A novel order spectrum-based Vold-Kalman filter bandwidth selection scheme for fault diagnosis of gearbox in offshore wind turbines

Feng, Ke; Ji, Jinchen; Wang, Kesheng; Wei, Dongdong; Zhou, Chengning; Ni, Qing

doi:10.1016/j.oceaneng.2022.112920

Cited by 44 publications

(11 citation statements)

References 29 publications

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“…Several run-to-failure tests are arranged on an experiment rig of spur gearbox, which is widely applied in machine tools, robotics, and other intelligent manufacturing systems. 44 Two kinds of endurance tests are scheduled: one is with lubricating oil, and the other is without lubricating oil. Figure 8 presents the schematic representation of the test rig of the spur gearbox.…”

Section: Resultsmentioning

confidence: 99%

A cyclostationarity-based wear monitoring framework of spur gears in intelligent manufacturing systems

Feng

Chen

et al. 2023

Structural Health Monitoring

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The gearbox is widely applied as the mechanical transmission system of intelligent manufacturing systems, such as machine tools and robotics. The harsh working environments make the gear surface prone to wear. The progression of surface wear can bring severe failures to the gear tooth, including gear tooth root crack, surface spalling of gear tooth, and tooth breaking, all of which could damage the whole transmission system. Hence, it is essential to monitor and evaluate the gear surface wear propagation. The gear wear has been proven highly relevant with the vibration second-order cyclostationary (CS2) characteristics. Therefore, this paper develops a novel cyclostationarity-based framework to monitor and evaluate gear wear propagation. More specifically, the squared envelope (SE) of the residual signal, removing deterministic components, is utilized to identify the gear wear distribution and its propagation trends, validated using the measured gear surface morphology. Moreover, a new CS2-based indicator is proposed to assess the severity of gear surface wear, achieving a high correlation with measured surface roughness: [Formula: see text] is more than 0.9. The developed cyclostationarity-based framework can comprehensively evaluate the degradation status of the gear system caused by surface wear, significantly benefiting the health management of the gear transmission system, which is of great practical value for the health management of intelligent manufacturing systems. A series of endurance tests are conducted to verify the effectiveness and superiority of the developed framework for gear wear monitoring compared with the conventional indicators.

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Section: Resultsmentioning

confidence: 99%

A cyclostationarity-based wear monitoring framework of spur gears in intelligent manufacturing systems

Feng

Chen

et al. 2023

Structural Health Monitoring

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“…To check WCER’s superiority in fatigue pitting propagation monitoring over other conventional indicators, two CS2-based indicators (ICS2 and RCC) and five traditional indicators are used for comparisons, 42 as shown in Figure 20. From Figure 20, it can be found that the traditional CS2 indicators, RCC and ICS2, can indicate the pitting progression to some extent; however, neither of them has a better performance than WCER; also, the pitting propagation at the last stage has not been well tracked.…”

Section: Resultsmentioning

confidence: 99%

A novel vibration indicator to monitor gear natural fatigue pitting propagation

Feng

et al. 2023

Structural Health Monitoring

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Fatigue pitting can reduce the gear surface durability and induce other severe failures, which will eventually lead to the complete loss of transmission function of the transmission system. Thus, monitoring fatigue pitting progression is vital to avoid unexpected economic losses and incidents. Thanks to the unique characteristics of the gear meshing process, there is a close relationship between the tribological features of fatigue pitting and gear vibration cyclostationarity. Based on the vibration cyclostationarity, this paper develops a novel second-order cyclostationary (CS2) fatigue pitting monitoring indicator, which can accurately assess the degradation status of the gear system and benefit subsequent health management. The advantage of the developed cyclostationary indicator in evaluating and monitoring the process of fatigue pitting propagation is demonstrated with the natural fatigue pitting progression test, through comparisons with other conventional indicators.

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“…The Vold-Kalman filter is an effective tool for extracting non-stationary vibration harmonics and is used in noise reduction filtering, control systems, condition monitoring, and other fields. However, the performance of the Vold-Kalman filter depends on the filter bandwidth, and it is a challenge to select the bandwidth flexibly according to the signal characteristics [4]. Computed order tracking (COT) [5] is a common method for fault diagnosis of mechanical equipment under time-varying speed.…”

Section: Introductionmentioning

confidence: 99%

Bearing failure diagnosis at time-varying speed based on adaptive clustered fractional Gabor transform

Liu

Shang

Gao

et al. 2023

Meas. Sci. Technol.

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For bearing fault diagnosis at time-varying speed with tachometer-free and non-resampling, the crucial process is to obtain a high-resolution time-frequency representation and extract fault features. However, current multi-component non-stationary signal feature extraction methods based on time-frequency transform suffer from fixed parameter settings and insufficient resolution for low signal-to-noise ratio signals. To address these issues, a novel adaptive clustered fractional Gabor transform is proposed and applied to extract bearing fault features at time-varying speed. Firstly, the Grey Wolf optimization is utilized to adaptively search for the optimal fractional order and Gauss window length based on the maximum spectral kurtosis and the generalized time-bandwidth product to achieve the most adequate fractional Gabor spectrum. Then, the Clustering by Fast Search and Find of Density Peaks algorithm reconstructs the sparse representation of the fractional Gabor spectrum, remapping multi-component signals into single-component clusters. Bearing fault diagnosis is achieved by matching the relative order of each cluster with the bearing fault characteristic coefficients. Simulation signals validate the superiority of the feature extraction method, and experimental signals validate the feasibility of the bearing fault diagnosis method.

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A novel order spectrum-based Vold-Kalman filter bandwidth selection scheme for fault diagnosis of gearbox in offshore wind turbines

Cited by 44 publications

References 29 publications

A cyclostationarity-based wear monitoring framework of spur gears in intelligent manufacturing systems

A cyclostationarity-based wear monitoring framework of spur gears in intelligent manufacturing systems

A novel vibration indicator to monitor gear natural fatigue pitting propagation

Bearing failure diagnosis at time-varying speed based on adaptive clustered fractional Gabor transform

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