Method for predicting remaining service life of sealing device of piston compressor

Abstract

a Provide the vibration data 1a of the reciprocating part 12 of the reciprocating compressor 11, or the feature quantity 1b that can be calculated from the vibration data 1a. b Generate the input matrix 3 from the vibration data 1a or the feature quantity 1b. c Apply the model 4 to the input matrix 3 to execute the simulation. The model 4 is used to describe the error propagation in the closed system. Each simulation step results in an output matrix 5. Use the output matrix 5 of the nth simulation step as the input matrix 3' of the (n + 1)th step. f Select the parameter 6c from the values of the output matrices 5, 5'. g Determine the number of simulation steps required until the parameter 6c falls below the threshold 8a close to zero. Output this number as the simulation result 9. h Obtain a plurality of results 9, 9' by repeating steps a to g. i Fit a curve to the results 9, 9'. Calculate the time point 7 when the curve intersects the lower boundary 8c. The intersection point is regarded as an indicator of the occurrence of a failure and as an indicator of the predicted end time of the service life of the seal device 10.

Description

Technical Field

[0001] The present invention relates to a method for determining the degree of deterioration (degree of degradation) of a sealing device of a reciprocating compressor and predicting the remaining service life (remaining service years, remaining service life), a preventive maintenance system for a reciprocating compressor, a reciprocating compressor equipped with the preventive maintenance system, and a computer-readable storage medium. Further, the present invention relates to the use of a method for monitoring the deterioration of a sealing device of a reciprocating compressor and / or predicting the remaining service life of a sealing device of a reciprocating compressor and / or adjusting a leak occurring in a sealing device of a reciprocating compressor.

Background Art

[0002] "Condition monitoring" is a process of identifying (identifying, detecting) significant changes indicating the occurrence of a failure (developing fault) by monitoring the state parameters (vibration, temperature, etc.) of a machine. This "condition monitoring" is an important component of predictive maintenance and is also called "preventive maintenance" or "predictive maintenance". Preventive maintenance enables planning maintenance work (maintenance work) and taking other measures to avoid unexpected machine downtime, resulting damage and its consequences. This improves the availability of the production system and at the same time reduces the system downtime and maintenance costs.

[0003] For compressing fluids such as gases and aerosols, compressors are usually used. In a reciprocating compressor, the rotational motion of the crankshaft generated by the drive device is converted by the connecting rod into the reciprocating motion of one or more pistons, so as to compress the gas or aerosol sucked into the compressor unit. By design, the piston is subjected to the forces generated during this process. At the end of each stroke, the piston is accelerated from zero speed to high speed in the middle of the stroke and then returned to zero again.

[0004] In at least a part of the compression cycle, the compression chamber is sealed by a closed cylinder valve before the compressed gas or aerosol is discharged to the output device by opening at least one cylinder valve. These valves are subject to a certain degree of wear due to their movement. The so-called packing, a sealing system for the piston rod, is used for the moving piston rod to seal the working chamber on the crank side. Since the sealing element contacts the vibrating piston rod, it always wears out.

[0005] The unwanted escape of compressed gas or aerosol from the compressor unit, particularly due to leakage related to the wear of the above-mentioned sealing element and / or cylinder valve, is generally referred to as "leakage". It is possible to use corresponding sensors to measure the leakage and evaluate the function of the compressor unit. Ideally, this sensor is arranged in the individual compressor unit or compression stage of the compressor. However, such sensors are often not installed in the compressor for cost reasons. Also, the measured leakage values are not always automatically recorded and transmitted.

[0006] A method for monitoring the operation of a reciprocating compressor using a pressure sensor and a vibration sensor is known from Patent Document 1. Based on the detected internal pressure of the device and the detected vibration of the reciprocating device of the compressor, the protection system described in Patent Document 1 calculates the rigidity value of the cylinder assembly. Therefore, it is possible to evaluate the structural integrity of the cylinder assembly during operation. The protection system is also enabled to stop the compressor after it is determined that the state of the compressor deviates from a predetermined standard state.

