A practical signal processing approach for fault detection of axial piston pumps using instantaneous angular speed

The axial piston pump is a core component for power output conversion in the hydraulic system. Monitoring pump status and diagnosing faults in an optimal way are of profound significance for ensuring system reliability. Currently, considerable studies concentrate primarily on the development of vibration-monitoring technologies. However, due to strong fluid shock and noise, vibration analysis has low signal-to-noise ratio and difficulty in fault location. Therefore, an approach of instantaneous angular speed-based fault detection is introduced in this paper since it has the advantages of short transfer path and non-intrusive measurement. Instantaneous angular speed (IAS) is obtained by cross-period linear interpolation (CLI) algorithm for the voltage square wave induced by a magneto-electric tachometer transducer. Compared with the elapsed time method, CLI has the capacity to precisely capture the speed fluctuation. Weighted angle synchronous averaging (WASA) is then utilized to realize the extraction of abnormal wave components in IAS as a vital signal preprocessing method. Instantaneous angular speed fluctuation (IASF) characteristics of the angular domain are analyzed to study the fault diagnosis under varying working conditions. Moreover, it is proven that after processing, the fault features are extracted in the order spectrum where the deterministic shaft order and its harmonics corresponding to wear characteristics are displayed clearly. Experimental results indicate that IAS has demonstrated more effective and sensitive than vibration signals, thus providing a promising tool for the health monitoring of a pump.