Method and support device for monitoring the operation of a compressor

Abstract

According to one embodiment, a method for monitoring the operation of a compressor is disclosed, comprising the steps of iteratively performing: measuring a process amount indicating instantaneous operation of the compressor (107); estimating the process amount of the compressor based on one or more setting parameters of the compressor using a model (102); comparing the estimated process amount with a measured process amount (103); and thereby obtaining an instantaneous performance index (104), wherein the method further includes the step of determining a degradation parameter (108) based on a successive series of instantaneous performance indexes, the estimation step (102) being further performed based on the degradation parameter (108), and reporting the operation based on the degradation parameter (108).

Description

[Technical Field]

[0001] The present invention relates to a method for monitoring a compressor and an auxiliary device for such a compressor. [Background technology]

[0002] A compressor is a machine designed to supply gases, such as ambient air, under higher pressure for industrial processes and / or medical applications. Depending on the required pressure, desired application, desired results, and other boundary conditions, there are various compressor technologies to choose from, such as axial flow compressors vs. centrifugal compressors, and oil-free vs. oil-lubricated compressors.

[0003] Furthermore, compressors may be equipped with peripheral devices, also known as support devices, such as coolers, oil separators, filters or air filters, dryers, and drive motors.

[0004] Furthermore, cooling and / or lubrication circuits may exist that use coolants and / or lubricants other than oil, such as water. When referring to oil as a coolant and / or lubricant, it should be understood that this may also include the use of other types of coolants and / or lubricants, such as water.

[0005] Furthermore, it is clear that in most industrial environments and / or medical fields, high availability of compressors is expected without sacrificing performance. However, as with most machinery, some form of degradation inevitably occurs over time, particularly due to the presence of rotating parts, contamination of the air surrounding the machine, exposure to large temperature fluctuations, oil loss, and other internal or external influences that may interfere with and / or degrade the proper operation of the compressor. [Prior art documents] [Patent Documents]

[0006] [Patent Document 1] German Patent Application Publication No. 102006032648 [Patent Document 2] International Publication No. 2015 / 149928 [Patent Document 3] U.S. Patent Application Publication No. 2004 / 018096 [Overview of the project] [Problems that the invention aims to solve]

[0007] Therefore, there is a need for a method to monitor the operation of the compressor, and, if necessary, the operation of peripheral equipment supporting the compressor, so that operators, fleet managers, etc., can be warned or at least notified that a proper function is at risk, or, even if it is not a strictly critical function, is no longer optimal.

[0008] Therefore, an object of the present invention is to provide a method for monitoring a compressor and an auxiliary device for such a compressor. [Means for solving the problem]

[0009] According to the present invention, the above objective is achieved by providing a computer implementation method according to claim 1 for monitoring the operation of a compressor and / or its support device, according to a first aspect of the present invention. The method comprises iteratively performing the steps of: measuring one or more process quantities that indicate the instantaneous operation of the device and / or compressor; estimating the one or more process quantities of the device and / or compressor based on one or more setting parameters of the compressor using a model; and comparing the estimated process quantities with measured process quantities to obtain an instantaneous performance indicator, wherein the method further comprises the steps of determining a degradation parameter based on a successive series of instantaneous performance indicators; the estimation being further performed based on the degradation parameter; and reporting the operation based on the degradation parameter.

[0010] The step of measurement is defined as quantitatively determining a quantity obtained from one or more observations, recordings, or samples at a specific measurement location, using an appropriate measuring instrument for that purpose, such as a sensor for representing the observed quantity as a numerical value with relevant units that can be compared with other values ​​of the same quantity.

[0011] The estimation step is defined as determining the value of a quantity, using a scientific model representing the technical process and / or apparatus, based on the measured process quantity and / or setting parameters, with the measured process quantity and / or setting parameters as input, and therefore the output being a value estimated based on one or more calculations.

[0012] In the first step, process variables indicating the instantaneous operation of the compressor and / or the device are measured. These process variables 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 of the compressor, the humidity level of the gas, the gas flow rate, and / or the valve position. Further, it should be noted that this list is not exhaustive and thus is not limited to the variables mentioned. On the other hand, it should also be noted that not all process variables are measured, but only some of them are.

