using Improved Variational Mode Decomposition Method

Chaotic Characteristic Analysis of Vibration Response of Pumping Station Pipeline Using Improved Variational Mode Decomposition Method

A Fault Diagnosis Method of Bogie Axle Box Bearing Based on Spectrum Whitening Demodulation

Compound Fault Diagnosis of a Wind Turbine Gearbox Based on MOMEDA and Parallel Parameter Optimized Resonant Sparse Decomposition

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