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.

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.

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Bearing is the most vulnerable key part in rotating machine and bears important influence on the safety of equipment. Weakness and complexity are the two features of fault characteristic information carried by signals in the early stage of fault. For that, a fault is difficult to be recognized correctly. To identify a compound failure of bearing, the paper has brought forward a new self-adaptive option method for component signals that are sensitive to failure feature information of bearing. The sensitivity of kurtosis to bearing failure is exploited and the influence of signal complexity on the extraction of failure feature information is taken seriously, the paper has proposed the self-adaptive option method for component signals that are sensitive to failure feature by combined kurtosis with Complexity parameter included in Hjorth parameters. Furthermore, as the mid-value represents the general level of signal and is not affected by larger or smaller data, with the mid-values of kurtosis and Complexity parameter as the boundary, the paper has chosen the component signals which can more comprehensively show the failure features of bearing. Additionally, by principal component analysis (PCA), component signals selected are blended and reconstructed. Finally, by the Hilbert envelope spectrum of signals reconstructed, failure types of bearing are identified. To verify the effectiveness of presented method, the presented method is compared with conventional method on the basis of the exactly consistent data. The result indicates that the proposed method is superior to the traditional one in extracting fault information and identifying the multiple failure types of bearing.

To solve the difficulty in extracting the characteristics of rotor–stator rubbing faults, the paper has proposed the combined method of Complexity parameter, the harmonic fusion vector bispectrum (HFVB), and intrinsic time-scale decomposition (ITD). First, to fully embody the characteristic information of fault, the HFVB is used to blend the information of signals collected from sensors installed in different positions. Second, taking in mind that the ITD algorithm can embody the effective separation of nonstationary and nonlinear signals, the ITD algorithm makes the separation of blended signals. Third, regarding the important influence of signal complexity on fault characteristic extraction, the complexity parameter of Hjorth parameters can provide very great embodiment of signal complexity. Complexity parameter of Hjorth parameters is introduced as a characteristic parameter index to make a option of proper rotation component (PRC) which can show the characteristics of rubbing fault better. Fourth, signals are reconstructed based on chosen signal components. Meanwhile, to reduce the influence of noise, reconstructed signals are denoised accordingly. Finally, implement the characteristic extraction and fault identification of rubbing faults according to the square demodulation spectrum (SDF) of denoised signals. The result indicates that the harmonic fusion vector bispectrum method can embody the effective blending of fault information; the complexity parameter in Hjorth parameter can serve as the index parameter for option of sensitive characteristic components of rubbing faults. Based on the proposed method, in the square demodulation spectrum of reconstructed signals, it can effectively and precisely provide the characteristics of rotor–stator rubbing fault and successfully identify a fault type.8714542030