[0013] In the second step, one or more process variables of the device and / or the compressor are estimated. This estimation is optionally carried out in combination with the device based on one or more setting parameters of the compressor that serve as inputs to the scientific model of the compressor, and the output of the model is the estimated process variable. According to one embodiment, the estimation can also be carried out based on the measured process variables, that is, other process variables can be derived based on the measured process variables. Therefore, as already mentioned, it should be noted that the estimation of process variables is a calculation.

[0014] The model used is initially a model of a compressor and / or a support device without defects, that is, a model of an ideal machine that is a compressor and / or a support device.

[0015] In the latter part of this text, machines, compressors, and / or devices are mentioned, and it should be further noted that these terms are interchangeable for the purpose of explaining the present invention. When referring to the term "machine", it may refer to a compressor, a support device, or a combination of both.

[0016] A model representing a machine is a scientific model such as a physical or multi-physical model that describes different physical parts of the machine and includes a set of differential equations and / or empirical relationships that depend on each other by one or more common variables.

[0017] It should be noted that the first and second steps can be performed in parallel and simultaneously. Therefore, the terms "first" and "second" used are used to distinguish between different steps, but do not indicate a specific chronology and / or hierarchy between the two steps.

[0018] Next, the measured process rate is compared to the estimated process rate. Since the model of an ideal machine is used to initially estimate the process rate, i.e., a machine without defects, the difference between the measured process rate and the estimated process rate, also called the delta, in principle indicates the deviation behavior between an ideal or healthy machine and a real machine. The larger this difference, the greater the deviation behavior of the machine compared to a healthy machine.

[0019] The difference between the measured process rate and the estimated process rate is, in principle, an indicator of the machine's performance compared to an ideal machine, and is therefore further called a performance index. The calculated performance index at a specific point in time is the instantaneous performance index at that particular point in time.

[0020] The steps of estimating the process load and measuring the process load are performed iteratively; in other words, these steps are executed repeatedly, resulting in a series of consecutive instantaneous performance metrics.

[0021] According to the novel and innovative concept of the present invention, the computer implementation method further includes the step of determining degradation parameters based on a successive series of instantaneous performance indicators, and estimation is also performed based on these degradation parameters.

[0022] A series of instantaneous performance metrics calculated over a continuous period of time, in principle, indicates deviations in the machine's behavior compared to a healthy machine, but in this case, the deviations occur over a period of time rather than instantaneously. This period includes a series of consecutive time points. For example, if this series of instantaneous performance metrics shows a linear downward trend, depending on how it is defined, this indicates a gradual downward trend in deviation behavior. Instantaneous performance metrics are defined as the difference between an estimated value and a measured value. As a result, the performance metrics decrease as degradation progresses. This gradually decreasing deviation behavior further indicates damping, aging, or degradation of the machine. Based on this series of instantaneous performance metrics, the degradation parameters defined above are determined or derived.

[0023] In other words, to determine the degradation parameter, a certain amount of data is analyzed over a predetermined limited period, during which the degradation parameter is assumed to be constant. This batch of data consists of a continuous series of instantaneous performance metrics, further reducing the impact of potential noise on this data. Over longer periods, both the performance metrics and the degradation parameter will show a decreasing or increasing trend, depending on their definition.

[0024] It should be noted that aging, decay, or deterioration of machinery is undesirable but unavoidable even under ideal conditions, and begins from the moment it is put into use and continues to occur during operation. This deterioration is caused by external factors such as friction of moving parts, corrosion of materials, deformation such as material fatigue or creep, metal fatigue or permanent deformation, contamination and the presence of dust particles, seasonal temperature differences, oil loss, and / or other factors known to those skilled in the art.

[0025] In such situations, a series of instantaneous performance indicators will gradually decrease depending on how they are defined. On the other hand, this series of indicators may also show sudden or abrupt changes, resulting in larger discrepancies across the entire series. This may indicate a defect or malfunction that deviates from the normal wear and tear of the machine.

[0026] Subsequently, process volume is estimated based on the determined degradation parameters. Finally, the operation of the compressor and / or support equipment can be reported based on the degradation parameters.

