CN119532179A

CN119532179A - Method for monitoring the operation of a compressor and its supporting equipment

Info

Publication number: CN119532179A
Application number: CN202411060338.6A
Authority: CN
Inventors: P·戈伊恩斯; M·希伦; H·范德·桑德; E·罗劳蒂
Original assignee: Atlas Copco Airpower NV
Current assignee: Atlas Copco Airpower NV
Priority date: 2023-08-31
Filing date: 2024-08-05
Publication date: 2025-02-28
Also published as: BE1031930A1; TW202526175A; WO2025046530A1; BE1031930B1

Abstract

The present disclosure relates to a method for monitoring the operation of a compressor and its supporting equipment. According to one embodiment, a method for monitoring the operation of a compressor is disclosed, the method comprising iteratively performing the following steps: measuring (107) a process quantity indicative of the instantaneous operation of the compressor; and using a model to estimate (102) the process quantity of the compressor based on one or more setting parameters (101) of the compressor; comparing the estimated process quantity with the measured process quantity (103), thereby obtaining an instantaneous performance indicator (201); characterized in that the method further comprises the following steps: determining a degradation parameter (108) based on a series of continuous instantaneous performance indicators; and wherein the estimation (102) is further performed based on the performance indicator (108); and reporting the operation based on the degradation parameter (108).

Description

Method for monitoring operation of compressor and supporting equipment thereof

Technical Field

The present invention relates to a method for monitoring a compressor and a support device for such a compressor.

Background

Compressors are mechanical machines designed to provide a gas (such as ambient air) at a relatively high pressure for applications in industrial processes and/or medical departments. Depending on the desired pressure, desired application, desired results, and other boundary conditions, there are various compressor technologies available, such as axial and centrifugal compressors, and oil-free and oil-lubricated compressors.

Further, the compressor may be equipped with peripheral devices, also referred to as support devices, such as coolers, oil separators, filters or air filters, dryers, drive motors, etc.

In addition, there may also be cooling and/or lubrication circuits with a coolant and/or lubricant other than oil, such as water. When referring to oil as the coolant and/or lubricant, it should be understood that this may also include the use of another type of coolant and/or lubricant, such as water.

In addition, it is apparent that in most industrial environments and/or medical sectors, high availability of compressors is desired without necessarily compromising performance. However, like most mechanical machines, some form of degradation will inevitably occur over time due to the presence of rotating components, contamination in the ambient air surrounding the machine, exposure to large temperature differentials, loss of oil, and other internal or external effects that may prevent and/or degrade the normal operation of the compressor, etc.

Thus, there is a need for a method of monitoring the operation of a compressor and, if necessary, the operation of peripheral devices supporting the compressor, if any, so as to be able to alert or at least inform an operator, fleet manager, etc. that normal operation is compromised or that it is less relaxed and less optimal.

It is therefore an object of the present invention to provide a method for monitoring a compressor and a support device for such a compressor.

Disclosure of Invention

According to the present invention the above identified object is achieved by providing a computer implemented method for monitoring the operation of a compressor and/or supporting equipment thereof according to the first aspect of the present invention, which method comprises iteratively performing the steps of measuring one or more process quantities indicative of the instantaneous operation of the device and/or the compressor, estimating the one or more process quantities of the device and/or the compressor based on one or more set parameters of the compressor using a model, comparing the estimated process quantities with the measured process quantities, thereby obtaining an instantaneous performance indicator, characterized in that the method further comprises the steps of determining a degradation parameter based on a series of consecutive instantaneous performance indicators, and wherein the estimation is further performed based on the degradation parameter, and then reporting the operation based on the degradation parameter.

A measurement step is defined as quantitatively determining the quantity obtained from one or more observations, recordings or samplings at a particular measurement location by means of a measuring instrument adapted to take measurements, such as a sensor for representing the observed quantity as a number with associated units that can be compared with other values of the same quantity.

The estimating step is defined as determining a value of a quantity based on the measured process quantity and/or the setting parameter using a scientific model representing the technical process and/or the apparatus, with the measured process quantity and/or the setting parameter as input and with the value that needs to be determined based on one or more calculations and thus estimated as output.

