Abstract:To effectively extract the information of compound faults of inter-shaft bearing of an aero-engine based on casing vibration signals, the paper has introduced the concept of weighted Katz fractal dimension and proposed the method combining information fusion, wavelet transform (WT), singular value decomposition (SVD), and Katz fractal dimension, the cross-correlation function (CCF-WT-SVD-Katz algorithm). The method includes homologous information fusion achieved by the CCF of horizontal and vertical vibration … Show more
“…Due to its approximate symmetry, symN wavelet will not result in overlaying of frequency bands in signal reconstruction and can reduce the possibility of phase distortion of signal in decomposition and reconstruction. 10 Therefore, sym5 wavelet is chosen to decompose casing vibration signals. The thinking diagram corresponding to paper as is displayed in Figure 3.Figure 3.Process flow diagram of proposed fault diagnosis method.…”
Section: Proposed the Methodsmentioning
confidence: 99%
“…Details:1. sym 5 wavelet 10 is used for decomposing casing vibration signal of aircraft engine and the decomposition layer-number is 5. 22 2.…”
Section: Proposed the Methodsmentioning
confidence: 99%
“…In case of a fault occurring in bearings including an intermediate bearing, specific frequency components which correspond to fault types of bearing will appear in its spectrum. According to Reference 10, the calculation equations of these feature frequencies are displayed in Table 1. By Reference 10, Table 1, the parameters of intermediate bearing and the rotation speed from high and low-pressure rotor: when a fault of outer ring colliding with cage occurs, the feature frequency of fault is 72.1 Hz; inner ring colliding with cage, 85.4 Hz; rolling element, 924.8 Hz.…”
Section: Case Analysismentioning
confidence: 99%
“…8,9 For example, Yu Mingyue, et al, introduced Katz fractal dimension to boost the features of component signals after wavelet transform and effectively extracted the feature information including in vibration signal in the state of compound failure of intermediate bearing. 10 Nonstationary signals were decomposed into multiple empirical wavelet functions (EWF) based on empirical wavelet transform and energy value was taken as the parameter index for EWF selection, and the failure of rolling bearing was diagnosed successfully by Li et al 11 Lempel-Ziv complexity applies to the algorithm in which measurement increases with sequence length and new mode also increases. 12 Larger Lempel-Ziv complexity of sequency gets closer to random sequence.…”
In order to solve the difficulty to correctly identify a compound failure in intermediate bearing of aircraft engine, the paper has brought forward the method of strengthening fault feature information of intermediate bearing with weighted multiscale Lempel–Ziv complexity parameter. The method works by combining multiscale Lempel–Ziv complexity parameter (M-LZC) with wavelet transform (WT) and singular value decomposition (SVD) and implements the feature extraction and identification of compound failure in intermediate bearing. Firstly, failure information from different frequency bands of casing vibration signals is separated and signals are denoised based on WT and SVD difference spectrum. Secondly, concerning that the larger Lempel–Ziv complexity (LZC) of signal is, the more abundant failure information in signal will be; meanwhile, so as to avoid “excessive coarse graining” caused by traditional binarization method, M-LZC values of component signals after denoising are used to describe the failure feature information included in component signals. Thirdly, to further strengthen the failure feature information of intermediate bearing, larger weight value will be endowed to the component signals containing more abundant failure feature information. Failure information is boosted by M-LZC weight coefficient of component signal. Finally, component signals of an enhanced feature are reconstructed and the spectrum of reconstructed signal is used to make precise identification of compound failure type of intermediate bearing in aircraft engine.