[0027] The advantage of this method of monitoring the operation of compressors and / or support machinery is that, in this way, undesirable but natural or expected deterioration of the machinery during normal operation is taken into account. This allows for a more accurate and precise understanding of the actual operation of the machinery.

[0028] On the other hand, as already mentioned, sudden changes indicate a serious defect or malfunction in the machine. Furthermore, in this situation, the machine operator can gain a more accurate understanding of the operation of the compressor and / or support equipment.

[0029] According to one embodiment, the method further includes the step of updating the model based on degradation parameters.

[0030] The initial model representing the ideal machine includes additional parameters so that the model eventually represents a machine that is already in operation. This creates a model that better represents the actual machine. As a result, the estimate of the process quantity becomes closer to the actual value.

[0031] According to one embodiment, the model may also include a parametric model that includes one or more model parameters such as a heat transfer coefficient, efficiency parameters, temperature compensation parameters, cooling parameters, speed compensation parameters, friction parameters, or any other parameters suitable for modeling a machine.

[0032] A parametric model consists of a set of model parameters that are directly or indirectly linked to one another through a set of variables. The inputs to the parametric model are the machine setting parameters corresponding to the model variables and the selection of the measured process quantity, which can then be estimated based on the model parameters. Furthermore, a portion of the measured process quantity can be used to verify the model's output. The next step in updating the model is to update the model parameters.

[0033] According to one embodiment, a computer implementation method for monitoring the operation of a compressor and / or support device further includes the step of deriving a health indicator based on model parameters.

[0034] A parametric model includes model parameters that describe the operation of the machine. These model parameters are iteratively updated based on measurements, while monitoring the machine's operation according to the described method, in order to match the estimates with the observed values; therefore, these model parameters also represent the operation. Next, a health index represents the set of model parameters with at least a single value, thereby representing the operation of the compressor and / or support equipment. In other words, a health index, which is at least one value, represents the state or operation of the compressor and / or support equipment. Alternatively, multiple health indices can be derived based on multiple partial selections of model parameters. Furthermore, it should be noted that this health index already takes into account the potential for machine degradation. Therefore, this health index accurately represents the actual state of the machine, not the ideal state. Furthermore, these steps, as described above, further limit the influence of noise on the measurements.

[0035] In the optional step, health indicators can also be reported.

[0036] According to one embodiment of the present invention, the method further includes the step of limiting the range of compressor setting parameters based on degradation parameters.

[0037] If degradation parameters indicate that the machine is operating only suboptimally, or that there is a risk of the machine operating only suboptimally, this can already be predicted by limiting the range of the setting parameters. This prevents the risk of sudden failure and also prevents further damage to the machine if it continues to operate based on setting parameters that could adversely affect subsequent operation. These setting parameters include compressor pressure, flow rate, optionally humidity level and / or power, or other possible setting parameters.

[0038] Another advantage is the ability to optimize machine availability. In this case, even if the machine is only operating suboptimally, it can be guaranteed to continue operating. This can be further predicted by scheduling maintenance in a timely manner. Furthermore, it is also possible to optimize the operation of the compressor room control system that controls a group of compressors.

[0039] Furthermore, this method may also include a step of switching the compressor off when the degradation parameter exceeds a predetermined value. This is a limit case when restricting the range of the setting parameter, and that range is limited to a limit value that completely disables the compressor. This step is performed, for example, when the degradation parameter value indicates a serious danger or when emergency maintenance is required.

[0040] A second aspect of the present invention is disclosed, which includes a processing unit configured to perform the method according to the first aspect of the present invention.

[0041] A third aspect of the present invention is disclosed, a computer program product comprising computer executable instructions for performing the method of the first aspect when executed on a computer.

[0042] According to a fourth aspect of the present invention, a computer-readable storage means including a computer program product of the third aspect is disclosed.

[0043] According to a fifth aspect of the present invention, a compressor including a data processing system according to a second aspect of the present invention is disclosed.