In a first step, a process quantity indicative of instantaneous operation of the compressor and/or the apparatus is measured. These process quantities are the temperature of the gas, the current of the compressor drive motor, the rotational speed of the rotating parts of the compressor, the inlet pressure of the compressor, the outlet pressure of the compressor, the ambient pressure surrounding the compressor, the humidity level of the gas, the flow rate of the gas and/or the valve position. It should also be noted that this list is not exhaustive and is therefore not limited to the amounts mentioned. On the other hand, it should also be noted that not all process quantities are measured, but some of them.

In a second step, one or more process quantities of the device and/or the compressor are estimated. The estimation is based on one or more setting parameters of the compressor (used as inputs to a scientific model of the compressor), optionally in combination with the apparatus, wherein the output of the model is an estimated process quantity. According to one embodiment, the estimation may also be made based on the measured process quantity, in other words, other process quantities may be derived based on the measured process quantity. Note that, therefore, the estimated process quantity is calculated as already mentioned.

The model used is initially a model of the compressor and/or support device without defects, in other words of an ideal machine as a compressor and/or support device.

In this text, machines, compressors and/or equipment will be mentioned later, but it is further noted that these terms are interchangeable for the purpose of discussing the present invention. When referring to the term machine, it may refer to a compressor, a support apparatus, or a combination of both.

The model representing the machine is a scientific model, such as a physical model or a multi-physical model, comprising a set of differential equations and/or empirical relationships describing the different physical parts of the machine and related to each other by one or more common variables.

It should also be noted that the first step and the second step may be performed simultaneously in parallel. Thus, the terms "first" and "second" are used to distinguish between different steps without indicating a particular temporal order and/or hierarchy between the two steps.

The measured process quantity is then compared with the estimated process quantity. Because the process quantity is initially estimated using a model of the ideal machine (i.e., a machine without any defects), the difference (also referred to as delta) between the measured process quantity and the estimated process quantity will in principle be indicative of the deviation behavior between the ideal or healthy machine and the actual machine. The greater the difference, the greater the deviating behaviour of the machine compared to a healthy machine.

The difference between the measured process quantity and the estimated process quantity is further referred to as a performance index, since it is in principle an indication of the performance of the machine compared to an ideal machine. Thus, the calculated performance index at the specific time point is the instantaneous performance index at the specific time point.

The step of estimating the process quantity is performed iteratively with the step of measuring the process quantity, in other words, the steps are repeatedly performed, resulting in a series of consecutive instantaneous performance indicators.

According to the new innovative concept of the present invention the computer implemented method further comprises the step of determining a degradation parameter based on a series of consecutive instantaneous performance indicators and further estimating based on the degradation parameter.

In principle, likewise, a series of consecutive instantaneous performance indicators calculated at a series of consecutive points in time is an indication of the deviating behaviour of the machine compared to a healthy machine in principle, but in this case not instantaneous but within a certain period of time. Thus, the period of time includes successive points of time. For example, if the series of instantaneous performance indicators represents a linear downward trend, this indicates that the deviation behavior is in a gradual downward trend, by definition. The instantaneous performance index is then defined as the difference between the estimated value on the one hand and the measured value on the other hand. As a result, the performance index will decrease with increasing degradation. This gradual decrease in deviation behavior further indicates decay, aging or degradation of the machine. The degradation parameters defined above are determined or derived based on the series of consecutive instantaneous performance indicators.

In other words, in order to determine the degradation parameter, a certain amount of data is analyzed within a predefined limited period of time, in which the degradation parameter is assumed to be constant. The batch of data is made up of a series of consecutive instantaneous performance metrics, further reducing the impact of potential noise on the data. Over a longer period of time, both the performance index and the degradation parameter will show a tendency to decrease or increase according to their definition.

Note that aging, decay or degradation of the mechanical machine, although undesirable, is unavoidable even under ideal conditions and will occur from the moment it is put into use and will occur all the time during operation. The degradation is caused by friction of the moving parts, corrosion of the material, deformation such as fatigue or creep of the material, metal fatigue or permanent deformation, external factors such as contamination and the presence of dust particles, seasonal temperature differences, oil losses and/or other factors known to the skilled person.

In such a case, a series of instantaneous performance indicators will gradually decrease according to their definition. On the other hand, the series may also have abrupt or abrupt processes, so that large differences can be noted throughout the series. This may indicate that there is a defect or failure that deviates from the normal degradation of the machine.