“…Due to its approximate symmetry, symN wavelet will not result in overlaying of frequency bands in signal reconstruction and can reduce the possibility of phase distortion of signal in decomposition and reconstruction. 10 Therefore, sym5 wavelet is chosen to decompose casing vibration signals. The thinking diagram corresponding to paper as is displayed in Figure 3.Figure 3.Process flow diagram of proposed fault diagnosis method.…”
Section: Proposed the Methodsmentioning
confidence: 99%
“…Details:1. sym 5 wavelet 10 is used for decomposing casing vibration signal of aircraft engine and the decomposition layer-number is 5. 22 2.…”
Section: Proposed the Methodsmentioning
confidence: 99%
“…In case of a fault occurring in bearings including an intermediate bearing, specific frequency components which correspond to fault types of bearing will appear in its spectrum. According to Reference 10, the calculation equations of these feature frequencies are displayed in Table 1. By Reference 10, Table 1, the parameters of intermediate bearing and the rotation speed from high and low-pressure rotor: when a fault of outer ring colliding with cage occurs, the feature frequency of fault is 72.1 Hz; inner ring colliding with cage, 85.4 Hz; rolling element, 924.8 Hz.…”
Section: Case Analysismentioning
confidence: 99%
“…8,9 For example, Yu Mingyue, et al, introduced Katz fractal dimension to boost the features of component signals after wavelet transform and effectively extracted the feature information including in vibration signal in the state of compound failure of intermediate bearing. 10 Nonstationary signals were decomposed into multiple empirical wavelet functions (EWF) based on empirical wavelet transform and energy value was taken as the parameter index for EWF selection, and the failure of rolling bearing was diagnosed successfully by Li et al 11 Lempel-Ziv complexity applies to the algorithm in which measurement increases with sequence length and new mode also increases. 12 Larger Lempel-Ziv complexity of sequency gets closer to random sequence.…”
In order to solve the difficulty to correctly identify a compound failure in intermediate bearing of aircraft engine, the paper has brought forward the method of strengthening fault feature information of intermediate bearing with weighted multiscale Lempel–Ziv complexity parameter. The method works by combining multiscale Lempel–Ziv complexity parameter (M-LZC) with wavelet transform (WT) and singular value decomposition (SVD) and implements the feature extraction and identification of compound failure in intermediate bearing. Firstly, failure information from different frequency bands of casing vibration signals is separated and signals are denoised based on WT and SVD difference spectrum. Secondly, concerning that the larger Lempel–Ziv complexity (LZC) of signal is, the more abundant failure information in signal will be; meanwhile, so as to avoid “excessive coarse graining” caused by traditional binarization method, M-LZC values of component signals after denoising are used to describe the failure feature information included in component signals. Thirdly, to further strengthen the failure feature information of intermediate bearing, larger weight value will be endowed to the component signals containing more abundant failure feature information. Failure information is boosted by M-LZC weight coefficient of component signal. Finally, component signals of an enhanced feature are reconstructed and the spectrum of reconstructed signal is used to make precise identification of compound failure type of intermediate bearing in aircraft engine.
“…(4) Two sensitive component signals are reconstructed and reconstructed signals are denoised based on singular value difference spectrum (Yu et al, 2021) to reduce the influence of noise.…”
For effective identification of rub-impact faults between rotor and stator of equipment, the paper has contributed the method integrating dual complexity parameters and variational mode decomposition (VMD). Firstly, to effectively separate the fault characteristics involved in signals, VMD algorithm was applied to decompose vibration signals and the component signals were obtained; secondly, taking account of the large difference of fault feature information involved in different component signals and in order to make options of sensitive fault component signals and reduce the loss of fault features, multi-scale Lempel–Ziv complexity and complexity parameter in Hjorth parameters were brought. Starting from 2 different perspectives of complexity evaluation, choose from the sensitive component signals containing more fault characteristics with these 2 complexity parameters; thirdly, signals were reconstructed based on selected sensitive component signals, and meanwhile, singular value difference spectrum algorithm was used to denoise reconstructed signals to further lessen the influence of noises; finally, the rub-impact fault between rotor and stator was identified by square demodulation spectrum (SDS) of denoised signal. The effectiveness of the proposed method has been proved by comparative analysis with other approaches as well as validation analysis of rub-impact fault signals in multiple situations.
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