[0044] A sixth aspect of the present invention discloses a method for monitoring the operation of a compressor and / or its support device. The method includes iteratively performing the steps of: measuring one or more process quantities that represent the instantaneous operation of the device and / or the compressor; estimating one or more process quantities of the device and / or the compressor based on one or more setting parameters of the compressor using a model; and comparing the estimated process quantities with the measured process quantities to obtain an instantaneous performance index. The method further includes the step of determining degradation parameters based on a series of consecutive instantaneous performance indexes, wherein the estimation is further performed based on the degradation parameters, and the method includes the step of reporting the operation based on the degradation parameters.

[0045] Furthermore, based on the measured process volume, one or more process volumes can be estimated.

[0046] Furthermore, this method includes a step of updating the model based on the degradation parameters.

[0047] The process quantity includes one or more of the following: temperature, flow rate, velocity, inlet pressure, outlet pressure, ambient pressure, humidity, flow rate, and / or valve position.

[0048] The model may include a parametric model containing one or more model parameters. The update can then be performed by updating the model parameters. Furthermore, this method may include a step of deriving a health index based on the model parameters. In this case, the model parameters may include one or more of the following: heat transfer coefficient, efficiency parameter, temperature compensation parameter, cooling parameter, velocity compensation parameter, and friction parameter.

[0049] Furthermore, this method may include a step of limiting the range of compressor setting parameters based on degradation parameters. The setting parameters may then include one or more of the group of pressure, flow rate, humidity level, and power.

[0050] Furthermore, this method may include a step of stopping the compressor when the degradation parameter exceeds a predetermined value. [Brief explanation of the drawing]

[0051] [Figure 1] This is a schematic diagram illustrating a method for calculating instantaneous performance indicators of a compressor and / or support device. [Figure 2] This is a schematic diagram illustrating a method for monitoring the operation of a compressor and / or support device according to one embodiment of the present invention. [Figure 3] This figure shows a physical model representing the operation of the compressor and support equipment. [Figure 4] This figure shows another model illustrating the operation of the compressor and support equipment. [Figure 5] A closed cooling circuit containing oil is shown, along with a schematic diagram illustrating the heating of the oil. [Figure 6A] Figure 3 shows the results of a regression analysis to determine the parameters and / or coefficients in the model. [Figure 6B] Figure 3 shows the results of a regression analysis to determine the parameters and / or coefficients in the model. [Figure 6C] Figure 3 shows the results of a regression analysis to determine the parameters and / or coefficients in the model. [Figure 7A] The contamination parameters in the model shown in Figure 3 were adjusted to match the estimated values ​​with the measured values ​​for a cooling circuit contamination level of 75%. [Figure 7B] Figure 3 shows the results of adjusting the contamination parameters in the model to match the estimated values ​​with the measured values ​​for a 75% contamination level in the cooling circuit. [Figure 8A] Figure 3 shows the results of adjusting the contamination parameters in the model to match the estimated values ​​with the measured value where the cooling circuit contamination level was 92%. [Figure 8B] Figure 3 shows the results of adjusting the contamination parameters in the model to match the estimated values ​​with the measured value where the cooling circuit contamination level was 92%. [Figure 9A] Figure 3 shows the results of adjusting the contamination parameters in the model to match the estimated values ​​with the measured value of 94.8% contamination in the cooling circuit. [Figure 9B] Figure 3 shows the results of adjusting the contamination parameters in the model to match the estimated values ​​with the measured value of 94.8% contamination in the cooling circuit. [Figure 10] Figures 7 to 9 show the adjusted pollution parameters as a function of pollution level. [Figure 11] This shows the determination of healthy zones based on contamination parameters as a function of contamination levels. [Modes for carrying out the invention]

[0052] The present invention will be further described with reference to the drawings.

[0053] The present invention is described with reference to specific drawings in relation to particular embodiments, but is not limited thereto and is determined solely by the claims. The drawings provided are schematic and not limiting. In the drawings, the size of certain elements may be exaggerated for illustrative purposes and may not be drawn to scale. Dimensions and relative dimensions do not necessarily correspond to the actual embodiments of the present invention.

[0054] Furthermore, terms such as "first," "second," and "third" are used in the description and claims to distinguish similar elements and are not necessarily used to describe a sequential or chronological order. These terms are interchangeable under appropriate circumstances, and embodiments of the present invention may be carried out in an order other than those described or illustrated herein.