Subsequently, an estimation of the process quantity will also be made based on the determined degradation parameters. Finally, the operation of the compressor and/or the support device may then be reported based on the degradation parameters.

An advantage of this method for monitoring the operation of the compressor and/or supporting machine is that in this way undesired but natural or expected degradation of the machine in normal operation is taken into account. This provides a more accurate and precise picture of the actual operation of the machine.

On the other hand, as already mentioned, a sudden change will in turn indicate an acute defect or failure of the machine. Also in this case, the machine operator will get a better picture of the operation of the compressor and/or supporting equipment.

According to one embodiment, the method further comprises the step of updating the model based on the degradation parameters.

The model initially representing the ideal machine will also include parameters that cause the model to become representative of the machine that is already in operation. This results in a model that is more representative of the actual machine. As a result, the estimated value of the process quantity is closer to the actual value.

According to one embodiment, the model may also contain a parametric model comprising one or more model parameters such as heat transfer coefficients, efficiency parameters, temperature correction parameters, cooling parameters, speed correction parameters, friction parameters, or any other parameters suitable for modeling a machine.

The parametric model comprises a set of model parameters linked to each other directly or indirectly via a set of variables. The input to the parametric model is then a selection of set parameters of the machine and the measured process quantity corresponding to the variables of the model, after which the process quantity can be estimated based on the model parameters. In addition, some of the measured process quantities may be used to verify the output of the model. Then, the step of updating the model is performed by updating the model parameters.

According to one embodiment, the computer-implemented method for monitoring the operation of the compressor and/or the support device further comprises the step of deriving a health indicator based on the model parameters.

The parametric model, which includes model parameters, describes the operation of the machine. Because the model parameters are iteratively updated to conform the estimates to the measurement-based observations while monitoring the operation of the machine in accordance with the described method, these model parameters also represent its operation. The health indicator then represents the set of model parameters in at least a single value, whereby the health indicator is representative of the operation of the compressor and/or the support device. In other words, the at least one value (health indicator) is indicative of a condition or operation of the compressor and/or the support device. Alternatively, multiple health indicators may be derived based on multiple partial selections of model parameters. Furthermore, it is noted that the health index already takes into account possible degradation of the machine. Thus, the health indicator is a correct representation of the actual machine condition and thus not the ideal machine. Furthermore, as mentioned above, these steps will further limit the possible impact of noise on the measurement.

In an optional step, a health indicator may also be reported.

According to one embodiment of the invention, the method further comprises the step of limiting the range of setting parameters of the compressor based on the degradation parameters.

If it can be inferred from the degradation parameters that the machine is working suboptimal or there is a risk that the machine will work suboptimal, this can already be expected by limiting the range of the setting parameters. This prevents the risk of sudden malfunctions based on setting parameters that may have a negative impact on the further operation of the machine, and also prevents any further damage to the machine in case the machine continues to operate. These set-up parameters include the pressure, flow rate, optional humidity level and/or power of the compressor or other possible set-up parameters.

Another advantage is that the usability of the machine can be optimized. Although the machine will then run sub-optimally, it will be ensured that it does remain operational. This can then be further expected by scheduling maintenance in time. In addition, the operation of the compressor compartment controller for controlling the compressor bank may also be optimized.

Furthermore, the method may further comprise the step of turning off the compressor when the degradation parameter exceeds a predefined value. Note that this is a limiting case that limits the range of the setting parameters, which in turn limits the range to a limit value, i.e. a value that completely prevents operation. This step will be performed, for example, if the value of the degradation parameter indicates an acute risk, or when emergency maintenance needs to be performed.

According to a second aspect of the invention, a data processing system is disclosed, comprising a processing unit configured to perform the method according to the first aspect of the invention.

According to a third aspect of the present invention, a computer program product is disclosed, the computer program product comprising computer executable instructions for performing the method of the first aspect when the program is executed on a computer.

According to a fourth aspect of the present invention, a computer readable storage device embodying the computer program product of the third aspect is disclosed.

According to a fifth aspect of the present invention, a compressor comprising a data processing system according to the second aspect of the present invention is disclosed.