[0007] Patent Document 2 relates to a method and an apparatus for implementing a vibration diagnosis monitoring and evaluation method for individual mechanical parts. At least one time signal of the vibration of the machine is recorded and evaluated by frequency analysis using a frequency analyzer. Next, the maximum frequency extracted from the frequency spectrum is assigned to a specific mechanical part, and compared with the target value data, model data, and / or reference data of each mechanical part. When the determined extracted maximum frequency from the currently measured vibration data deviates from the target value data, model data, and / or reference data of the mechanical part, evaluation information or a warning message is output.

[0008] However, the drawback of the above-described methods and systems is the fact that the user is notified only when one of the calculated values deviates from the ideal value. With these methods and systems, it is impossible to transmit the remaining service life in real time and to display the degree of wear of the sealing element of the compressor. Therefore, there is still a need for a method and a system that can accurately predict the future state and the failure time of mechanical parts such as the sealing element. Furthermore, in preventive maintenance, parts with a long remaining service life (remaining service years, remaining service life) are often replaced. Such premature replacement causes unnecessary costs and is not very sustainable.

[0009] The paper of Non-Patent Document 1 is also known from the state of the art. Non-Patent Document 1 deals with the comparison of the predictive performances of different methods for estimating the remaining useful life (RUL) of a reciprocating compressor from the viewpoints of the accuracy and precision of the compared methods. The predictive methods (forecasting methods) taken up are data-driven methods that model the degradation process using past information. The data used in the calculations are derived from industrial reciprocating compressors and have already been acquired from a server. Therefore, these data are not raw data from sensors attached to the compressor or from sensors measuring the compressor.

Prior Art Documents

Patent Documents

[0010]

Patent Document 1

Patent Document 2

Non-Patent Documents

[0011]

Non-Patent Document 1

Non-Patent Document 2

Non-Patent Document 3

Non-Patent Document 4

Non - Patent Document 5

Summary of the Invention

Problems to be Solved by the Invention

[0012] However, the drawback of such a method is that it reaches its limit in the case of a new error event, that is, a case that has not been trained. Furthermore, the drawback of such a method is that the accuracy of prediction (forecast) generally depends on the quantity and quality of the available data.

[0013] Based on the above - mentioned prior art, it is an object of the present invention to solve such drawbacks and other drawbacks of the prior art. In particular, the present invention aims to provide a method capable of determining the degree of wear of the sealing device of a reciprocating compressor and predicting the remaining service life of the sealing device in a cost - effective and reliable manner.

Means for Solving the Problems

[0014] This problem is solved according to the independent claims by a method for monitoring the deterioration of the sealing device of a reciprocating compressor, and / or for predicting the remaining service life of the sealing device of a reciprocating compressor, and / or for readjusting the leakage occurring in the sealing device of a reciprocating compressor, a preventive maintenance system for a reciprocating compressor, a reciprocating compressor, a computer - readable storage medium, and the use of the method. Advantageous embodiments and further developments are the subject of the dependent claims.

[0015] This problem is solved by a method implemented on a computer for determining the degree of wear of the sealing device (sealing arrangement, sealing configuration, sealing mechanism, sealing construction) of a reciprocating compressor and predicting the remaining service life. This method comprises the following steps (a) to (h).

[0016] (a) Provide vibration data of the reciprocating part of a reciprocating compressor. Alternatively, features that can be calculated from the vibration data can also be used in the method according to the invention. The vibration data, or the features that can be calculated from the vibration data, must be traceable to specific positions (specific positions (segment M)) of the movement of the reciprocating part of the reciprocating compressor.

[0017] (b) Create an input matrix from the vibration data or from features that can be calculated from the vibration data. The input matrix has dimensions (M×N) determined by the number of segments M and the number of sampling times N.

[0018] (c) Perform a simulation comprising a plurality of simulation steps of applying a model that can be used to describe the propagation of errors in a closed system to the input matrix. Each simulation step generates an output matrix. The output matrix of the nth simulation step is used as the input matrix for the (n + 1)th simulation step.

