Fault Diagnosis of Wind Turbine Gearbox Based on Dual-tree Complex Wavelet and Information Entropy

Liu, Qingqing; Yang, Jiangtian

doi:10.1109/phm-chongqing.2018.00210

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…Ideally, the instantaneous fundamental frequency of the current signal would be the same as the instantaneous frequency, as shown in Equation (6). Due to the presence of electromechanical coupling sites, instantaneous frequency signals will also have frequency components that reflect the characteristics of torsional vibration in actual electromechanical systems.…”

Section: Three-phase Current Instantaneous Speed Estimationmentioning

confidence: 99%

“…In summary, the three-phase current instantaneous rotation speed estimation algorithm proposed in this section is shown in Figure 3. Since the instantaneous phase and the instantaneous frequency display a differential relationship, estimation of the instantaneous rotation angle of the shaft requires the instantaneous phase, which is derived from the Park vector demodulation method in the algorithm process shown in Equations ( 4)- (6). After subsequent operations, the instantaneous angle of rotation can be estimated.…”

Section: Three-phase Current Instantaneous Speed Estimationmentioning

confidence: 99%

“…At present, vibration analysis is the main method for extracting the state features of a planetary gearbox. To achieve feature extraction of the vibration signal of a planetary gearbox, various signal processing methods have been used, such as envelope analysis [ 2 ], stochastic resonance [ 3 ], Hilbert Transform [ 4 ], time domain analysis [ 5 ], wavelet transform [ 6 , 7 ], empirical mode decomposition [ 8 , 9 , 10 ], etc. Lei Yaguo et al [ 11 ] summarized and evaluated the planetary gear fault diagnosis method based on vibration analysis, showing that the vibration signal of the planetary gearbox has many congenital defects, making signal analysis difficult.…”

Section: Introductionmentioning

confidence: 99%

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A Three-Phase Current Tacholess Envelope Order Analysis Method for Feature Extraction of Planetary Gearbox under Variable Speed Conditions

Zhang

Kuang

et al. 2021

Sensors

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Planetary gearboxes are the key components of large equipment, such as wind turbines, shield machines, etc. The operating state of the planetary gearbox is related to the safety of the equipment as a whole, and its feature extraction technology is essential. In assessing the problem of the non-stationarity of the current signal under variable speed conditions and the difficulty of evaluating the operating state of the planetary gearbox under a tacholess condition, a three-phase current, variable-speed tacholess envelope order analysis method is proposed. Firstly, a tacholess rotation speed estimation is completed by extracting the trend term of the instantaneous frequency of the asynchronous motor’s three-phase currents. The motor slip rate is assumed to be constant. Then, the envelope order analysis signal is obtained by re-sampling in the angular domain. Finally, the features of the envelope order signal are extracted, and a linear discriminant analysis (LDA) algorithm is used to fuse multiple indexes to generate a comprehensive feature reflecting the operating status of the planetary gearbox. The results of the simulation analysis and experimental verification show that the proposed method is effective in evaluating the operating state of the planetary gearbox under variable speed conditions. Compared with the traditional time–frequency ridge extraction method, the tacholess speed estimation method can improve the instantaneous speed estimation accuracy. The comprehensive index of envelope order completes the planetary gearbox state identification process, and a 95% classification accuracy rate is achieved.

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Section: Three-phase Current Instantaneous Speed Estimationmentioning

confidence: 99%

Section: Three-phase Current Instantaneous Speed Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Three-Phase Current Tacholess Envelope Order Analysis Method for Feature Extraction of Planetary Gearbox under Variable Speed Conditions

Zhang

Kuang

et al. 2021

Sensors

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“…The findings revealed that the accuracies of DSFOA-SVM and PSO-SVM were 93.33% and 90%, respectively. Liu et al [14] introduced a sensitive component selection algorithm (SCSA), which integrates envelope spectral entropy and correlation coefficient to emphasize the fault characteristics of wind turbine gearboxes. Information entropy (IE) and dual-tree complex wavelet transform (DT-CWT) were employed to detect gearbox faults.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Wind Turbine Gearbox Using Vibration Scatter Plot and Visual Geometric Group Network

Wang,

Hung,

et al. 2024

Processes

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This study aims to develop a fault detection system designed specifically for wind turbine gearboxes. It proposes a hybrid fault diagnosis algorithm that combines scatter plot analysis with the visual geometric group (VGG) technique to identify various fault types, including gear rust, chipping, wear, and aging. To capture vibration signals, a three-axis vibration sensor was integrated with a NI-9234 DAQ card. Digital signal processing techniques were employed to actively filter out noise from the captured signals. Gaussian white noise was incorporated into the training data to enhance the noise resistance of the network model, which was then utilized for scatter plot generation. The VGG technique was subsequently applied to identify faults. The testing data were collected at two different speeds, with 1500 samples taken at each speed, totaling 3000 samples. For both training and testing, 400 samples of each fault type were employed for training, while 200 samples were allocated for testing. The test results demonstrated an overall identification accuracy of 97.7% for both the no-fault gearbox and the four-fault states, underscoring the effectiveness of the proposed methodology.

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Fault Detection and Classification for Wind Turbine Gearbox via a Time-Frequency Analysis Method

Zhu

Huang

et al. 2020

2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)

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Fault Diagnosis of Wind Turbine Gearbox Based on Dual-tree Complex Wavelet and Information Entropy

Cited by 4 publications

References 13 publications

A Three-Phase Current Tacholess Envelope Order Analysis Method for Feature Extraction of Planetary Gearbox under Variable Speed Conditions

A Three-Phase Current Tacholess Envelope Order Analysis Method for Feature Extraction of Planetary Gearbox under Variable Speed Conditions

Fault Diagnosis of Wind Turbine Gearbox Using Vibration Scatter Plot and Visual Geometric Group Network

Fault Detection and Classification for Wind Turbine Gearbox via a Time-Frequency Analysis Method

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