According to a sixth aspect of the present invention, a method of monitoring the operation of a compressor and/or a supporting device thereof is disclosed, the method comprising iteratively performing the steps of measuring one or more process quantities indicative of the instantaneous operation of the device and/or the compressor, and estimating the one or more process quantities of the device and/or the compressor based on one or more set parameters of the compressor using a model, comparing the estimated process quantities with the measured process quantities, thereby obtaining an instantaneous performance indicator, characterized in that the method further comprises the steps of determining a degradation parameter based on a series of consecutive instantaneous performance indicators, and wherein the estimating is further performed based on the degradation parameter, reporting the operation based on the degradation parameter.

Further, one or more process quantities may be estimated based on the measured process quantities.

Furthermore, the method comprises the step of updating the model based on the degradation parameters.

The process quantity includes one or more of the group of temperature, flow, velocity, inlet pressure, outlet pressure, ambient pressure, humidity, flow rate, and/or valve position.

The model may include a parametric model that includes one or more model parameters. Thus, the updating can be performed by updating the model parameters. Furthermore, the method may comprise the step of deriving the health indicator based on model parameters. Thus, the model parameters may include one or more of the group of heat transfer coefficients, efficiency parameters, temperature correction parameters, cooling parameters, speed correction parameters, friction parameters.

Further, the method may include the step of limiting a range of setting parameters of the compressor based on the degradation parameter. Thus, the set parameters may include one or more of the group of pressure, flow rate, humidity level, power.

Furthermore, the method may comprise the step of turning off the compressor when the degradation parameter exceeds a predefined value.

Drawings

The present invention will be further illustrated with reference to the accompanying drawings, wherein,

FIG. 1 schematically illustrates a method for calculating an instantaneous performance index of a compressor and/or a support device;

FIG. 2 schematically illustrates a method for monitoring operation of a compressor and/or a support device in accordance with one embodiment of the invention;

FIG. 3 illustrates a physical model representing operation of a compressor and a support device;

FIG. 4 illustrates another model representing operation of the compressor and support apparatus;

FIG. 5 illustrates a schematic diagram of a closed cooling circuit including oil and a warming of the oil;

FIGS. 6A-6C illustrate the results of regression training for determining parameters and/or coefficients in the model as illustrated in FIG. 3;

7A-7B illustrate the result of adjusting the contamination parameters in the model as illustrated in FIG. 3 to match its estimate to the measured value, wherein the cooling circuit has a contamination level of 75%;

8A-8B illustrate the result of adjusting the contamination parameters in the model as illustrated in FIG. 3 to match its estimate to the measured value, wherein the cooling circuit has a contamination level of 92%;

9A-9B illustrate the result of adjusting the contamination parameters in the model as illustrated in FIG. 3 to match its estimate to the measured value, wherein the cooling circuit has a contamination level of 94.8%;

FIG. 10 illustrates pollution parameters adjusted as a function of pollution level in the diagrams of FIGS. 7-9, and

FIG. 11 illustrates determining a healthy area based on contamination parameters as a function of contamination level.

Detailed Description

The present invention will be described with respect to certain embodiments and with reference to certain drawings but the invention is not limited thereto and is only determined by the claims. The drawings described are only schematic and are non-limiting. In the drawings, the size of some of the elements may be exaggerated and not drawn on scale for illustrative purposes. The dimensions and relative dimensions do not necessarily correspond to actual practical embodiments of the invention.

Furthermore, the terms first, second, third and the like in the description and in the claims, are used for distinguishing between similar elements and not necessarily for describing a sequential or chronological order. The terms are interchangeable under appropriate circumstances and the embodiments of the invention can be practiced in other than described or illustrated herein.

In addition, for purposes of illustration, the terms "top," "bottom," "upper," "lower," and the like are used throughout the specification and claims and are not necessarily used to describe relative positions. The terms so used are interchangeable under appropriate circumstances and the embodiments of the invention described herein are capable of operation in other orientations than described or illustrated herein.

Furthermore, the various embodiments, while referred to as "preferred embodiments," are to be construed as exemplary ways of practicing the invention, and are not intended to limit the scope of the invention.