[0019] (d) Optionally, form a Laplacian matrix for each output matrix and calculate the eigenvalues of each Laplacian matrix. The Laplacian matrix can be obtained, in particular, by multiplying each output matrix by its transpose matrix.

[0020] (e) Optionally, calculate the normalized distance (normalized distance) of the output matrix with respect to the output matrix of the previous simulation step. (f) Exactly one parameter is selected from among the value of the output matrix, the eigenvalues of the Laplacian matrix calculated in any step (d), or the normalized distance calculated in any step (e). The selected parameter represents a measure of the synchronization of traceable (traceable, traceable) vibration data for or to the features calculable from segment M to each other.

[0021] (g) Determine the number of simulation steps as the number of simulation steps that must be executed until the selected parameter falls below a predetermined threshold close to zero. The result of the executed simulation, i.e., the determined number of simulation steps, is converted into time units based on the sampling rate of the vibration data used in the input matrix or the features calculable from the vibration data.

[0022] (h) By repeating the above steps (a) to (g) at regular discrete time intervals, a number of simulation results are obtained.

[0023] (i) After each simulation, considering the past simulation results, a curve is fitted to the obtained simulation results, particularly by local linear regression or other appropriate methods, and the time point within the characteristic quantity at which the curve intersects the predetermined (predetermined, predefined) lower boundary is determined. The lower boundary is, in particular, the horizontal axis (x-axis) passing through the zero point of the Cartesian coordinate system. The calculated time point at which the curve fitted to the simulation results intersects the lower boundary is regarded as an indicator of the occurrence of a malfunction, particularly as an indicator of the occurrence of a total failure, and as an indicator of the predicted end of the service life of the sealing device.

[0024] In the context of the present invention, the term "sampling time" is understood to mean the time at which a value, particularly a value of vibration data or a value of a characteristic quantity calculable from vibration data, is measured or calculated.

[0025] In the context of the present invention, the term "sealing device" particularly means a piston rod packing that seals the piston rod and / or a cylinder valve that seals the compression chamber.

[0026] The magnitude of the predetermined (predefined) threshold depends on the input data, i.e., the vibration data or the characteristic quantity calculable from the vibration data. Since the approximation of the second smallest eigenvalue to zero suggests high system instability, the magnitude of the predetermined threshold is a value close to zero.

[0027] The present invention is based on the recognition that leaks affect the vibrations measured in a compressor. In particular, in addition to an increase in leaks, damage to packing and guide rings leads to displacement and misalignment from the ideal movement of the piston rod and piston, as well as an increase in friction and vibrations in each segment of the piston movement. Since the accelerating force of the piston is maximum in the middle of the piston movement, the displacement also increases in this region and becomes prominent due to the increase in vibrations. As the leak increases, this displacement further increases, which may be accompanied by an increase in vibrations spreading between some adjacent segments of the piston movement towards the end of the stroke.

[0028] This is reflected in the simulation stage of the present invention where the propagation speed of faults in the stroke end direction is simulated. Mapping the measured vibrations to the reciprocating parts of a reciprocating compressor, particularly to specific positions of the piston, is a necessary preprocessing step to ensure that the input matrix is a two-dimensional representation of the vibrations measured over the entire cycle of the reciprocating device. Although not wishing to be limited to this, the vibration pattern in the movement of the reciprocating device, particularly in the movement of the piston, is assumed to represent the thermodynamic processes inside the cylinder. This hypothesis is supported by information from experts in manual machine diagnosis. These experts have reported the correlation between the possibility of leaks and an increase in friction in the segments, and the uniform and parallel increase in vibration peaks in these segments.

[0029] In the context of the present invention, the vibrations occurring in each segment of the piston movement and the propagation of those vibrations to adjacent segments are considered as interacting particles. Due to the time evolution of the vibrations, it is possible to describe the dynamics between the particles, for example, using a time-dependent Ginzburg-Landau (TDGL) model. This model is a modification of the kinematic Ising model where the discrete variables represent the states of the magnetic spins (-1, +1) of the particles. The free energy of such a system is estimated by observing the change in the order parameter (Non-Patent Document 2).

