Abstract:
The present invention provides cavitation detection systems and methods employing a classifier for detecting, diagnosing and/or classifying cavitation in a pumping system. The classifier can be integral to tie cavitation detection system and/or operatively coupled to the cavitation system via a controller, diagnostic device and/or computer. Parameters such as flow, pressure and motor speed arc measured and/or estimated, and then provided to a classifier system Such systems include Bayesian, Fuzzy Set, nonlinear regression, neural networks and other training systems, for example The classifier system provides a signal indicative of the existence and extent of cavitation. An exemplary classification system is presented that delineates cavitation extent into one or more of the following categories:  0  (no cavitation),  1  (incipient cavitation),  2  (medium cavitation),  3  (fill cavitation) and  4  (surging cavitation). The cavitation signal can be utilized for monitoring and/or controlling a pumping system to mitigate pump wear, failure and other conditions associated with cavitation.

Description:
TECHNICAL FIELD 
     The present invention relates to the art of pumping systems, and more particularly to systems and methodologies for detecting and diagnosing pump cavitation. 
     BACKGROUND OF THE INVENTION 
     Motorized pumps are employed in industry for controlling fluid flowing in a pipe, fluid level in a tank or container, or in other applications, wherein the pump receives fluid via an intake and provides fluid to an outlet at a different (e.g., higher) pressure and/or flow rate. Such pumps may thus be employed to provide outlet fluid at a desired pressure (e.g., pounds per square inch or PSI), flow rate (e.g., gallons per minute or GPM), or according to some other desired parameter associated with the performance of a system in which the pump is employed. For example, the pump may be operatively associated with a pump control system implemented via a programmable logic controller (PLC) or other type of controller coupled to a motor drive, which controls the pump motor speed in order to achieve a desired outlet fluid flow rate, and which includes I/O circuitry such as analog to digital (A/D) converters for interfacing with sensors and outputs for interfacing with actuators associated with the controlled pumping system. In such a configuration, the control algorithm in the PLC may receive process variable signals from one or more sensors associated with the pump, such as a flow meter in the outlet fluid stream, inlet (suction) pressure sensors, outlet (discharge) pressure sensors, and the like, and may make appropriate adjustments in the pump motor speed such that the desired flow rate is realized. 
     In conventional motorized pump control systems, the motor speed is related to the measured process variable by a control scheme or algorithm, for example, where the measured flow rate is compared with the desired flow rate (e.g., setpoint). If the measured flow rate is less than the desired or setpoint flow rate, the PLC may determine a new speed and send this new speed setpoint to the drive in the form of an analog or digital signal. The drive may then increase the motor speed to the new speed setpoint, whereby the flow rate is increased. Similarly, if the measured flow rate exceeds the desired flow rate, the motor speed may be decreased. Control logic within the control system may perform the comparison of the desired process value (e.g., flow rate setpoint) with the measured flow rate value (e.g., obtained from a flow sensor signal and converted to a digital value via a typical A/D converter), and provide a control output value, such as a desired motor speed signal, to the motor drive according to the comparison. 
     The control output value in this regard, may be determined according to a control algorithm, such as a proportional, integral, derivative (PID) algorithm, which provides for stable control of the pump in a given process. The motor drive thereafter provides appropriate electrical power, for example, three phase AC motor currents, to the pump motor in order to achieve the desired motor speed to effectuate the desired flow rate in the controlled process. Load fluctuations or power fluctuations which may cause the motor speed to drift from the desired, target speed are accommodated by logic internal to the drive. The motor speed is maintained in this speed-control manner based on drive logic and sensed or computed motor speed. 
     Motorized pump systems, however, are sometimes subjected to process disturbances, which disrupt the closed loop performance of the system. In addition, one or more components of the process may fail or become temporarily inoperative, such as when partial or complete blockage of an inlet or outlet pipe occurs, when a pipe breaks, when a coupling fails, or when a valve upstream of the pump fluid inlet or downstream of the pump discharge fluid outlet becomes frozen in a closed position. In certain cases, the form and/or nature of such disturbances or failures may prevent the motorized pump from achieving the desired process performance. For instance, where the pump cannot supply enough pressure to realize the desired outlet fluid flow rate, the control system may increase the pump motor speed to its maximum value. Where the inability of the pump to achieve such pressure is due to inadequate inlet fluid supply, partially or fully blocked outlet passage, or some other condition, the excessive speed of the pump motor may cause damage to the pump, the motor, or other system components. 
