A Review of Pump Cavitation Fault Detection Methods Based on Different Signals

Abstract

As one of the research hotspots in the field of pumps, cavitation detection plays an important role in equipment maintenance and cost-saving. Based on this, this paper analyzes detection methods of cavitation faults based on different signals, including vibration signals, acoustic emission signals, noise signals, and pressure pulsation signals. First, the principle of each detection method is introduced. Then, the research status of the four detection methods is summarized from the aspects of cavitation-induced signal characteristics, signal processing methods, feature extraction, intelligent algorithm identification of cavitation state, detection efficiency, and measurement point distribution position. Among these methods, we focus on the most widely used one, the vibration method. The advantages and disadvantages of various detection methods are analyzed and proposed: acoustic methods including noise and acoustic emission can detect early cavitation very well; the vibration method is usually chosen first due to its universality; the anti-interference ability of the pressure pulsation method is relatively strong. Finally, the development trend of detecting cavitation faults based on signals is given: continue to optimize the existing detection methods; intelligent algorithms such as reinforcement learning and deep reinforcement learning will be gradually integrated into the field of cavitation status identification in the future; detection systems still need to be further improved to accommodate different types of pumps; advanced sensing devices combined with advanced signal processing techniques are one of the effective means to detect cavitation in a timely manner; draw on other fault detection methods such as bearing faults and motor faults.

Pump Fault Detection Based on MFCC-MLCNN

“…In contrast, the method based on signal feature extraction and classification and identification, through the acquisition, processing and feature extraction of acoustic signals, extracts the characteristic parameters that can reflect the pump failure, and then classifies and diagnoses the pump failure through the classifier. In terms of acoustic feature extraction, the commonly used feature parameters in the existing sound-based pump fault identification research are Meier frequency cepstrum coefficient (MFCC), linear prediction cepstrum coefficient (LPCC), and Gammatone frequency cepstrum coefficient (GFCC) and so on [5], which have been researched by a large number of scholars and achieved relevant results.…”

Unveiling the Potential of Cavitation Erosion-Induced Heavy Crude Oil Upgrading

“…Cavitation leads to a reduction in the performance and life span of devices in hydraulic systems. There are several studies that have investigated cavitation flow in pipelines [17][18][19], turbines [20,21], pumps [22,23], and valves [24,25].…”

“…Fluids 2023, 8, x FOR PEER REVIEW 3 of 18 and life span of devices in hydraulic systems. There are several studies that have investigated cavitation flow in pipelines [17][18][19], turbines [20,21], pumps [22,23], and valves [24,25]. Ultrasonic processing is widely applied in the petroleum and chemical industries.…”

Effect of Cavitating Hydraulic Elements on Pump Characteristics

“…Cavitation can be identified acoustically [4,18], by vibrations [19], visually and by measuring hydraulic characteristics [4,8,14,15]. An overview of cavitation detection in pumps is given in [20,21].…”

“…The authors of [20] recommended the detection of cavitation in problematic cases by combining two methods. Cavitation detection by the acoustic method is non-destructive and has a high accuracy for early cavitation detection, but the price of sensors is high, and this method is difficult to use in practice.…”