Evaluation the low cost of vibration and acoustics techniques based on novel cavitation detecting in axial pumps by varying load conditions and statistical method

Abstract

Abstract Rapid advancement of manufacturing, the management of machinery data has drawn more attention. Managing the health methods of machinery is condition monitoring (CM), which has produced remarkable outcomes. However, the signals vibration and acoustic were changed as cavitation levels changed. Furthermore, it was customary to finish multiple tasks one at a time. Using pressure, vibration, and auditory inputs for detection of simultaneous cavitation. In this work suggests a task CM for analysis of diverse load conditions. First, a fast Fourier transform-based data increase is developed to solve sample signals that this study faced. The distinguishable sensitivity features from signals in domain frequency of pump. Next, a novel cavitation index (ICP) structure prediction is put out. In conclusion, the CPI is assessed using various datasets of pumps for both temporal and frequency domain vibration and acoustic signals. Techniques of acoustic and vibration are sensitive to recognize cavitation at high frequencies between 10 and 15 kHz, according to frequency domain analysis of both methods. In contrast to the acoustic technique, the vibration technique demonstrated greater sensitivity in detecting the onset of cavitation within frequency region of 1to 2 kHz, under varying operational conditions. As a result, it is evident that using a low-frequency range sensor to identify cavitation can lower the sensor’s cost.

Keywords

• Axial pumps
• Vibration
• Acoustic
• Prediction cavitation
• Efficiency
• Testing