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Large-sized rotating machines usually contain weak feature information of rub-impact fault, which is hard to extract. General scale transformation stochastic resonance (GSTSR) can match input signals with different frequencies by using the optimal barrier height and boost the weak fault feature in signals. The performance of GSTSR is determined by systemic parameters. When a rub-impact fault occurs between rotor and stator, vibration signals are often accompanied by an impact. Therefore, the paper takes advantage of sensibility of waveform factor to rub-impact fault information and margin factor to impact properties of signal and reconstructs a new signal evaluation index based on waveform and margin factors. The signal evaluation index is treated as the fitness function of grey wolf optimization (GWO) algorithm and combined with GSTSR to perform a comprehensive evaluation to rub-impact fault feature information. The result of comparison with conventional method (with signal to noise ratio (SNR) as fitness function) indicates that in case of extracting rub-impact fault features, the proposed method identifies a rotor-stator rub-impact fault more precisely than the classical method. [ABSTRACT FROM AUTHOR]

To solve the difficulties of extracting the fault feature information from compound failures of rolling bearings, a new method by combining intrinsic time scale decomposition (ITD), Hjorth parameters and Teager energy spectrum (ITD-Hjorth-TES) is proposed. The method starts with combining wavelet global threshold denoising algorithm and ITD, the signals denoised are decomposed by means of ITD. Secondly, the complexity parameter is determined from Hjorth parameters of proper rotation components (PRCs) obtained by decomposition. Thirdly, pick up the PRCs associated with the two maximum complexity values and calculate the cross-correlation function of two PRCs. Finally, the feature corresponding to the compound failures of the rolling bearing is extracted and the fault type is identified with the Teager energy spectrum of obtained cross-correlation function. The result indicates that the feature from compound failures of rolling bearings can be extracted efficiently and an accurate judgment on the type of compound failure can be implemented. [ABSTRACT FROM AUTHOR]

To properly identify the combined faults of rolling bearings, the paper has proposed a new approach to extract the fault characteristics of rolling bearings based on the combination of discrete wavelet transform (DWT) and generalized S transform (GST). To effectively separate the signals of different frequency bands, original acceleration signal was subjected to wavelet decomposition to obtain approximate and detailed signals. Furthermore, a time–frequency analysis was given to approximate and detailed signals through generalized S transform due to its excellent time–frequency property. At last, according to time–frequency slice diagram of GST, feature frequency of the rolling bearing could be effectively extracted under different combined fault types. The result indicates that the combination of wavelet transform and generalized S transform can identify the combined fault types for bearings more precisely than generalized S transform alone. [ABSTRACT FROM AUTHOR]

To precisely determine the type of bearing fault, the paper has proposed the solution by combining first-order difference of signals and coarse-graining. When a bearing fault occurs, vibration signals are often accompanied by impact features and meanwhile, greater difference among adjacent points of signals corresponds to more evident impact features. Compared with original vibration signals, the first-order difference of signals works better in measuring this kind of impact feature. Therefore, the solution proposed in the paper takes first-order difference in place of original signals and combines with coarse-graining algorithm for subsequent treatment. Compared with average value of signals, the variance of signal can represent the discreteness of signal which describes the impact feature of bearing fault from the other side. Therefore, the solution has coarse-graining operation for difference signals in improved coarse-graining method with variance as criterion to further highlight impact features of rolling bearing. Secondly, a feature set has been established to comprehensively represent fault information of bearing with the average of coarse-graining sequence as feature parameter. Finally, the type of bearing fault is identified according to the fault feature set and support vector machine (SVM). To verify the effectiveness and universality of solution proposed by the paper, a comparison is given to proposed solution and classical method according to dataset of faults of rotor and rolling bearing, and public dataset. The result indicates that with different rotate speeds and types of faults, the proposed solution is more precise to identify the type of bearing faults; in two different datasets, the average identification rate of the proposed solution is 96.43% (aeroengine–rotor–rolling bearing fault dataset) and 100% (public dataset) for unknown samples. [ABSTRACT FROM AUTHOR]

To fulfill the effective diagnosis of the rubbing fault between the rotor and the stator, the combination strategy of adaptive weight particle swarm optimization (PSO) and general scale transformation stochastic resonance (GSTSR) is proposed in the paper. Firstly, in view of the self-adaptive weighted PSO featured by high precision and quick convergence, the method has made self-adaptive adjustment of systematic parameters based on PSO algorithm (with signal–noise ratio as fitness function). Secondly, GSTSR can further highlight the characteristic information otherwise covered by noise. Therefore, self-adaptive weighted PSO algorithm is combined with GSTSR to make characteristic extraction of rotor–stator rubbing faults. Finally, a comparative analysis with other methods and the analysis of faults in different states all indicate that the combination of self-adaptive weighted PSO algorithm and GSTSR can enhance rubbing fault characteristics and has effective identification of rotor–stator rubbing faults. [ABSTRACT FROM AUTHOR]