[0030] The method according to the present invention is basically a hybrid digital sensor that simulates the function or measurement of a leakage sensor using vibration data. In other words, the method according to the present invention makes it possible to predict damage to a packing or valve by simulating leaks that are not directly measured using another parameter, that is, using vibration data or characteristic quantities that can be calculated from vibration data. The selected method does not require additional pressure sensors, temperature sensors, or the calculation of dynamic pressure, so it is cost-effective in terms of the required sensor technology. Furthermore, the method according to the present invention has been proven to be particularly robust with respect to the accuracy of the predicted service life of the sealing device against changes in, for example, the utilization rate of a compressor or other fluctuations not caused by wear.

[0031] In a preferred embodiment of the method according to the present invention, the second smallest eigenvalue of the output matrix is selected as a parameter. In this case, the predetermined threshold is an amount that is approximately zero, for example, 0.000025.

[0032] In a preferred embodiment of the method according to the present invention, method step (d) is carried out. The second smallest eigenvalue of the Laplacian matrix is selected as a parameter. In this case, the predetermined threshold is an amount that is approximately zero, for example, 0.000025.

[0033] The second smallest eigenvalue of the Laplacian matrix is called the algebraic connectivity or the Fiedler eigenvector (Non-Patent Document 3). This metric plays an important role in, for example, the synchronization of coupled oscillators, the robustness of networks, and cascade failures (Non-Patent Document 4). When the algebraic connectivity of a graph becomes zero, the graph is divided into a plurality of connected components, that is, the dynamic process does not synchronize (Non-Patent Document 5).

[0034] On the other hand, the method according to the present invention can be remotely implemented so that the vibration data measured by the compressor is sent to an independent location and evaluated at the independent location. Alternatively, in a further embodiment of the method according to the invention, it is also conceivable that the vibration data provided, or the characteristic quantities calculable from the vibration data, are obtained as part of the method implemented. For this purpose, at least one time signal x(t) of the vibration of the reciprocating part of the reciprocating compressor is detected by at least one vibration sensor. Optionally, it is possible to calculate characteristic quantities from the recorded time signal x(t). The detected time signal x(t) of the vibration, or optionally the calculated characteristic quantities, are then divided into a plurality of time segments. These always correspond to the same position of the reciprocating device, in particular of the piston or the piston rod. For each time segment, the average value of the time signal x(t) recorded in each time segment, or optionally the characteristic quantities calculated therefrom, is calculated in order to obtain an averaged vibration value or an averaged characteristic quantity. Furthermore, the position of the reciprocating part of the reciprocating compressor is detected by at least one position sensor. The detected position data are provided for implementing the method. Based on the recorded position data, the movement of the reciprocating part is preferably subdivided into a plurality of segments M of equal magnitude with respect to each other. Finally, the averaged vibration values or the averaged characteristic quantities are assigned to the segments M of the movement such that each averaged vibration value or averaged characteristic quantity always corresponds to the same segment M of the movement (motion) of the reciprocating part. Thus, the leakage at a specific point is reflected in the vibration data accordingly. For example, the time interval is one minute. That is, within one minute, a certain number of measurement points are obtained in a certain segment M of the movement of the reciprocating part. By averaging these measurement points, they are provided as the minute average value (miniutes average value) of each segment M of the input matrix.

[0035] In a preferred embodiment of the method according to the invention, the time when the selected parameter falls below a predetermined (predefined) threshold value is predicted only after the parameter has reached a predetermined (predefined) critical value (criticality value). The critical value indicates the onset of irreversible deterioration in the sealing device.

[0036] As a result, the fluctuation range of the simulation results is significantly reduced. Therefore, the accuracy of predicting the time when a failure of the sealing device actually occurs is improved. Furthermore, the start of the prediction is shifted in the direction of the actual failure. Therefore, while the accuracy of the prediction is improved, the risk that the prediction result is misdetected (the risk of a false positive prediction result) is reduced.

[0037] In a preferred embodiment of the method according to the present invention, the model applied to the input matrix is the time-dependent Ginzburg-Landau model (TDGL). In the context of the present invention, the time-dependent Ginzburg-Landau model (TGDL) (Non-Patent Document 2), as a generalization of the n-vector and the 2D Ising model, has been found to be particularly well-suited to the current experimental context (experimental content).

