Method for Determining Operational Information of a Metering Pump

The present invention relates to a method for determining operational information of a metering pump.

Metering or dosing pumps are used for feeding and dosing precise amounts of liquid. These metering pumps usually have a moveable displacement member for example in form of a membrane or piston driven by a drive motor via a drive system transferring the rotational movement of the motor into a linear movement of the displacement member.

For many applications it is desirable to determine operational information of the metering pump, e.g. to detect the presence of malfunctions or to compute an effective stroke length or other operational parameter of the pump.

EP 3 591 226 discloses a metering pump that includes a control device that is designed in such a manner that it detects the current position of the displacement element, detects the torque of the electric drive motor at several positions of the displacement element and that monitors the torque in relation to the position of the displacement Element. This prior art metering pump includes an analyzing module that either compares torque or pressure curves detected over time or that compares a detected pressure or torque curve with a previously stored sample curve.

However, it remains desirable to provide a method for determining operational information of a metering pump that is applicable to different types of metering pumps or that can at least efficiently be adapted for use with different types of metering pumps. It is also generally desirable to provide such a method that is reliable and computationally efficient and that can be configured in a cost-efficient manner.

Thus, it remains desirable to provide a method for determining operational information of a metering pump that solves one or more of the above problems and/or that has other benefits, or that at least provides an alternative to existing solutions.

According to one aspect, disclosed herein are embodiments of a method for determining operational information of a metering pump, the metering pump comprising a dosing chamber, a displacement member and a drive motor for driving the displacement member. Various embodiments of the method comprise:

The inventors have realized that operational information of a metering pump can reliably be determined from detected values of an indicator quantity indicative of a strength of activation of the displacement member at respective positions of the displacement member during operation of the metering pump, by employing a trained machine-learning model. Various embodiments of the method can efficiently be adapted for use with different types of metering pumps.

During operation of various embodiments of the metering pump, the drive motor moves the displacement member, preferably in a reciprocating manner so that the displacement member by its movement increases and decreases the volume of the dosing chamber. When the dosing chamber is filled with an incompressible liquid, the change in volume of the dosing chamber defines the delivered liquid volume. In the presence of air or cavitation, the medium inside the dosing chamber is compressible. In that case, the change in volume is different from delivered liquid volume. Generally, the delivered volume is the change in volume times the effective stroke length.

During operation of the metering pump, a pressure inside the dosing chamber or a relating indicator quantity, e.g. a relating force or torque, may be detected and recorded, and used to determine an operational condition or other operational information of the metering pump. The pressure or related indicator quantity may be measured continuously or intermittently, in particular periodically, as a sequence, in particular a time series, of detected values. The drive motor may be an electric drive motor.

A relating force or torque may be a force acting on the displacement member, in particular by the drive motor. The force or torque acting on the displacement member are related, in particular substantially proportional, to the pressure inside the dosing chamber. Therefore, while some embodiments use a detected pressure in the dosing chamber as indicator quantity, other embodiments use a detected force or torque as an indicator quantity.

For the purpose of detecting the pressure, the metering pump may comprise a pressure sensor for detecting the pressure inside the dosing chamber. In an alternative solution, the pressure may be calculated on the basis of the drive torque or force provided by the drive motor. The calculation may be based on knowledge of the mechanical connection between the drive motor and the displacement member. The drive torque or force may for example be measured by a respective torque or force sensor, respectively, or may be derived from electric values of the drive motor. Generally, further examples of an indicator quantity include a motor current, a motor voltage or another quantity derivable from, or otherwise related to, the motor current and/or the motor voltage and/or another quantity related to the motor load.

The inventors have found that a suitably trained machine-learning model may determine a variety of operational information of a metering pump from a plurality of detected values of an indicator quantity. Examples of such operational information include the identification of discrete operational states as well as the prediction of continuous-valued operational parameters. Various embodiments of the method disclosed herein allow determination of such operational information without the need for expert knowledge being applied to identify how various operational states or parameter-values are derivable from the measured indicator quantity. Accordingly, various embodiments of the method disclosed herein use different types of machine-learning models.

In particular, the operational information may include a classification of an operational condition of the metering pump, in particular a classification of an error condition or malfunction of the metering pump. The operational condition may be a current condition or even a predicted future condition, e.g. a prediction of an imminent error condition likely to occur in the near future. To this end, the machine-learning model may include a classification model trained to output an identifier of one of a plurality of discrete classes. Examples of classes may include predetermined error conditions such as “cavitation”, “air bubbles”, “leak condition”, etc. In some embodiments, the classification model may be trained to output an estimated likelihood that one or more error conditions and/or other classes of operational conditions are present or are likely to occur in the future, in particular in the near future.