     Some typical process disturbance conditions associated with motorized pump systems include pump cavitation, partial or complete blockage of the inlet and/or outlet, and impeller wear or damage. Cavitation is the formation of vapor bubbles in the inlet flow regime or the suction zone of the pump, which can cause accelerated wear, and mechanical damage to pump seals, bearing and other pump components, mechanical couplings, gear trains, and motor components. This condition occurs when local pressure drops to below the vapor pressure of the liquid being pumped. These vapor bubbles collapse or implode when they enter a higher-pressure zone (e.g., at the discharge section or a higher pressure area near the impeller) of the pump, causing erosion of impeller casings as well as accelerated wear or damage to other pump components. 
     If a motorized pump runs for an extended period under cavitation conditions, permanent damage may occur to the pump structure and accelerated wear and deterioration of pump internal surfaces, bearings, and seals may occur. If left unchecked, this deterioration can result in pump failure, leakage of flammable or toxic fluids, or destruction of other machines or processes for example. These conditions may represent an environmental hazard and a risk to humans in the area. Thus, it is desirable to provide improved control and/or diagnostic systems for motorized pumps, which minimize or reduce the damage or wear associated with pump cavitation and other process disturbances, failures, and/or faults associated with motorized pump systems and pumping processes. 
     SUMMARY OF THE INVENTION 
     The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the invention. It is intended to neither identify key or critical elements of the invention nor delineate the scope of the invention. Rather, the sole purpose of this summary is to present some concepts of the invention in a simplified form as a prelude to the more detailed description that is presented hereinafter. The invention provides methods and systems for detecting cavitation in pumping systems. The methods comprise measuring pressure and flow information related to the pumping system and detecting cavitation using a classifier system, such as a neural network. The systems comprise a classifier system for detecting pump cavitation according to flow and pressure data. The invention may be employed in cavitation monitoring, as well as in control equipment associated with pumping systems, whereby pump wear and failure associated with cavitation conditions may be reduced or mitigated. 
     One aspect of the invention provides a system for detecting cavitation in a motorized pumping system, comprising a classifier system for detecting pump cavitation according to flow and pressure data. The classifier system may comprise a neural network receiving flow and pressure signals from flow and pressure sensors associated with the pumping system, wherein the neural network is trained using back propagation. The classifier may further receive pump speed data from a speed sensor associated with the pumping system to detect pump cavitation according to the flow, pressure, and speed data. In this manner, pump cavitation may be detected for pumping systems employing variable frequency motor drives. The neural network of the classifier system may be further adapted to determine the extent of cavitation in the pumping system, such as by providing an output according to the degree of cavitation in the pump. The neural network, moreover, may provide a cavitation signal indicative of the existence and extent of cavitation in the pumping system, wherein the cavitation signal may be used to change the operation of the pumping system according to the extent of cavitation. 
     According to another aspect of the present invention, there is provided a method of detecting cavitation in a pumping system having a motorized pump, comprising measuring pump flow and pressure data, and detecting pump cavitation according to the flow and pressure data using a classifier system. The classifier system may comprise a neural network trained by back propagation, which inputs pressure and flow information and outputs a classification of the existence and the extent of cavitation in the pumping system. Pump speed may also be measured and provided to the neural network, whereby pump cavitation may be detected and diagnosed at different pump speeds. The methodology may further comprise providing a cavitation signal indicative of the extent of cavitation, and changing or altering the operation of the pumping system in accordance therewith, whereby the system may be controlled to reduce or mitigate pump cavitation. 
    
    
     To the accomplishment of the foregoing and related ends, the invention, then, comprises the features hereinafter fully described. The following description and the annexed drawings set forth in detail certain illustrative aspects of the invention. However, these aspects are indicative of but a few of the various ways in which the principles of the invention may be employed. Other aspects, advantages and novel features of the invention will become apparent from the following detailed description of the invention when considered in conjunction with the drawings. 