The term "comprising" as used in the claims should not be interpreted as being limited to only the means or steps described below, but rather the term does not exclude other elements or steps. The terms should be interpreted as specifying the presence of the stated features, elements, steps or components as referred to, but not excluding the presence or addition of one or more other features, elements, steps or components, or groups thereof. Thus, the scope of the expression "a device comprising components a and B" should not be limited to a device consisting of only components a and B. It is meant that for the purposes of the present invention, only components a and B of the device are listed and the claims are to be further construed as including equivalents of these components as well.

Fig. 1 schematically illustrates a method for calculating an instantaneous performance index of a compressor and/or a support device. The first step includes measuring data 101 as input to a model 102 representing the compressor and/or support equipment. For example, model 102 is a physical model as further illustrated in fig. 3. The input includes speedAnd pressure300. Inlet temperature302, And a heat transfer coefficient UA108. Then, in 303, power P _compr may be calculated based on the speed and pressure 300. Then, on this basis, the heat generated when the compressor is operated can be calculated as Q _{heat generated} 305, for example, by taking into consideration the efficiency coefficient, as illustrated in fig. 3. The required compressor power may be determined from the compression model 308 using the air outlet temperature T _out, the air inlet temperature T _in, the air outlet pressure p _out, and the air inlet pressure p _in.

It is also understood that the above illustrated example of the physical model 102 may be replaced with other models representing compressors and/or support devices. Referring to fig. 4, which illustrates another model 400, a set of inputs 401 to 405, such as speed 401, pressure 402, inlet temperature 403, ventilation condition 404, and oil temperature 405 at a point in time, are provided so that oil temperature 407 at a subsequent point in time can be calculated using a set of equations 406.

Referring again to fig. 3 and thus to the physical model 102, reference numeral 304 includes a model for determining the cooling capacity P _cooler of the cooling circuit of a chiller that is present in an arrangement that includes a compressor. Referring to fig. 5, the cooling circuit 500 is, for example, a closed oil circuit. The oil is circulated by means of a pump 501 to one or more machine parts 502, in which heat is removed to the oil. The oil is then cooled again in an oil cooler 504, for example by means of a ventilator 505. The oil may then be further used as a lubricant 506 for machine components (such as a gearbox) and recirculated in the cooling circuit 500 using the pump 501.

The proper operation of the oil circuit 500 may be negatively affected in various ways. For example, the oil may become contaminated due to wear of moving parts in the oil circuit 500, which may clog pipes and prevent lubrication and cooling of bearings and gears. At the beginning, the oil will be very pure, but when very fine particles in the cooling air sucked in from the environment are no longer retained by the filter, the oil may become contaminated. This phenomenon may occur in oil-injected compressors. The non-oil injected compressor has a closed oil circuit, which means that oil does not flow along the compressor elements, and therefore this phenomenon is unlikely to occur. However, just like oil-injected compressors, the air side of the oil cooler 504 may also become clogged with dirt particles in the air. This phenomenon is also referred to as plugging 503 and the oil cooler 504 will run suboptimal over time. This is because heat transfer becomes more difficult, resulting in an increase in the overall temperature of the oil. This in turn leads to an increase in the temperature of the compressed air, since for example the housing also reaches a higher temperature. In addition, the lubrication conditions of the bearings and gears will be suboptimal, causing these components to further warm up, age in an accelerated manner, and jeopardize the usability of the compressor.

The closed circuit can be modeled as a system in which the parameters are only time dependent, also called a lumped system, wherein the temperature rise 509 of the oil depends on the heat absorbed 507 as a result of the loss and on the heat released 508 by the cooler and thus on the cooling capacity.

The cooling capacity is then the heat removed during operation Q _{heat removed} and is proportional to the difference between the oil temperature T _oil and the cooling air temperature T _{air inlet}, where the proportionality coefficient is equal to the heat transfer coefficient UA108 of the cooler. In addition, an additional fan status (fanstate) factor is added that considers whether forced ventilation is effective. The cooling air temperature itself can be modeled as a correction above the temperature of the compressor case. Then, the cooling capacity P _cooler corresponding to the removed heat becomes P _cooler(fanstate,UA,T_oil,T_{air inlet},...)＝Q_{heat removed} 307.