Alternatively or additionally, in some embodiments, the operational information includes a value of an operational parameter. Accordingly, the machine-learning model may include a regression model trained to output a value of a continuous-valued operational parameter. Examples of operational parameters include: a discharge pressure, an effective stroke length, and a discharge flow. For example, one minus the effective stroke length is an indicator of the amount of air in the dosing chamber.

The machine-learning model receives a plurality of input values representing the detected values of the indicator quantity and/or input values derived from the detected values of the indicator quantity. To this end, the machine-learning model may be configured to receive different types of representations of the plurality of input values and/or additional input values. In some embodiments, the machine-learning model is configured to receive a plurality, e.g. a sequence or array, of input values of the indicator quantity, each of the plurality of input values being associated with a respective position of the displacement member, and wherein the machine-learning model is configured to output said operational information responsive to receiving at least said plurality of input values, e.g. in the form of pairs of input data as described below, or otherwise. The input values may be the detected values or values derived therefrom, e.g. by a noise reduction process, an averaging over multiple detected values or the like.

In some embodiments, e.g. when the input values always represent values of the indicator quantity at the same respective predetermined positions of the displacement member, or at predetermined times during the cyclic movement of the displacement member, a one-dimensional representation of the indicator quantity may be used as input data for the machine-learning model, thus allowing for a memory-efficient representation, which may be particularly beneficial especially for embedded implementations. Accordingly, in some embodiments, each of the plurality of input values is associated with a respective predetermined position of the displacement member and/or with a respective predetermined time during the cyclic movement of the displacement member. Hence, the input values represent a sequence of values where the sequence has a predetermined phase-relationship with the cyclic movement of the displacement member.

In other embodiments, the machine-learning model is configured to receive a plurality of pairs of input data, each pair of input data comprising a position of the displacement member and a corresponding value of the indicator quantity at said position, and wherein the machine-learning model is configured to output said operational information responsive to receiving said plurality of pairs of input data. Accordingly, detected values at varying positions may be used.

In the above and other embodiments, the machine-learning model may thus receive a representation of a so-called pressure-stroke curve relating the pressure or similar indicator quantity with the current position of the displacement member. The pressure-stroke diagram may be represented as a closed curve in a pressure-position coordinate system. Alternatively the pressure-stroke diagram may be represented as an open curve representing the pressure (or other indicator quantity) as a function of the time or phase along the cyclic movement of the displacement member.

In some embodiments, the machine-learning model may receive a two-dimensional array of input values, the two-dimensional array representing a pressure-stroke diagram, e.g. an array of image pixels representing an image of a pressure-stroke curve. For example, the input may be represented as a raster image of a representation of the pressure-stroke diagram. Each pixel is represented as a number between 0 and 1 indicating black level of the pixel. Other embodiments may use a different representation of the pressure-stroke diagram, e.g. a more compact representation.

To this end, for the purpose of detecting the position of the displacement member, the metering pump may comprise a suitable mechanism for detecting the current position of the displacement member. For example, the metering pump may comprise a position sensor or the drive motor may be a stepper motor such that the position can be determined by counting the rotational angle of the drive motor.

Accordingly, in some embodiments, the method comprises:

While some metering pumps allow determination of the position of the displacement member during operation, other types of metering pumps do not provide this information. It would thus be desirable to provide a method that is applicable to a wider range of metering pumps. To this end, in some embodiments, the machine-learning model is configured to receive a time series of detected values of the indicator quantity at respective points in time and to output said operational information responsive to receiving said time series of detected positions of the detected values of the indicator quantity. In particular, in some embodiments, the machine-learning model is configured to output said operational information based only on the received said time series of detected positions of the detected values of the indicator quantity. It will be appreciated that, when the cycle time of the movement of the displacement member, i.e. of the stroke cycle, is known and when the position of the displacement member is known for a reference time, the representation of the input data as a time series carries the same information as a representation of the indicator quantity as a function of position. However, as mentioned above, this information may not be readily available for all types of pumps. Nevertheless, the inventors have realized that a suitably trained machine-learning model may determine useful operational information from the indicator quantity alone, i.e. without the need of measuring the position of the displacement member.