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a side elevation view illustrating an exemplary motorized pump system and a cavitation detection system therefor in accordance with an aspect of the present invention; 
     FIG. 2 is a side elevation view illustrating another exemplary motorized pump system and a cavitation detection system therefor in accordance with the invention; 
     FIG. 3 is a side elevation view illustrating another exemplary motorized pump system and a cavitation detection system therefor in accordance with the invention; 
     FIG. 4 is a schematic diagram illustrated further aspects of the exemplary cavitation detection system in accordance with the invention; 
     FIG. 5 is a schematic diagram further illustrating the exemplary cavitation detection system of FIG. 4; 
     FIG. 6 is a schematic diagram illustrating an exemplary cavitation classification in accordance with the invention; 
     FIG. 7 is a perspective schematic diagram illustrating an exemplary neural network in accordance with another aspect of the invention; and 
     FIG. 8 is a flow diagram illustrating an exemplary method of detecting cavitation in a pumping system in accordance with an aspect of the present invention. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The various aspects of the present invention will now be described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. The invention provides systems and methods by which the adverse effects of pump cavitation may be reduced or mitigated by measuring pressure and flow information associated with a pumping system and detecting cavitation using a classifier system, such as a neural network trained via back propagation, receiving the pressure and flow information as inputs to the classifier. The classifier system may further consider pump speed information in detecting cavitation, whereby cavitation may be diagnosed at different pump speeds. 
     Referring now to FIGS. 1-3, an aspect of the present invention involves systems and apparatus for pump cavitation detection and/or diagnosis. The cavitation detection system may be operatively associated with a pumping system, and may be located in a controller, a stand-alone diagnostic device, or in a host computer, as illustrated and described in greater detail hereinafter with respect to FIGS. 1,  2 , and  3 , respectively. An exemplary motorized pumping system  12  is illustrated in FIG. 1 having a pump  14 , a three phase electric motor  16 , and a control system  18  for operating the system  12  in accordance with a setpoint  19 . Although the exemplary motor  16  is illustrated and described herein as a polyphase asynchronous electric motor, the various aspects of the present invention may be employed in association with single phase motors as well as with DC and other types of motors. In addition, the pump  14  may comprise a centrifugal type pump, however, the invention finds application in association with other pump types not illustrated herein, for example, positive displacement pumps. The control system  18  operates the pump  14  via the motor  16  according to the setpoint  19  and one or more measured process variables, in order to maintain operation of the system  12  commensurate with the setpoint  19  and within the allowable process operating ranges specified in setup information  68 . For example, it may be desired to provide a constant fluid flow, wherein the value of the setpoint  19  is a desired flow rate in gallons per minute (GPM) or other engineering units. 
     The pump  14  comprises an inlet opening  20  through which fluid is provided to the pump  14  in the direction of arrow  22  as well as a suction pressure sensor  24 , which senses the inlet or suction pressure at the inlet  20  and provides a corresponding suction pressure signal to the control system  18 . Fluid is provided from the inlet  20  to an impeller housing  26  including an impeller (not shown), which rotates together with a rotary pump shaft coupled to the motor  16  via a coupling  28 . The impeller housing  26  and the motor  16  are mounted in a fixed relationship with respect to one another via a pump mount  30 , and motor mounts  32 . The impeller with appropriate fin geometry rotates within the housing  26  so as to create a pressure differential between the inlet  20  and an outlet  34  of the pump. This causes fluid from the inlet  20  to flow out of the pump  14  via the outlet or discharge tube  34  in the direction of arrow  36 . The flow rate of fluid through the outlet  34  is measured by a flow sensor  38 , which provides a flow rate signal to the control system  18 . 
     In addition, the discharge or outlet pressure is measured by a pressure sensor  40 , which is operatively associated with the outlet  34  and provides a discharge pressure signal to the control system  18 . It will be noted at this point that although one or more sensors (e.g., suction pressure sensor  24 , discharge pressure sensor  40 , outlet flow sensor  38 , and others) are illustrated in the exemplary system  12  as being associated with and/or proximate to the pump  14 , that such sensors may be located remote from the pump  14 , and may be associated with other components in a process or system (not shown) in which the pump system  12  is employed. Alternatively, flow may be approximated rather than measured by utilizing pressure differential information, pump speed, fluid properties, and pump geometry information or a pump model. Alternatively or in combination, inlet and/or discharge pressure values may be estimated according to other sensor signals and pump/process information. 
