Hydraulic pump fault diagnosis based on chaotic characteristics of speed signals under non-stationary conditions

Abstract

Pumps are vital components in hydraulic equipment, and their malfunction is directly related to the normal operation of the whole system. The hydraulic system is accompanied by the conversion of multi-domain energy during operation. In particular, it exhibits non-stationarity and nonlinearity under variable operating conditions, which brings difficulties to fault diagnosis. In this paper, the instantaneous angular speed (IAS) signal obtained by the equal-angle measurement is studied to diagnose pump faults under non-stationary conditions. For this purpose, a synchroextracting normal S transform method is proposed to extract the time-frequency feature components properly. Then, the extracted signal is reconstructed into a high-dimensional phase space based on the improved C-C method. On this basis, the quantitative analysis indicators of pump state are obtained by the G-P algorithm and small-data method, including correlation dimension, Kolmogorov entropy, and largest Lyapunov exponent. Pump faults can be classified in a chaotic space by using these sensitive features. The results show that the proposed method is capable of diagnosing different states of pumps and robust to the variation of load values.