Research on Fault Feature Extraction Method Based on NOFRFs and Its Application in Rotor Faults

Abstract

Rub-impact between the rotating and static parts is a more common fault. The occurrence of faults is often accompanied by the generation of nonlinear phenomena. However, it is difficult to find out because the nonlinear characteristics are not obvious at the beginning of the fault. As a new frequency domain-based method, nonlinear output frequency response functions (NOFRFs) use the vibration response to extract the nonlinear characteristics of the system. This method has a better recognition rate for fault detection. Also, it has been applied in structural damages detection, but the high-order NOFRFs have the characteristics that the signals are weak and the features are difficult to extract. On this basis, the concept of the weighted contribution rate of the NOFRFs is proposed in this paper. The variable weighted coefficients with orders are used to amplify the influence of high-order NOFRFs on the nonlinearity of the system so as to extract its fault characteristics. The new index RI is proposed based on Clenshaw–Curtis quadrature formula to eliminate the effect of artificially selected weighted coefficients on sensitivity. Especially in the early stage of the fault, the new index varies greatly with the deepening of the fault. Both simulation and experimental results verify the validity and practicability of the new index. The new index has certain guiding significance in the detection of mechanical system faults.

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“…Liu et al introduced a multi-sensor information feature extraction method based on vibration intensity theory. The filtered pump outlet flow and pressure signals were converted into velocity and acceleration signals through a physical quantity transformation approach, which enhanced the information comprehensiveness and state assessment accuracy [8].…”

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“…Chu and Lu [15] assessed the feasibility to determine the rubbing location in a multi-disk rotor system by using dynamic stiffness identification. Liu et al [16] proposed a method based on Nonlinear Output Frequency Response Functions to identify signal features; the method was verified with simulations and experiments. Hu et al [17] conducted rub, crack and rub-crack coupled fault experiments and analysed the instantaneous frequency signatures of the faults using the Hilbert-Huang transform.…”