[0038] The use of a reciprocating compressor is often carried out under conditions that externally affect the vibration of the reciprocating parts of the reciprocating compressor. For example, when using a reciprocating compressor on a ship, the dominant wave pattern and the movement of the related ship itself may cause vibrations that overlap with the vibrations detected by the reciprocating compressor.

[0039] In order to reduce such influences, it is possible to additionally remove noise from the provided vibration data or the provided feature quantities. In a preferred embodiment of the method according to the present invention, the vibration data or the feature quantities provided and optionally denoised are normalized between -1 and +1. This is advantageous depending on the selected model and the distribution of the vibration data.

[0040] In a preferred embodiment, the method according to the invention further comprises a data transmission step for transmitting the vibration data or the characteristic quantities to at least one ground station. Additionally or alternatively, it is also conceivable that the method according to the invention comprises a data transmission step for transmitting the predicted remaining service life of the sealing device to the user interface of the reciprocating compressor. In either case, at least the simulation step, and the prediction of the time when the selected parameters fall below a predetermined (pre-defined) threshold value, are carried out in whole or in part on the ground using a ground station.

[0041] By transmitting the vibration data measured on the reciprocating compressor to a ground station, it becomes possible to remotely monitor the state of the reciprocating compressor without all the computational capabilities required to implement the method according to the invention being on the reciprocating compressor or in the vicinity of the reciprocating compressor. Furthermore, the predicted remaining service life of the sealing device is transmitted to the user interface of the reciprocating compressor, for example to the control panel of the reciprocating compressor or to a warning light, enabling the user of the reciprocating compressor to be prepared for an unexpected failure of the reciprocating compressor. This is particularly advantageous when monitoring reciprocating compressors used on ships, since an unexpected failure of the reciprocating compressor offshore can pose a significant safety risk.

[0042] In a preferred embodiment, the method according to the invention further comprises a step of transmitting to the user the specified time and / or the predicted end time of the service life of the sealing device. This transmission is preferably in real time. Additionally or alternatively, according to the invention, it is also conceivable to estimate the degree of wear of the sealing device from the behavior of the parameters over the number of simulation steps performed and to transmit the thus obtained degree of wear to the user. This transmission is also preferably carried out in real time.

[0043] As a result, the user of the reciprocating compressor can know the state of the reciprocating compressor. If necessary, the user can take timely preventive measures against the interruption of necessary maintenance (repair, maintenance).

[0044] In a preferred embodiment of the method according to the invention, the sealing device is a piston rod packing for sealing the piston rod or a cylinder valve for partitioning the compression chamber. The piston rod packing and the cylinder valve are each one of the components of the piston compressor. Therefore, damage or failure of the piston rod packing and the cylinder valve not only leads to a serious obstacle to the compression process, but also results in a reduction in the performance of the piston compressor.

[0045] In a preferred embodiment of the method according to the invention, the execution of the method is automatically started at a time selectable by the user, i.e., or at regular time intervals. By this process automation, the risk of unexpected failures of the reciprocating compressor is significantly reduced. On the other hand, it is also possible to monitor the implementation status of the maintenance (repair, maintenance) work defined by the manufacturer and / or the user.