The temperature increase of the oil in the oil circuit may be described as Q _{heat generated}-Q_{heat removed}, represented by reference numeral 306. Based on the above, with an operator 308 optionally supplemented with a gain factor, the oil temperature at a certain point in time can be calculated as an output 309 of the model.

Referring again to fig. 1, the difference between the estimated value 309 and the measured value 107 may then be calculated in 103. Reference numeral 104 then includes a series of residuals.

In principle, the above mentioned gain coefficients, which represent the respective parameters and/or coefficients describing the operation of the compressor and the cooling circuit, may be determined based on a sufficient number of measurements of the new and/or healthy machine. These may then be determined, for example, via least squares regression. Fig. 6A to 6C illustrate the results of such regression training. The y-axis of the illustrated graph shows oil temperature in degrees celsius, compressor speed in revolutions per minute rpm, and inlet temperature in degrees celsius, respectively, as a function of time. It should be further noted that fig. 6B, 7B, 8B and 9B have two y-axes, wherein the right y-axis represents the outlet pressure in bar.

The above model may be used to determine the efficiency of the cooling system and/or the compressor, as long as the oil circuit can be assumed to be healthy. In this way, the measured value and the estimated value via calculation will coincide with each other. However, when the cooler starts to clog, the heat transfer coefficient UA of the cooler will decrease. By introducing an additional coefficient β _U, further referred to as a contamination parameter, this degradation may be included in the model, β _U being an example of the degradation parameter defined above, wherein the value ranges between 1 corresponding to a healthy machine and 0 where no heat transfer at all. Thus, the capacity of the cooler is P_cooler＝Q_{heat removed}＝[fanstate.β_U.β_fanon+(1-fanstate)·β_fanoff]UA(T_oil-T_{air inlet}).

Thus, the model 102 used includes a pollution parameter β _U that represents the health, or in other words the degradation, of the machine or a part thereof, such as a cooling circuit. Further, in the example of model 102, a gain coefficient mortar _n representing the coefficient of friction of the compressor and/or peripheral device may be used as an indication of health. When the compressor and/or peripheral device is put into use, it will equal the expected coefficient of friction of the new machine, but over time a significant difference will occur between the measured and estimated values, as this coefficient of friction no longer corresponds to the coefficient of friction of the new machine.

Referring to fig. 2, degradation of the compressor and/or peripheral devices and associated cooling circuits will be considered in accordance with a new and inventive method 200. As shown in fig. 1, fig. 2 shows as inputs measurement data 101, a model 102 representing the compressor and/or support equipment, a calculation of a difference 103 between a measured value 107 and an estimated value 309, and a series of residuals 104. However, unlike the method as illustrated in fig. 1, in step 201, the residual 104 is minimized by updating one or more parameters of the model 102 (such as the pollution parameter β _U and the gain coefficient α _n) with feedback 202.

Other parameters, such as those used in other aspects of the model, such as efficiency of the compressor, oil velocity, coefficient of friction, etc., may also be optionally adjusted. In other words, by adjusting parameters and/or variables in the model 102, the estimated value will be made consistent with the measured value, and thus the residual 104 will be reduced to a minimum, i.e. to a noise level.

For example, if in a simple case only the friction coefficient changes via the feedback 202, the friction coefficient may represent a monotonically increasing trend in case of natural degradation or expected degradation of the cooling circuit 500. On the other hand, a monotonically decreasing trend may also be noted based on the reference and definition of the coefficients in the model. Thus, it should be appreciated that due to the feedback 202, one or more parameters change over time.

Referring to fig. 7A, a graphical representation of oil temperature on the y-axis over time on the x-axis is given, wherein, based on model 102, the solid line represents measured value 107 and the dashed line represents estimated value. Thus, the difference between them is calculated as the error [% ], which corresponds to the residual 104. The above mentioned parameters are adjusted by feedback 202 to bring the estimated value into agreement with the measured value. Further, in fig. 7B, measured values of the velocity on the left y-axis and the exhaust pressure on the right y-axis measured at the same time point as the oil temperature measured value with the passage of time on the x-axis are shown.