     In addition, it will be appreciated that while the motor drive  60  is illustrated in the control system  18  as separate from the motor  16  and from the controller  66 , that some or all of these components may be integrated. Thus, for example, an integrated, intelligent motor may include the motor  16 , the motor drive  60  and the controller  66 . Furthermore, the motor  16  and the pump  14  may be integrated into a single unit (e.g., having a common shaft wherein no coupling  28  is required), with or without integral control system (e.g., control system  18 , comprising the motor drive  60  and the controller  66 ) in accordance with the invention. 
     The control system  18  further receives process variable measurement signals relating to motor (pump) rotational speed via a speed sensor  46 . As illustrated and described further hereinafter, a cavitation detection system  70  within the controller  66  may advantageously detect and/or diagnose cavitation in the pump  14  using a neural network classifier receiving suction and discharge pressure signals from sensors  24  and  40 , respectively, as well as flow and pump speed signals from the flow and speed sensors  38  and  46 . The motor  16  provides rotation of the impeller of the pump  14  according to three-phase alternating current (AC) electrical power provided from the control system via power cables  50  and a junction box  52  on the housing of the motor  16 . The power to the pump  14  may be determined by measuring the current provided to the motor  16  and computing pump power based on current, speed, and motor model information. This may be measured and computed by a power sensor (not shown), which provides a signal related thereto to the control system  18 . Alternatively or in combination, the motor drive  60  may provide motor torque information to the controller  66  where pump input power is calculated according to the torque and possibly speed information. 
     The control system  18  also comprises a motor drive  60  providing three-phase electric power from an AC power source  62  to the motor  16  via the cables  50  in a controlled fashion (e.g., at a controlled frequency and amplitude) in accordance with a control signal  64  from the controller  66 . The controller  66  receives the process variable measurement signals from the suction pressure sensor  24 , the discharge pressure sensor  40 , the flow sensor  38 , and the speed sensor  46 , together with the setpoint  19 , and provides the control signal  64  to the motor drive  60  in order to operate the pump system  12  commensurate with the setpoint  19 . In this regard, the controller  66  may be adapted to control the system  12  to maintain a desired fluid flow rate, outlet pressure, motor (pump) speed, torque, suction pressure, or other performance characteristic. Setup information  68  may be provided to the controller  66 , which may include operating limits (e.g., min/max speeds, min/max flows, min/max pump power levels, min/max pressures allowed, NPSHR values, and the like), such as are appropriate for a given pump  14 , motor  16 , and piping and process conditions. 
     The controller  66  comprises a cavitation detection system  70 , which is adapted to detect and/or diagnose cavitation in the pump  14 , according to an aspect of the invention. Furthermore, the controller  66  selectively provides the control signal  64  to the motor drive  60  via a PID control component  71  according to the setpoint  19  (e.g., in order to maintain or regulate a desired flow rate) and/or a cavitation signal  72  from the cavitation detection component  70  according to detected cavitation in the pump, whereby operation of the pumping system  12  may be changed or modified according to the cavitation signal  72 . The cavitation detection system  70  may detect the existence of cavitation in the pump  14 , and additionally diagnose the extent of such cavitation according to pressure and flow data from the sensors  24 ,  40 , and  38  (e.g., and pump speed data from the sensor  46 ), whereby the cavitation signal  72  is indicative of the existence and extent of cavitation in pump  14 . 