[0055] Furthermore, terms such as top, bottom, over, and below in the description and claims are used for illustrative purposes only and are not necessarily used to describe relative positions. Terms used in this manner are interchangeable in appropriate contexts, and embodiments of the invention described herein may be used in orientations other than those described or illustrated herein.

[0056] Furthermore, various embodiments are referred to as “preferred embodiments,” but should be interpreted not as limiting the scope of the present invention, but as exemplary means of carrying out the present invention.

[0057] The term “including” as used in the claims should not be construed as being limited to the means or steps described below, and the term does not exclude other elements or steps. The term should be construed as identifying the presence of the mentioned feature, element, step or component, but not as excluding the presence or addition of one or more other features, elements, steps or components, or groups thereof. Accordingly, the scope of the expression “apparatus including means A and B” should not be limited to an apparatus consisting only of components A and B. Its meaning, with respect to the present invention, should be construed as listing only components A and B of the apparatus, and the claims should further include equivalents of these components.

[0058] Figure 1 is a schematic diagram illustrating a method for calculating instantaneous performance indicators of a compressor and / or support device. The first step involves including measurement data 101 as input to a model 102 representing the compressor and / or support device. For example, model 102 is a physical model as further shown in Figure 3. The inputs are:

number

number

number

[0059] Furthermore, it should be understood that the above example of physical model 102 can be replaced with other models representing the compressor and / or support equipment. Referring to Figure 4, which shows another model 400, a set of inputs 401-405, for example, speed 401, pressure 402, inlet temperature 403, ventilation conditions 404, and oil temperature at a certain time 405, can be used with a set of equations 406 to calculate the oil temperature at a subsequent time 407.

[0060] Referring again to Figure 3, and thus to the physical model 102, reference numeral 304 denotes the cooling capacity P of the cooling circuit of the cooler present in the configuration including the compressor. cooler This includes a model for determining the cooling circuit. Referring to Figure 5, the cooling circuit 500 is, for example, a closed oil circuit. The oil is circulated by a pump 501 to one or more mechanical parts 502, where heat is removed into the oil. The oil is then cooled again in an oil cooler 504, for example by a ventilator 505. The oil is then used further as a lubricant 506 for mechanical parts such as a gearbox and is recirculated in the cooling circuit 500 via the pump 501.

[0061] The correct functioning of the oil circuit 500 can be adversely affected in various ways. For example, the oil can be contaminated by wear of the moving parts within the oil circuit 500, which can clog the conduits and prevent the lubrication and cooling of the bearings and gears. Initially, the oil is very pure, but it can become contaminated when very fine particles in the cooling air drawn in from the environment are not retained by the filter. This phenomenon occurs in oil-injected compressors. Compressors that are not oil-injected have a closed oil circuit, so the oil does not flow along the compressor elements and this phenomenon is less likely to occur. However, as in the case of oil-injected compressors, the air side of the oil cooler 504 also clogs with dirty particles in the air. This phenomenon is also called clogging 503, and the oil cooler 504 functions only sub-optimally over time. This is because heat transfer becomes more difficult and the temperature of the entire oil increases. As a result, for example, the housing also becomes hot, increasing the temperature of the compressed air. Furthermore, the lubrication state of the bearings and gears is no longer optimal, these parts are further heated, age acceleratively, and the availability of the compressor is impaired.

[0062] The closed circuit can be modeled as a system where the parameters depend only on time, and is also called a lumped system. The heating of the oil 509 depends on the heat 507 absorbed as a result of losses and the heat 508 released by the cooler, and thus on the cooling capacity.

[0063] In this case, the cooling capacity is the heat Q removed during operation heat removed and is proportional to the difference between the oil temperature T oil and the cooling air temperature T air inlet . The proportionality coefficient is equal to the heat transfer coefficient UA (108) of the cooler. Furthermore, an additional fan state coefficient is added taking into account whether forced ventilation is active or not. The cooling air temperature itself can be modeled as a correction in addition to the temperature within the compressor housing. The cooling capacity P cooler corresponding to the heat removed is represented by reference numeral 307, P cooler (fanstate, UA, T oil, T air inlet ,…)=Q heat removed This is the result.

