4.6 Article

Acoustic Emission-Based Condition Monitoring and Remaining Useful Life Prediction of Hydraulic Cylinder Rod Seals

Journal

SENSORS
Volume 21, Issue 18, Pages -

Publisher

MDPI
DOI: 10.3390/s21186012

Keywords

hydraulic cylinder; acoustic emission; piston rod seal; root mean square; remaining useful life

Funding

  1. Norwegian Research Council
  2. SFI Offshore Mechatronics [237896]

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This study investigates using acoustic emission (AE) sensors to identify early stages of external leakage initiation in hydraulic cylinders through run to failure studies (RTF). The root mean square (RMS) feature is found to be a potent indicator for understanding leakage initiation.
The foremost reason for unscheduled maintenance of hydraulic cylinders in industry is caused by wear of the hydraulic seals. Therefore, condition monitoring and subsequent estimation of remaining useful life (RUL) methods are highly sought after by the maintenance professionals. This study aimed at investigating the use of acoustic emission (AE) sensors to identify the early stages of external leakage initiation in hydraulic cylinders through run to failure studies (RTF) in a test rig. In this study, the impact of sensor location and rod speeds on the AE signal were investigated using both time- and frequency-based features. Furthermore, a frequency domain analysis was conducted to investigate the power spectral density (PSD) of the AE signal. An accelerated leakage initiation process was performed by creating longitudinal scratches on the piston rod. In addition, the effect on the AE signal from pausing the test rig for a prolonged duration during the RTF tests was investigated. From the extracted features of the AE signal, the root mean square (RMS) feature was observed to be a potent condition indicator (CI) to understand the leakage initiation. In this study, the AE signal showed a large drop in the RMS value caused by the pause in the RTF test operations. However, the RMS value at leakage initiation is seen to be a promising CI because it appears to be linearly scalable to operational conditions such as pressure and speed, with good accuracy, for predicting the leakage threshold.

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