In order to bring the estimated value into agreement with the measured value, the parameters are thus adjusted, resulting in a higher degree of contamination or clogging being applied to the coolers in the model. This provides insight into the degree of contamination of the cooler. Referring again to fig. 2, the contamination level 205 of the cooler may then be derived based on the feedback 202 via an additional model 204 (e.g., a regression model). Here, block 201 is a minimizer that proposes a new value of parameter 108 leading to the next iteration via feedback 202. After convergence is achieved, the result is an updated version of the converged value of the degradation parameter 203, and then 108 is the proposed value of the degradation parameter.

In fig. 7, this contamination level corresponds to 75%. In fig. 8A to 8B and 9A to 9B, further illustrations are given as in fig. 7A to 7B, wherein in fig. 8 the cooler has a contamination level of 92% and in fig. 9 the cooler has a contamination level of 94.8%.

Furthermore, it is shown in fig. 10 that the pollution parameter β _", here denoted as normalized value β _clog, has a tendency to decrease depending on the cooler pollution level. On the basis of this, it is possible to determine to what extent the parameter represents the health of the cooling circuit and thus the health of the compressor. As illustrated in fig. 11, for example, three zones are shown, a fully blocked cooler, "fully blocked", a partially blocked cooler, "partially blocked", and a healthy cooler, "healthy".

By determining the degree of contamination, maintenance can be planned more efficiently. Once the contamination level exceeds a limit value, as illustrated for example in fig. 10, the maintenance may be scheduled to ensure machine availability and efficiency. Thus, maintenance may be planned when necessary, rather than purely periodic maintenance, and thus as a precaution. Finally, the risk of failure of the oil cooler (and thus of the compressor) can also be better estimated, and if necessary the operator can be informed in time so that no unexpected failure is encountered.

Claims

1. A computer-implemented method for monitoring the operation of a compressor and/or its supporting equipment, the method comprising iteratively performing the following steps:

- measuring (107) one or more process quantities (101) indicative of the instantaneous operation of the device and/or the compressor; and

- using a model (102, 400) to estimate (102) one or more process quantities of the device and/or the compressor based on one or more setting parameters of the compressor;

- comparing the estimated process quantity with the measured process quantity (103), thereby obtaining an instantaneous performance indicator (104);

It is characterized in that

The method further comprises the following steps:

- determining (202) a degradation parameter (203) based on a series of consecutive instantaneous performance indicators (104); and wherein said estimating (102) is further performed based on said degradation parameter (108);

- reporting said operation based on said degradation parameter (108).

2. The computer-implemented method of claim 1, wherein the estimating of the one or more process quantities is further performed based on the measured process quantity (107).

3. The computer-implemented method according to any one of the preceding claims, further comprising the step of updating (202) the model based on the degradation parameter (108).

4. The computer-implemented method according to any of the preceding claims, wherein the process quantity (101) comprises one or more of the group consisting of temperature, flow, velocity, inlet pressure, outlet pressure, ambient pressure, humidity, flow rate and/or valve position.

5. The computer-implemented method of any one of the preceding claims, wherein the model (102, 400) comprises a parametric model comprising one or more model parameters.

6. A computer implemented method according to claim 5 as appended to claim 3 or 4, wherein the updating (202) is performed by updating the model parameters (305, 306, 307).

7. The computer-implemented method according to any one of claims 5 to 6, further comprising the steps of:

- deriving a health indicator (205) based on the model parameters (305, 306, 307).

8. The computer-implemented method of any one of claims 5 to 7, the model parameters comprising one or more of the group consisting of a heat transfer coefficient, an efficiency parameter, a temperature correction parameter, a cooling parameter, a speed correction parameter, and a friction parameter.

9. The computer-implemented method according to any one of the preceding claims, further comprising the steps of:

- limiting the range of compressor setting parameters based on said degradation parameter (108).

10. The computer-implemented method of claim 9, the setting parameters comprising one or more of the group consisting of pressure, flow rate, humidity level, power.

11. The computer-implemented method according to any one of the preceding claims, further comprising the steps of:

- When the degradation parameter (108) exceeds a predefined value, shutting down the compressor.

12. A data processing system comprising a processing unit configured to perform the method according to any one of the preceding claims.

13. A computer program product comprising computer executable instructions for performing the method according to any one of claims 1 to 11 when the program is executed on a computer.

14. A computer storage medium comprising a computer program product according to claim 13.

15. A compressor comprising the data processing system according to claim 12.