[0064] The heating of the oil in the oil circuit is represented by reference numeral 306 (Q heat generated -Q heat removed ) can be written as. Based on the above, by using an operator 308 in which the gain coefficient is optionally supplemented, the oil temperature at a specific point in time can be calculated as the output 309 of the model.

[0065] Referring again to Figure 1, the difference between the estimated value 309 and the measured value 107 can be calculated as 103. Therefore, reference numeral 104 contains a series of residuals.

[0066] In principle, the aforementioned gain coefficients representing each parameter, and / or coefficients describing the operation of the compressor and cooling circuits, can be determined based on a sufficient number of measurements in a new and / or sound machine. These can then be determined, for example, by least-squares regression. Figures 6A to 6C show the results of such regression analysis. The y-axis of the illustrated graphs shows, as a function of time, the oil temperature in °C, the compressor speed in rpm, and the inlet temperature in °C, respectively. It should be further noted that Figures 6B, 7B, 8B, and 9B have two y-axis, with the right-hand y-axis showing the outlet pressure in bar.

[0067] As long as we can assume the oil circuit is healthy, the above model can be used to determine the efficiency of the cooling system and / or compressor. The calculated measurements and estimates agree with each other. However, as the cooler begins to clog, the heat transfer coefficient UA of the cooler decreases. This degradation is due to an additional coefficient β, also called the contamination parameter. U By introducing this, it can be included in the model, which is an example of a degradation parameter defined above, and this value ranges between 1, which corresponds to a healthy machine, and 0, which means there is no heat transfer at all. Next, the capacity of the cooler is (P cooler =Q heat removed=[fanstate·β U ·β fannon +(1-fanstate)·β fannoff ]UA(T oil -T air inlet ))

[0068] Therefore, the Model 102 used is a contamination parameter β that represents the health state, i.e., the degradation state, of a part of the machine, such as the machine or a cooling circuit. U This includes, in addition, the example of Model 102, the gain coefficient α represents the friction coefficient of the compressor and / or peripheral equipment. n This can be used as an indicator of the soundness of the machine. When the compressor and / or peripheral equipment are used, this will be equal to the expected coefficient of friction of a new machine, but over time, this coefficient of friction will no longer correspond to the coefficient of friction of a new machine, resulting in a significant discrepancy between the measured value and the estimated value.

[0069] Referring to Figure 2, the degradation of the compressor and / or peripheral equipment and associated cooling circuits is considered according to a new and innovative method 200. Figure 2, like Figure 1, shows the measured data 101 as input, a model 102 representing the compressor and / or auxiliary equipment, the calculation of the difference between the measured value 107 and the estimated value 309 103, and a series of residuals 104. However, unlike the method shown in Figure 1, in step 201, the contamination parameter β U and gain coefficient α n The residual 104 is minimized by updating one or more parameters of the model 102 based on the feedback 202.

[0070] Furthermore, it is possible to optionally adjust parameters used in other aspects of the model, such as compressor efficiency, oil speed, and friction coefficient. In other words, by adjusting the parameters and / or variables within Model 102, the estimates will come to match the measured values, and thus the residuals 104 will be reduced to the minimum, i.e., to the level of noise.

[0071] For example, in a simple case, if only the friction coefficient is changed via feedback 202, it may show a monotonically increasing trend in the case of natural or expected degradation of the cooling circuit 500. On the other hand, depending on the reference and definition of this factor in the model, a monotonically decreasing trend may also be observed. Therefore, it should be understood that one or more parameters change over time due to feedback 202.

[0072] Referring to Figure 7A, a graph of oil temperature on the y-axis over time on the x-axis is shown, where the solid line represents the measured value 107 and the dashed line represents the estimated value based on Model 102. The difference between these is calculated as the error [%], corresponding to the residual 104. Feedback 202 adjusts the parameters described above so that the estimated value matches the measured value. Furthermore, Figure 7B shows measured values ​​of velocity on the left y-axis and exhaust pressure on the right y-axis over time, measured at the same time as the oil temperature measurement.