In particular, in some embodiments, the machine-learning model includes a first machine-learning model and a second machine-learning model, the first machine-learning model being configured to compute, based on the received time series of detected values of the indicator quantity at respective points in time during the operation of the metering pump, an input representation of a plurality of input values of the indicator quantity, each input value being indicative of a value of the indicator quantity at a respective, in particular at a respective predetermined or otherwise known, position of the displacement member; the second machine-learning model may thus be configured to output the operational information responsive to receiving the computed input representation. In particular, the first machine-learning model may be trained to determine a phase and/or period of the cyclic motion of the displacement member from the time series of detected values of the indicator function. To this end, the first machine-learning model may receive pressure values or values of another indicator quantity obtained during a time window, which may have an unknown starting point relative to the stroke cycle of the pump and/or an unknown length relative to the stroke cycle of the pump. The first machine-learning model may output pressure values (or, if the first machine-learning model receives values of another indicator quantity, values of said another indicator quantity) for a time window corresponding to one stroke of the pump, the time window starting at a predetermined point of the stroke cycle, e.g. the bottom dead point. The second machine-learning model may then receive the pressure (or other indicator quantity) values corresponding to a single stroke as its input. Alternatively, the first machine-learning model may output pressure values (or, if the first machine-learning model receives values of another indicator quantity, values of said another indicator quantity) for a time window corresponding to a predetermined number of strokes or otherwise of a predetermined duration relative to the duration of a stroke cycle. The second machine-learning model may thus receive the pressure values or other indicator quantity values corresponding to said predetermined duration.

Providing separate first and second machine-learning models may result in a more compact, memory-efficient representation of the overall model. In particular, when the machine-learning model includes multiple models, e.g. for detecting respective operational conditions or for estimating respective parameters, the machine-learning model may include a single first machine-learning model performing the phase detection of the time series, and a plurality of second machine-learning models, each receiving the output of the first machine-learning model as an input. However, it will be appreciated that, in other embodiments, the first and second machine-learning models may be combined into a single machine-learning model, which may thus receive a time series of values of the indicator quantity where the time series has an unknown phase shift and/or an unknown duration relative to the cyclic motion of the displacement member. The combined machine-learning model may be trained to output the operational information directly from said time series of unknown phase and/or duration. Accordingly, the training set for such a combined machine-learning model may include input time series of different relative phase shifts and/or durations relative to the cyclic motion of the displacement member.

The present disclosure relates to different aspects including the method described above and in the following, corresponding apparatus, systems, methods, and/or products, each yielding one or more of the benefits and advantages described in connection with one or more of the other aspects, and each having one or more embodiments corresponding to the embodiments described in connection with one or more of the other aspects and/or disclosed in the appended claims.

In particular, according to one aspect, disclosed herein are embodiments of a computer-implemented method for creating a trained machine-learning model. The method comprises:

Generally, for the purpose of the present disclosure, the term “trained machine-learning model” refers to a machine-learning model having a set of parameters, such as weights, that have been adapted based on a set of training data using a suitable training algorithm, such as an unsupervised or a supervised training algorithm. Similarly, the term “training a machine-learning model” refers to the process of adapting the machine-learning model based on the training data. In particular the training, in particular the adaptation of the model parameters of the machine-learning model, may be performed using supervised learning based on a set of training data where each training data item is labelled by a corresponding target output.

Various embodiment of the method disclosed herein may be computer-implemented. Accordingly, disclosed herein are embodiments of a data processing system configured to perform the steps of the method described herein. In particular, the data processing system may have stored thereon program code adapted to cause, when executed by the data processing system, the data processing system to perform the steps of the method described herein. The data processing system may be embodied as a single computer or other data processing device, or as a distributed system including multiple computers and/or other data processing devices, e.g. a client-server system, a cloud based system, etc. The data processing system may include a data storage device for storing the computer program and detector data. The data processing system may include a communications interface for receiving the detected values and/or other types of sensor data. In some embodiments, the data processing system may partly or completely be embodied as a suitably programmed or otherwise configured processing unit, e.g. a control device for controlling operation of a metering pump. Accordingly, a part of the data processing system or the whole data processing system may be accommodated in a housing of the metering pump, e.g. as part of the control device for controlling operation of a metering pump or as a separate processing unit. Alternatively or additionally, the data processing system may include one or more data processing apparatus external to the metering pump. The data processing system may receive the detected values of an indicator quantity from the metering pump or otherwise, e.g. from a separate pressure sensor.