[0046] In principle, all the above-described embodiments can be combined with each other. This problem is further solved by a preventive maintenance (predictive maintenance) system for use with a reciprocating compressor. This preventive maintenance system includes a computing device (computing means) having at least one processor. The computing device not only receives position data of the reciprocating part of the reciprocating compressor, but also receives vibration data or characteristic quantities of the reciprocating part of the reciprocating compressor that can be calculated from the position data, and is configured to assign these vibration data or characteristic quantities to a segment M of the movement of the reciprocating part of the reciprocating compressor. The computing device is further configured to create an input matrix from the received vibration data or characteristic quantities and execute a plurality of simulations on the input matrix using a model. Each of the plurality of simulations includes a plurality of simulation steps. The model can be used to describe the propagation of errors in a closed system. As a result, an output matrix is generated in each case, and the output matrix of the nth simulation step is used as the input matrix of the (n + 1)th simulation step. The time interval (interval) between two simulations corresponds to the time unit defined in each case. In particular, the time interval between two simulations can be set to be within 1 minute to 6 hours, preferably within 1 minute to 4 hours, and particularly preferably within 1 minute to 60 minutes. The smaller the time interval between two simulations is selected, the smoother the result will be, while the time required to obtain the result will be longer. The computing device is further optionally configured to not only form the Laplacian matrix of each output matrix, but also calculate the eigenvalues of each Laplacian matrix, or calculate the normalized distance of the output matrix of the (n + 1)th simulation step with respect to the output matrix of the nth simulation step. The computing device is also configured to select a parameter from among the values of the output matrix, or from the calculated eigenvalues of the optionally formed Laplacian matrix, or from the optionally calculated normalized distance.The selected parameters represent a measure of the synchronization of the vibration data that can be traced back (tracked back) to segment M, or a measure of the synchronization of features computable from the vibration data. The computing device determines the number of simulation steps as the number of simulation steps that must be executed for the result of the simulation executed in each case until the selected parameters fall below a predetermined threshold close to zero. Further, this number of simulation steps is configured to be output in time units. The time unit or the simulation time is calculated from the time interval of the (averaged) values input to the input matrix generated from the vibration data. The computing device also fits a curve to the obtained simulation results and calculates, using an appropriate method (e.g., local linear regression), the future time at which the curve intersects a predefined lower boundary, particularly the X-axis. As a result, the computing device is configured to determine the failure time as a date. The calculated intersection point is regarded as an indicator of the occurrence of a failure and as an indicator of the prediction of the life of the seal device. The computing device is also configured to output the remaining useful life of the seal device of the piston compressor based on those indicators.

[0047] The advantages of the preventive maintenance system according to the present invention essentially result from the advantages already described for the method according to the present invention. In an advantageous embodiment, the preventive maintenance system according to the invention further comprises at least one position sensor. The at least one position sensor is designed to detect the position of the reciprocating part of the reciprocating compressor and is communicatively connected to the computing device. In this embodiment, the preventive maintenance system further comprises at least one vibration sensor. The at least one vibration sensor is designed to detect the time signal x(t) of the vibration of the reciprocating parts of the reciprocating compressor and is communicatively coupled to the computing device. The computing device is configured to divide the detected time signal x(t) of the vibration into a plurality of time segments and to form an averaged vibration value between the re-divided time segments. Further, the computing device is configured to divide the movement of the reciprocating part based on the determined position (determined position), preferably into a plurality of segments M of equal magnitude to each other, and to assign the averaged vibration value to these segments M of the movement.

[0048] This enables the state of the sealing device of the piston compressor to be monitored in more detail and the remaining service life to be predicted more accurately. In a preferred embodiment of the preventive maintenance system according to the invention comprising at least one position sensor, at least one of the vibration sensors is arranged on the cylinder head or the crosshead of the reciprocating compressor. This cylinder head or crosshead is related to the reciprocating part of the reciprocating compressor.

[0049] It has been found that by arranging a vibration sensor on the cylinder head, a particularly reliable explanation can be given regarding the state and remaining service life of the cylinder valve, especially the exhaust valve. On the other hand, by arranging a vibration sensor on the crosshead, it has been found that the state and remaining service life of the piston rod packing can be grasped particularly accurately.

[0050] This problem is further solved by a reciprocating compressor equipped with a preventive maintenance system as described above. In a preferred embodiment, the piston compressor is designed as a piston compressor in which the piston moves (moves) in the vertical direction. By moving the piston in the vertical direction, the wear of the seal element and the guide element is reduced. This type of piston compressor is often used in ships.

[0051] This problem is further solved by a computer-readable storage medium. The computer-readable storage medium embodies a computer program having computer-readable program code. The program code is adapted to cause a processor to execute the method described herein when executed by at least one processor of the computer.

