Diagnostic method by using vibration analysis for pump fault detection. (August 2018)

• Record Type: Journal Article
• Authors: Siano, D.; Panza, M.A.
• Is Part Of: Energy procedia. Volume 148(2018)
• Page Start: 10
• Page End: 17
• Publication Date: 2018-08
• Subjects: Pump Cavitation -- Fault Detection -- Vibration -- NonLinear AutoRegressive Model -- Artificial Neural Networks; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2018.08.013
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: Pump cavitation is a common problem affecting pumping systems. It occurs when the pressure of the liquid in the pump drops below a threshold and causes the liquid to vaporize creating tiny bubbles that, when they implode or collapse, trigger intense shockwaves inside the pump determining destructive damage. Excessive vibration on the pump casing could indicate cavitation. Consequently, vibration monitoring can help the detection and prevention of this harmful and undesired phenomenon. The purpose of this research is to analyze the capability of vibration-based techniques to detect, monitor and prevent pump cavitation. Experimental tests were performed on a gear pump used in the lubrication circuit of internal combustion engines. The pump, installed on a dedicated test bench, was forced to cavitate by placing a calibrated orifice on the suction side. Main working parameters, like oil flow rate, suction and delivery pressure, shaft speed, were accurately measured. Different pump operating conditions with and without cavitation occurrence were investigated through the use of a non-intrusive accelerometer, installed in proximity of the suction port so as to monitor the phenomenon in terms of vibration amplitude. A preliminary spectral analysis, based on the Fast Fourier Transform (FFT) of the vibrational signal, was performed in order to easily identify cavitation fundamental frequencies. A time-domain analysis technique was then implemented, aiming to realize an on line pump cavitation detection. Specifically, a NonLinear AutoRegressive (NLAR) approach based on the use of Artificial Neural Networks (ANN) was applied for modeling system behavior. In the paper, the results of the vibration-based method are discussed in depth, highlighting the pros and cons of the methodology. The presented outcomes demonstrate the ability of the proposed algorithm in accurately detect the presence of cavitation phenomena and to determine its intensity in pump real time operation. Hence, it may turn out to be a powerful tool for early detection of pumps incipient faults.