According to one aspect, disclosed herein are embodiments of a metering pump. Various embodiments of the metering pump comprise a displacement member, a drive motor for driving the displacement member, and a data processing system as disclosed above and in the following. In particular, the metering pump may comprise a processing unit, which may be integrated into or separate from a control device configured to control operation of the metering pump; the processing unit may be configured to perform the steps of the method described herein. The processing unit of the pump may perform an embodiment of the process described herein alone as a stand-alone device or as part of a distributed data processing system, e.g. in cooperation with an external data processing system such as with a portable data processing device and/or with a remote host computer and/or with a cloud-based architecture. The processing unit may be separate from or partially or completely be integrated into a control device for controlling operation of the pump. The pump may further include an integrated sensor configured to measure the indicator quantity or a quantity from which the indicator quantity can be derived.

According to another aspect, disclosed herein are embodiments of a system, the system comprising a metering pump and a data processing system as disclosed herein, wherein the metering pump comprises a dosing chamber, a displacement member, a drive motor for driving the displacement member. In some embodiments, the data processing system is separate from the metering pump and comprises an interface for receiving a plurality of detected values of an indicator quantity indicative of a strength of activation of the displacement member at respective positions of the displacement member during operation of the metering pump. Alternatively, the metering pump comprises the data processing system.

Yet another aspect disclosed herein relates to embodiments of a computer program configured to cause a data processing system to perform the acts of the method described above and in the following. A computer program may comprise program code means adapted to cause a data processing system to perform the acts of the method disclosed above and in the following when the program code means are executed on the data processing system. The computer program may be stored on a computer-readable storage medium, in particular a non-transient storage medium, or embodied as a data signal. The non-transient storage medium may comprise any suitable circuitry or device for storing data, such as a RAM, a ROM, an EPROM, EEPROM, flash memory, magnetic or optical storage device, such as a CD ROM, a DVD, a hard disk, and/or the like.

As an example of a dosing or metering pump,schematically shows a membrane pump. It has to be understood that the invention may be carried out in a similar manner with other types of dosing pumps, for example with a metering or dosing pump using a piston as a displacement member instead of a membrane. The pump as shown inhas a pump or dosing chamber, a side wall of which is formed by a membrane. This membraneis a displacement member. By displacement of the membranethe volume inside the dosing chambercan be increased for filling the dosing chamberand decreased for discharging the liquid from the dosing chamber. At the lower side of the dosing chamberthere is arranged a suction valvewhereas on the opposite side there is arranged a pressure valve. Both valves are designed as check valves. In this example, ball shaped valve elements are closing the valve by gravity. However, additionally a biasing element, such as a spring, can be provided. During operation, liquid is sucked from a liquid containervia a suction linethrough the suction valveinto the dosing chamberand discharged out of the dosing chamberthrough the pressure valve. From the pressure valvethe liquid is discharged via a pressure lineand a pressure loading valvefor example into a pipeof a facility. The pressure loading valvein the pressure linedefines the pressure in the pressure line, i.e. maintains the pressure on the outlet side of the pressure valveat a predefined pressure. This pressure is set by the pressure loading valve. Connected to the supply lineis a pulsation damperfor equalizing a pressure pulsation occurring in the outlet or pressure line.

The membraneis moved in reciprocating manner via the connection rod. For driving the connection rodin reciprocating manner there is provided a drive motor, in particular an electric drive in form of an electric drive motor, for example a stepper motor. The rotating drive motormoves the connection rodvia an eccentric drivetransferring the rotational movement into a linear reciprocating movement. The eccentric driveis coupled to the electric drive motorvia a gear drive. The connection rodis connected to the eccentric driveat a connection pointwhich is distanced from the rotational axis x of the eccentric driveby the eccentricity e. This causes the linear movement of the connection rodinto direction S if the eccentric driveis rotated in the rotational direction R. In this example, furthermore, a springis arranged in the drive. The springis a compression spring connected to the connection rodsuch that the springis compressed when the connection rodis moved backwards into direction Smoving the membranein the retracted position. The springcan accumulate energy during the suction stroke. This energy is released during the pressure stroke when the connection rodtogether with the membraneis moved in the forward, i.e. advanced position in the direction S. By this the springsmooths the torque to be applied by the electric drive motorduring the entire stroke. It has to be understood that it is also possible to arrange a spring being compressed during the pressure stroke and acting as a return spring. Furthermore, the invention may also be realized without a spring.