[0052] In the context of the present invention, the term "storage medium" also includes, in particular, cloud storage, flash memory, or embedded memory. This problem is ultimately solved by using the method described herein to monitor the deterioration of the sealing device of the reciprocating compressor. Alternatively or additionally, this problem is solved by using the method described herein to predict the remaining service life of the sealing device of the reciprocating compressor, in particular the remaining operating time or the replacement time. As yet another option or in addition to the above applications, the problem is solved by reproducing the leakage occurring in the sealing device of the reciprocating compressor using the method described herein.

[0053] Hereinafter, various embodiments of the present invention will be described in more detail with reference to the drawings, where generally the same or corresponding elements are given the same reference numerals. Show it.

Brief Description of the Drawings

[0054]

Figure 1

Figure 2

Figure 3

Figure 4a

Figure 4b

Figure 5

Figure 6

Mode for Carrying Out the Invention

[0055] Figure 1 is a flowchart showing a sequence of a possible embodiment of the method according to the present invention for predicting the remaining useful life in order to determine the wear degree (degree of wear) of the seal device of a reciprocating compressor. The input matrix 3 is subsequently generated in step (b) for the vibration data 1a considered by at least one vibration sensor belonging to the reciprocating compressor, which has been provided in step (a), or for the feature quantity 1b calculated from the vibration data 1a. For example, this includes a step of generating a data structure from the measured vibration data 1a or from the feature quantity 1b calculated from the vibration data 1a. The columns of the input matrix 3 represent the vibration data 1a at specific positions of the piston or the feature quantity 1b calculated from the vibration data 1a. On the other hand, the rows of the input matrix 3 represent the change over time of the vibration data 1a or the feature quantity 1b. As a result, the input matrix 3 becomes either an N×N matrix or an N×M matrix. Depending on the difference in the positions of the vibration sensors, conclusions can be drawn about the severity of degradation and the type of seal device. Following step (b), in step (c), a simulation is started that includes a plurality of simulation steps. In step (c.1), a simulation based on the state field of vibration dynamics is executed. In this example, the time-dependent Ginzburg–Landau model 4 is used. If applied correctly, the results of the existing numerous state field models (4) that can be used to describe the propagation of faults in a closed system are equivalent. To solve the partial differential equation, the discrete version of the Laplace operator Δ is recursively applied to the output matrix 5 of the previous simulation step in each simulation step. In the example of Figure 1, the output matrix 5 of the first simulation step (c.1) is used as the input matrix 3′ of the second simulation step (c.2), thereby generating the output matrix 5′. Figure 2 shows the output matrices 5, 5′ after different numbers of simulation steps in the form of a heat map. In the figure belonging to time n = 1, a plurality of white and black dots are shown. These represent the values (states) of the output matrix 5 after the first simulation step. As the number of simulation steps is repeated, the output matrix only changes slightly.In an embodiment of the method according to the present invention shown in FIG. 1, the Laplacian matrices 6a, 6a′, 6a′′ are formed after each of steps (c.1) to (c.n) for the obtained output matrices 5, 5′, 5′′. This can be done, for example, by multiplying each of the output matrices 5, 5′, 5′′ by its transpose matrix (not shown). Next, the eigenvalues 6b, 6b′, 6b′′ of each of the Laplacian matrices 6a, 6a′, 6a′′ in the simulation steps are calculated. This is explicitly shown in the flowchart of FIG. 1 for processing steps (d.1), (d.2), (d.n). As explained in steps (f.1), (f.2), (f.n), in each simulation step, exactly one parameter 6c is selected from the calculated eigenvalues 6b, 6b′, 6b′′. In the example described here, the selected parameter 6c is the second smallest eigenvalue in each simulation step. FIG. 3 shows an example of the progression of the real part Re of the second smallest eigenvalue 6d over n simulation steps of the simulation. In step (g) of the method, the simulation step in which the second smallest eigenvalue first falls below a predetermined threshold value (predetermined threshold) 8a close to zero is identified. This simulation step is returned as the simulation result 9, in units of simulation time, particularly time, minutes, seconds, and in some cases future time, particularly calendar date. The simulation time is calculated from the distance between the measured values of the input matrix 3 generated from the vibration data 1a or from the characteristic quantity 1b calculable from the vibration data 1a, that is, from the number N of sampling time points. For example, an undercut of the predetermined threshold 8a after the simulation step (n) of "750" can be converted to a time unit of about half a day by dividing "750" by 1440 minutes (24 hours × 60 minutes) when using the average per minute. Thereafter, according to step (h) of the method, by repeating step (b) of the method, the input matrix 3 is generated. (c) By executing the simulation step (4), the Laplacian matrix 6a is generated. (d) The eigenvalue 6b is calculated. (f) The second smallest eigenvalue 6d is selected and stored.Identify the number of simulation steps as the number of simulation steps until the value of (g) falls below a threshold close to zero. By returning the value of this number of simulation steps as the simulation time, further simulations are executed. Finally, in step (i), a curve is fitted to the simulation results 9, 9′, 9′′ using an appropriate method. Calculate the intersection time 7 as the time at which the curve intersects a predefined lower boundary (lower limit value) 8c. In Fig. 4a, all the simulations started (x-axis) are plotted against the number of simulation steps (y-axis) until the curve falls below a predefined threshold value 8a. Fig. 4b shows an example of the adjustment of the simulation results 9, 9′, 9′′ obtained by local linear regression using Loess smoothing over the entire simulation results 9, 9′, 9′′ shown in Fig. 4a. The failure time or remaining useful life (remaining service life) is calculated from the intersection of the regression curve and the predefined lower boundary 8c, which is the x-axis in this example.