     Referring also to FIG. 2, the cavitation detection system  70  may comprise a stand-alone diagnostic device  150 . The diagnostic component or device  150  is operatively associated with the motor  16  and the pump  14 , in order to receive pressure, flow, and pump speed signals from the sensors  24 ,  40 ,  38 , and  46 , whereby pressure and flow (e.g., and pump speed) information is provided to a classifier (e.g., neural network) in the cavitation detection system  70 , as illustrated and described hereinafter with respect to FIGS. 4-7. In addition, the diagnostic component  150  may include a display  154  for displaying information to an operator relating to the operation of the motorized pumping system  12 . The diagnostic component  150  may further include an operator input device  160  in the form of a keypad, which enables a user to enter data, information, function commands, etc. For example, the user may input information relating to system status via the keypad  160  for subsequent transmission to a host computer  166  via a network  168 . In this regard, the control system  18  may also be operatively connected to the network  168  for exchanging information with the diagnostic component  150  and/or the host computer  166 , whereby cavitation signals or cavitation information from the cavitation detection system  70  may be provided to one or both of the controller  66  and/or the host computer  166 . In addition, the keypad  160  may include up and down cursor keys for controlling a cursor, which may be rendered on the display  154 . Alternatively or in addition, the diagnostic component  150  may include a tri-state LED (not shown) without the display  154  or the keypad  160 . Alternatively, the diagnostic component  150  could be integrated into the motor  16  and/or the pump  14 . 
     The diagnostic component  150  may further include a communications port  164  for interfacing the diagnostic component  150  with the host computer  166  via a conventional communications link, such as via the network  168  and/or a wireless transmitter/receiver  105 . According to an aspect of the present invention, the diagnostic component  150  may be part of a communication system including a network backbone  168 . The network backbone  168  may be a hardwired data communication path made of twisted pair cable, shielded coaxial cable or fiber optic cable, for example, or may be wireless or partially wireless in nature (e.g., via transceiver  105 ). Information is transmitted via the network backbone  168  between the diagnostic component  150  and the host computer  166  (e.g., and/or the control system  18 ) which are coupled to the network backbone  168 . The communication link may support a communications standard, such as the RS232C standard for communicating command and parameter information. However, it will be appreciated that any communication link or network link such as DeviceNet suitable for carrying out the present invention may be employed. 
     Referring as well to FIG. 3, the cavitation detection system  70  may reside in the host computer  166 , for example, wherein the cavitation detection system  70  is implemented in whole or in part in software executing in the host computer  166 . In this regard, it will be appreciated that the cavitation detection system  70  may receive pressure and flow information or data from the sensors  24 ,  40 , and  38  (e.g., as well as speed information from sensor  46 ) via a data acquisition board in the host computer  166  and/or via communications from the controller  66  via the network  168 , in order to perform detection and/or diagnosis of cavitation in the pumping system  12 . 
     Referring also to FIGS. 4 and 5, the cavitation detection system  70  according to the invention may comprise a classifier system such as a neural network  200  for detecting pump cavitation according to flow and pressure data. The classifier neural network  200  receives flow and pressure signals from flow and pressure sensors  38 ,  40 , and  24  associated with the pumping system  12  of FIGS. 1-3, which are then used as inputs to the neural network  200 . The network  200  processes the pressure and flow information or data and outputs a cavitation signal  72 , which indicates the existence of cavitation. In addition, the signal  72  may classify the extent of cavitation in the pump  14 . The neural network  200  may, but need not, receive motor (pump) speed information from the speed sensor  46 , which may also be used in detecting and diagnosing the existence and extent of cavitation in the pumping system  12 . For example, the speed information from the sensor  46  may be employed by the neural network  200  in order to facilitate or improve the detection and/or diagnosis of pump cavitation where the pump  14  is driven at different speeds (e.g., via a variable frequency motor drive  60 ). It will be appreciated that while the exemplary implementations of the present invention are primarily described in the context of employing a neural network, the invention may employ other nonlinear training systems and/or methodologies (e.g., for example, back-propagation, Bayesian, Fuzzy Set, nonlinear regression, or other neural network paradigms including mixture of experts, cerebellar model arithmetic computer (CMACS), radial basis functions, directed search networks, and functional link nets). 