[0073] Therefore, in order to match the estimates with the measurements, the parameters are adjusted, and as a result, a high degree of contamination or blockage is imposed on the cooler in the model. This provides insight into the degree of contamination of the cooler. Referring again to Figure 2, then, based on feedback 202, the degree of contamination 205 of the cooler can be derived via an additional model 204, for example, a regression model. Block 201 is here a minimizer that proposes a new value for parameter 108, which leads 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, where 108 is the proposed value of the degradation parameter.

[0074] In Figure 7, this contamination level corresponds to 75%. Figures 8A, 8B, 9A, and 9B show further diagrams similar to those in Figures 7A and 7B, where in Figure 8 the cooler has a contamination level of 92%, and in Figure 9 the cooler has a contamination level of 94.8%.

[0075] Furthermore, Figure 10 shows the contamination parameter β. UThe normalized value β clog However, it has been shown to decrease depending on the degree of contamination of the chiller. Based on this, it is possible to determine the health range in which this parameter represents the health of the cooling circuit, and therefore the compressor. As shown in Figure 11, for example, three ranges are shown: a completely clogged chiller (total blockage), a partially clogged chiller (partial blockage), and a healthy chiller (healthy).

[0076] By determining the degree of contamination, maintenance can be planned more efficiently. As soon as the degree of contamination exceeds a limit, maintenance can be scheduled to ensure the availability and efficiency of the machine, as shown in Figure 10, for example. Thus, maintenance can be planned not only as a purely periodic preventative measure, but also as needed. Finally, it is also possible to better estimate the risk of oil cooler, and therefore compressor, failure, and, if necessary, to notify operators in a timely manner to avoid facing unexpected failures.

Claims

1. A computer implementation method for monitoring the operation of a compressor and / or its support device, - A step (107) of measuring one or more process quantities (101) that indicate the instantaneous operation of the apparatus and / or compressor; - Step (102) of using a model (102, 400) to estimate one or more process amounts of the apparatus and / or compressor based on one or more setting parameters of the compressor; and - A step of comparing the estimated process amount with the measured process amount (103), thereby obtaining an instantaneous performance index (104), The method further includes performing the following repeatedly: - A step (202) of determining degradation parameters (203) based on a series of consecutive instantaneous performance indicators (104); and a step of further performing the estimation (102) based on degradation parameters (108); - A step of reporting the operation based on the degradation parameter (108), Computer implementation methods, including those mentioned above.

2. The computer implementation method according to claim 1, further comprising the step (202) of updating the model based on the degradation parameter (108).

3. The computer implementation method according to claim 2, wherein the model (102, 400) includes a parametric model that includes one or more model parameters.

4. The computer implementation method according to claim 3, wherein the updating step (202) is performed by updating the model parameters (305, 306, 307).

5. - A step of deriving a health index (205) based on the aforementioned model parameters (305, 306, 307), The computer implementation method according to claim 3, further comprising:

6. The computer implementation method according to claim 3, wherein the model parameters include one or more of the group consisting of a heat transfer coefficient, efficiency parameters, temperature correction parameters, cooling parameters, speed correction parameters, and friction parameters.

7. The computer implementation method according to claim 1, wherein the estimation of one or more process quantities is further performed based on the measured process quantity (107).

8. The computer implementation method according to claim 1, wherein the process quantity (101) includes one or more of the group consisting of temperature, flow rate, velocity, inlet pressure, outlet pressure, ambient pressure, humidity, flow rate, and / or valve position.

9. - A step of limiting the range of the setting parameters of the compressor based on the degradation parameter (108), The computer implementation method according to claim 1, further comprising:

10. The computer implementation method according to claim 9, wherein the setting parameter includes one or more of the group consisting of pressure, flow rate, humidity level, and power.

11. - A step of switching the compressor off when the degradation parameter (108) exceeds a predetermined value, The computer implementation method according to claim 1, further comprising:

12. A data processing system comprising a processing unit configured to perform the method described in any one of claims 1 to 11.

13. A computer program comprising a computer-executable instruction for performing the method according to any one of claims 1 to 11.

14. A computer-readable storage medium comprising the computer program described in claim 13.

15. A compressor comprising the data processing system described in claim 12.

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