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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 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]