     Referring also to FIG. 5, the cavitation detection system  70  may further comprise a pre-processing component  202  receiving the pressure and flow data from the sensors  24 ,  40 , and  38 , respectively, which provides one or more attributes  204  to the neural network  200 , wherein the attributes  204  may represent information relevant to cavitation which may be extracted from the measured pressure, flow, and/or speed values associated with the pumping system  12 . The attributes  204  may thus be used to characterize pump cavitation by the neural network  200 . The neural network  200 , in turn, generates a cavitation signal  72  which may comprise a cavitation classification  206  according to another aspect of the invention. The neural network classifier  200  thus evaluates data measured in the diagnosed pumping system  12  (e.g., represented by the attributes  204 ) and produces a diagnosis (e.g., cavitation signal  72 ) assessing the presence and severity of cavitation in the system  12 . The neural network in this regard, may employ one or more algorithms, such as a multi-layer perception (MLP) algorithm in assessing pump cavitation. 
     As illustrated further in FIG. 6, the cavitation signal  72  output by the classifier neural network  200  is indicative of both the existence and the extent of cavitation in the pumping system  12 . For instance, the exemplary signal  72  comprises a classification  206  of pump cavitation having one of a plurality of class values, such as  0 ,  1 ,  2 ,  3 , and  4 . In the exemplary classification  206  of FIG. 6, each of the class values is indicative of the extent of cavitation in the pumping system  12 , wherein class  0  indicates that no cavitation exists in the pumping system  12 . The invention thus provides for detection of the existence of cavitation (e.g., via the indication of class values of  1  through  4  in the cavitation signal  72 ), as well as for diagnosis of the extent of such detected cavitation, via the employment of the neural network classifier  200  in the cavitation detection system  70 . It will be noted at this point that the cavitation classification  206  is but one example of a classification possible in accordance with the present invention, and that other such classifications, apart from those specifically illustrated and described herein, are deemed as falling within the scope of the present invention. 
     Referring now to FIG. 7, the exemplary neural network  200  comprises an input layer  210  having neurons  212 ,  214 ,  216 , and  218  corresponding to the suction pressure, discharge pressure, flow rate, and pump speed signals, respectively, received from the sensors  24 ,  40 ,  38 , and  46  of the pumping system  12 . One or more intermediate or hidden layers  220  are provided in the network  200 , wherein any number of hidden layer neurons  222  may be provided therein. The neural network  200  further comprises an output layer  230  having a plurality of output neurons corresponding to the exemplary cavitation classification values of the class  206  illustrated and described hereinabove with respect to FIG.  6 . Thus, for example, the output layer  230  may comprise output neurons  232 ,  234 ,  236 ,  238 , and  240  corresponding to the class values  0 ,  1 ,  2 ,  3  and  4 , respectively, whereby the neural network  200  may output a cavitation signal (e.g., signal  72 ) indicative of the existence as well as the extent of cavitation in the pumping system (e.g., system  12 ) with which it is associated. 
     In this regard, the number, type, and configuration of the neurons in the hidden layer(s)  220  may be determined according to design principles known in the art for establishing neural networks. For instance, the number of neurons in the input and output layers  210  and  230 , respectively, may be selected according to the number of attributes (e.g., pressures, flow, speed, etc.) associated with the system  70 , and the number of cavitation classes  206 . In addition, the number of layers, the number of component neurons thereof, the types of connections among neurons for different layers as well as among neurons within a layer, the manner in which neurons in the network  200  receive inputs and produce outputs, as well as the connection strengths between neurons may be determined according to a given application (e.g., pumping system) or according to other design considerations. 
     Accordingly, the invention contemplates neural networks having many hierarchical structures including those illustrated with respect to the exemplary network  200  of FIG. 7, as well as others not illustrated, such as resonance structures. In addition, the inter-layer connections of the network  200  may comprise fully connected, partially connected, feed-forward, bi-directional, recurrent, and off-center or off surround interconnections. The exemplary neural network  200 , moreover, may be trained according to a variety of techniques, including but not limited to back propagation, unsupervised learning, and reinforcement learning, wherein the learning may be performed on-line and/or off-line. For instance, where transitions between classes are continuous and differences between classes of cavitation are slight, it may be difficult to use unsupervised learning for the purpose of cavitation detection, in which case supervised learning may be preferred, which may advantageously employ back propagation. In this regard, training of the classifier may be done on a sufficient amount of training data covering many cavitation degrees (e.g., severities) and operating conditions of the pumping system. Furthermore, the training of the network  200  may be accomplished according to any appropriate training laws or rules, including but not limited to Hebb&#39;s Rule, Hopfield Law, Delta Rule, Kohonen&#39;s Learning Law, and/or the like, in accordance with the present invention. 