The dosing pump has a control devicecontrolling the electric drive motor. The control devicecomprises a monitoring modulefor monitoring the operation of the dosing pump. The control devicemay comprise usual electronic components like, in particular, a CPU or other processing unit, a storage device and one or more software applications for control of the dosing pump. The software applications may be stored on the storage device and be for execution on the CPU. The monitoring modulemay preferably be realized as a software module. In this example, the monitoring moduleis integrated into the control device. However, it would be possible to transfer information to an external computing or monitoring device, in particular a cloud device acting as a monitoring module. For this the control devicemay comprise a communication interfacefor wired and/or wireless communication.

The monitoring moduleis configured to continuously or intermittently record a pressure P inside the dosing chamberand the position of the displacement member. The pressure inside the dosing chamberand the position of the displacement member, e.g. the membraneof the membrane pump of, may be recorded as a representation of a pressure-stroke curve in a pressure-stroke diagram. For detecting the position of the membranealong the direction S, in this example, an encoderdetecting the angular position of the rotor of the drive motoris used. Furthermore, it is possible to detect certain positions of the drive or the displacement member, for example by a single sensor and to calculate the further positions on basis of the known velocity of the displacement member and the time past. Furthermore, instead of a special encoder, a stepper motor may be used. In knowledge of the transmission ratio of the gear driveand the geometrical design of the eccentric drivebased on the angular position, the position in direction S can be calculated. The pressure P inside the dosing chambermay either be detected by a pressure sensoror indirectly by detecting the torque of the drive motoror a force acting in the drive and calculating the pressure P on the basis of the force F acting onto membrane, or otherwise. In this example, a pressure sensoris arranged at the dosing chamberand connected to the control device. In case that a force or torque is detected as an indicator quantity instead of the pressure, it is possible to continuously record this force or torque over the position of the displacement member instead of recording the pressure, as the pressure is related to, in particular proportional to, the force or related to the torque, in particular to the torque multiplied by a term that depends on the position of the eccentric drive.

The control devicefurther comprises a processing moduleconfigured to implement a trained machine-learning model. The processing modulemay e.g. be implemented as a software module executed by the CPU of the control device, or otherwise. The trained machine-learning module may comprise a suitable representation of the model structure and of parameter values of the model parameters, e.g. of the weights of a neural network model. In this example the processing module, including the trained machine-learning module, is integrated into the control device. However, it would be possible to implement the processing moduleand/or the machine-learning module on a data processing system external to the control device of the pump. To this end, the control devicemay exchange information with an external data processing system, e.g. a cloud computing architecture, that functions as a processing module and/or implements a machine-learning module. During operation, the processing modulereceives recorded pressure values and associated positions of the displacement member from the monitoring module. The processing moduleoptionally processes the received information and feeds it into the trained machine-learning module, which in turn returns corresponding operational information as described herein. The processing module may be configured to display the information on a display of the control unit and/or raise an alarm in case of a detected fault condition and/or forward the information and/or any raised alarms to an external device or system via the communication interface, and/or the like. An example of a process performed by the processing moduleand the machine-learning module will be described in more detail below. The trained machine-learning module may be commissioned with the control deviceduring installation or it may subsequently be loaded onto the control device, e.g. via communication interface.

It will be appreciated that embodiments of the method disclosed herein may also be implemented to compute operational information of other types of metering pumps. The machine-learning model may be integrated into such a pump or implemented by an external computing device, e.g. by a separate control unit that may be communicatively coupled to the pump and/or to a sensor configured to measure an indicator quantity. Yet further, the machine-learning model may be implemented by a remote data processing system, e.g. as a cloud service, configured to receive the indicator quantity or quantities from the pump and/or from a separate sensor, and compute the operational information as described herein.

As will further be discussed below, some embodiments of a metering pump may not be capable of monitoring the position of the displacement member. Such pumps may only be capable of monitoring the pressure in the dosing chamber or another, related indicator quantity. Accordingly, in such embodiments the machine-learning model may receive the pressure or other indicator quantity as its only input.

schematically shows an example of a pressure-stroke diagram depicting a pressure-stroke curve as can be detected by the monitoring modulein general, or otherwise. The abscissa shows the stroke lengths S in percent, i.e. the linear movement of the membranebetween its position representing the minimum volume of the dosing chamberand the position defining the maximum volume of the dosing chamber. The ordinate shows the pressure Pas detected by the pressure sensor. A stroke of 0 percent corresponds to the lower dead centerand the stroke length of 100 percent corresponds to the upper dead center. The curve illustrates four phases of the membrane movement. The lower portion of the curve represents the suction phase, the portion with rapidly increasing pressure on the left side represents the compression phase, the upper portion represents the discharge phaseand the right portion with rapidly decreasing pressure represents an expansion phase, in which the internal pump volume is expanded. The expansion phasetogether with the suction phasecorresponds to a movement of the membranein the direction S, whereas the compression phaseand the discharge phaseform the pressure stroke in direction S.

