Sensorless Cavitation Detection with CANplus™ Engine Control Panels

Cavitation can severely damage pump equipment and machinery. 

Cavitation is the formation of bubbles within a liquid due to a significant reduction in pressure. Different liquids have varying levels of resistance to cavitation based on factors such as gas concentration and foreign particles. When these bubbles enter an area of higher pressure, the bubbles implode, leading to high-impact forces on metal surfaces, resulting in fatigue and cavitation pits within the pump. 

Cavitation can be detected audibly, with acoustic instrumentation, by machine vibration sensors, or by a decrease or change in performance. Cavitation can dramatically affect the performance and lifespan of machinery where liquid is present, making it vital to understand what this phenomenon entails and how best to combat it. 

Machine learning detects cavitation on CANplus engine control panels. 

Cattron’s CANplus™ engine control panels have built-in machine learning, enabling the panel to detect cavitation without additional sensors. Machine learning delivers unparalleled performance and reliability to the CP1000 and CP750-E engine control panels. 

Incorporated into the CANplus control panels, machine learning is a powerful feature that allows the system to learn the unique normal operation patterns for your application, engine or pump. Unlike traditional preset factory settings, this advanced edge computing adapts to the specific application of your equipment.  

The CANplus panel uses engine data from its machine-learning algorithm to identify and notify operators of cavitation conditions before they cause damage. This proactive approach safeguards against unexpected downtime and maintenance costs and ensures that your engines and pumps are operating at their optimal capacity and efficiency. 

Not only do our CANplus CP1000 and CP750-E engine control panels offer cavitation detection and machine learning, but they can be seamlessly integrated with our RemoteIQ™ cloud-based monitoring and control solution to provide an extra layer of oversight and management, giving you peace of mind.

Examples of Cavitation and Detection

In a scenario involving the movement of pond water by a pump, debris accumulation within the inlet strainer may impede flow, leading to a restriction in the pump’s intake. This restriction causes a decline in pressure on the suction side, approaching vacuum levels. Eventually, the pressure drops low enough to breach the water’s vapor barrier, resulting in bubbles forming within the centrifugal pump, typically originating near its center. As these water bubbles migrate towards higher-pressure zones, they undergo implosion. CANplus machine learning, trained to recognize the system’s normal behavior, can autonomously identify cavitation instances based solely on the data collected from the engine. This eliminates the need for additional suction and discharge pressure sensors. Integrating pressure transducers into the system further enhances the panel’s capability to detect deviations from the optimal system performance. 

Although cavitation in deadheaded pumps is less common, it can still occur under certain conditions. When a pump is deadheaded, the pressure at its inlet diminishes as it attempts to draw in fluid despite the closed valve or system obstructing flow. Much like the situation with a clogged strainer, the vapor barrier is eventually breached, leading to the formation and implosion of bubbles within the pump. CANplus machine learning can also identify this type of cavitation using data from the engine, offering a comprehensive solution for cavitation detection across various scenarios.