     An exemplary method  302  of detecting cavitation in a pumping system is illustrated in FIG. 8 in accordance with another aspect of the present invention. The various methodologies of the invention may comprise measuring pump flow and pressure data, providing the flow and pressure data to a classifier system, and detecting pump cavitation according to the flow and pressure data using the classifier system. While the exemplary method  302  is illustrated and described herein as a series of blocks representative of various events and/or acts, the present invention is not limited by the illustrated ordering of such blocks. For instance, some acts or events may occur in different orders and/or concurrently with other acts or events, apart from the ordering illustrated herein, in accordance with the invention. Moreover, not all illustrated blocks, events, or acts, may be required to implement a methodology in accordance with the present invention. In addition, it will be appreciated that the exemplary method  302  and other methods according to the invention may be implemented in association with the pumps and systems illustrated and described herein, as well as in association with other systems and apparatus not illustrated or described. 
     Beginning at  304 , pump flow and pressure sensor data are read at  306 . For example, readings may be taken at  306  from flow and pressure sensors operatively associated with the pump so as to sense at least one flow and at least one pressure, respectively, associated with the pumping system. More than one pressure reading may be obtained at  306 , such as by measuring suction pressure data and discharge pressure data associated with an inlet and an outlet, respectively, of the pumping system. In this regard, it will be appreciated that other sensor values associated with a pumping system may be measured at  306 , such as pump speed. In this manner, the cavitation may be detected and/or diagnosed at various speeds. 
     Thereafter at  308 , the measured pumping system parameters (e.g., pressures, flow, speed, etc.) are provided to a classifier system, such as a neural network. For instance, the flow and pressure data (e.g., and pump speed data) may be provided as inputs to a neural network, wherein the neural network may be trained using back propagation of other learning techniques (e.g., reinforcement learning, unsupervised learning) in either on-line or off-line learning. The neural network of the classifier system, moreover, may be trained using one or more learning rules or laws, including but not limited to Hebb&#39;s Rule, Hopfield Law, the Delta Rule, and/or Kohonen&#39;s Law. At  310 , a cavitation signal is provided by the classifier, which is indicative of cavitation in the pumping system, whereafter the method  302  returns to again measure and process flow and pressure data at  306 - 310  as described above. 
     It will be appreciated that the classifier may further diagnose the extent of pump cavitation according to the flow and pressure data. In this regard, the detection of pump cavitation at  310  according to the flow and pressure data may comprise providing a cavitation signal from the classifier system indicative of the existence and extent of pump cavitation. The method  302  may further comprise changing the operation of the pump according to the cavitation signal, such as where the cavitation signal is provided to a controller associated with the pumping system. In this manner pump cavitation and the adverse effects may be avoided or reduced in accordance with the invention. In order to ascertain the extent of pump cavitation, the cavitation signal or other output from the neural network of the classifier system, may comprise a classification of pump cavitation having one of a plurality of class values, wherein each of the plurality of class values is indicative of the extent of cavitation in the pumping system, and wherein at least one of the plurality of class values is indicative of no cavitation in the pumping system. 
     Although the invention has been shown and described with respect to certain illustrated aspects, it will be appreciated that equivalent alterations and modifications will occur to others skilled in the art upon the reading and understanding of this specification and the annexed drawings. In particular regard to the various functions performed by the above described components (assemblies, devices, circuits, systems, etc.), the terms (including a reference to a “means”) used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure, which performs the function in the herein illustrated exemplary aspects of the invention. In this regard, it will also be recognized that the invention includes a system as well as a computer-readable medium having computer-executable instructions for performing the acts and/or events of the various methods of the invention. 
     In addition, while a particular feature of the invention may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. As used in this application, the term “component” is intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and a computer. Furthermore, to the extent that the terms “includes”, “including”, “has”, “having”, and variants thereof are used in either the detailed description or the claims, these terms are intended to be inclusive in a manner similar to the term “comprising.”