When the monitoring moduleof the control devicecontinuously or intermittently records or monitors the pressure and associated displacement values, changes in the pressure-stroke curve over time or over several strokes can be detected by the monitoring device. Different problems or malfunctions which may occur in the dosing pump have different effects on the course of the curve in the pressure-stroke diagram.

illustrates examples of pressure-stroke curves of dosing pumps in the presence of different malfunctions or other operational conditions, such as cavitation, the presence of air bubbles, etc. It will be appreciated that other indicator quantities, such as force or torque, may be represented in dependence of stroke length in a similar manner, thus relating in a different form of indicator-stroke diagrams, which may be used to detect operational conditions and estimate operational parameters in a similar manner.

Various embodiments of the method disclosed herein provide an efficient way of reliably detecting such problems or malfunctions from the recorded pressure, or other indicator quantity, and displacement values.

schematically illustrates a flow diagram of a training method for creating a trained machine-learning model for subsequent use in a method for determining operational information of a metering pump.

Initially, the process obtains a set of training data items. To this end, in step S, the process obtains a set of input sequences. Each input sequence represents values of an indicator quantity indicative of a strength of activation of a displacement member of a metering pump at respective positions of the displacement member during operation of said metering pump. For the purpose of the following description, embodiments of the methods and apparatus disclosed herein will mainly be described with reference to pressure as indicator quantity. However, it will be appreciated that the various methods and apparatus may use other indicator quantities instead or in addition to pressure.

The input sequence may represent a time series of measured pressure values P(t), P(t), . . . , P(t) where the times t, . . . , tare respective times during at least one stroke cycle of a metering pump. The number n, n>1, of recorded values may depend on the rate at which the metering pump records the values. Alternatively, the input sequence may represent a sequence of pairs of pressure and position data: (P, S), (P,S), . . . , (P,S), where each pair (P,S) represents a pressure and a corresponding position, in particular the position of the displacement member at which the pressure was measured. It will be appreciated that, e.g. when the measured pressure values always represent pressure values at respective predetermined positions, the positions Smay not need to be explicitly included in the input sequence. Instead, the input sequent may be represented as a pressure vector p[i], i=1, . . . ,n, where the index i enumerates the predetermined positions S. It will further be appreciated that other embodiments may use other representations of an input sequence, e.g. use a measured torque or force instead of the pressure. In one embodiment, the input sequence may be represented as an array of values/data pairs. In other embodiments, the input sequence may be represented in a different manner, e.g. as an image of a pressure-stroke curve where the pressure-stroke curve represents a sequence of pairs of pressure and associated position data.

The input sequences may be obtained by operating a plurality of metering pumps under various operational conditions and by recording pressure and/or position data from the corresponding sensors of the pump. For example, the recorded pressure and/or position data may be obtained by a monitoring module of a metering pump, e.g. as described in connection withabove, or otherwise. The recorded data may be received from the pump via a suitable communications interface of the pump or they may be stored locally and later retrieved from a data storage device of the pump or a separate monitoring unit.

In step S, each of the obtained input sequences are labelled with one or more target output values indicative of the operational information of the pump operating when said input sequence has been obtained.

The target output values may be obtained in a variety of ways, e.g. by manually determining an operational condition, e.g. an operational malfunction, of the pump and by assigning the target value a class identifier representing the determined operational condition. For example, some operational conditions, such as cavitation may be induced, e.g. by choking the inlet of the pump. In other embodiments, the target values may be obtained by performing reference measurements of e.g. the output pressure or effective stroke length of the metering pump. For example, target values of the effective stroke length may be determined from a flow measurement and from the pump speed and pump volume.

In any event, the process may store the obtained target output values associated with the corresponding input sequences to which they are related, thus creating a set of labelled training data items. Each training data item includes an input sequence and a corresponding target output, the input sequence being indicative of an indicator quantity indicative of a strength of activation of a displacement member of a metering pump at respective positions of the displacement member during operation of said metering pump, the corresponding target output being indicative of operational information observable during said operation of said metering pump. It will be appreciated that the training data may include multiple target values for each input sequence, e.g. an identifier for classifying the operational condition and/or respective values of one or more operational parameters. Accordingly, the training data may be used to train a single machine-learning model or different machine-learning models to output respective types of operational information.