[0056] Fig. 5 shows the change over time of the critical index CI (criticality index). This curve can be used to determine the point in time when the calculation of the remaining useful life starts due to the start of irreversible degradation treatment in the sealing device. This point in time can be determined using the statistical distribution of the simulation results. In particular, when a certain percentage of the simulation results is below a limit value, this limit value is "1" in Fig. 5 and is indicated by the white line approximately 92 days later (damage occurrence, damage onset).

[0057] Fig. 6 shows the transition of the accuracy of the prediction error in calendar days six weeks before the actual useful life of the sealing device ends, that is, before the occurrence of a failure or total failure of the sealing device. As can be seen from Fig. 6, the difference between the predicted useful life and the occurrence of the actual damage event is very small. In particular, it shows that the accuracy of the predicted useful life is very high on the last day before the actual failure of the sealing device occurs.

Explanation of symbols

[0058] 1a…Vibration data 1b…Feature quantity that can be calculated from vibration data 1c…Position data 1d…Time interval of oscillation signal 2a…Average vibration value 2b…Average feature quantity 3…Input matrix 4…Model 5…Output matrix 5a…Transposed matrix of output matrix 6…Algebraic connectivity 6a…Laplacian matrix 6b…Eigenvalue of Laplacian matrix 6c…Parameter 6d…Second smallest eigenvalue of Laplacian matrix 7…Prediction time 8a…Threshold value 8b…Critical value 8c…Lower bound 9…Simulation result 10…Sealing device 10a…Piston rod packing 10b…Cylinder valve 11…Piston compressor 11a…User interface of reciprocating device 12…Part of reciprocating device 13…Vibration sensor 14…Position sensor 15…Ground station 16…Cylinder head 17…Crosshead 20…Preventive maintenance system 21…Computing device 22…Processor (processing device) 23…Data transmission device 24…Storage device 25…Measurement system 30…Computer-readable medium 31…Computer program 32…Program code L…Simulated leakage L′…Measured leakage M…Segment of the movement of the reciprocating part N…Sampling time point (sampling time)

Claims

1. A method as a computer-implemented method for determining the wear degree of a sealing device (10) of a reciprocating compressor (11) and predicting the remaining service life, the method comprising: (a) providing vibration data (1a) or a feature quantity (1b) of a reciprocating part (12) of the reciprocating compressor (11), wherein the feature quantity (1b) can be calculated from the vibration data (1a), and the vibration data (1a) or the feature quantity (1b) can be traced back to a plurality of segments M of the movement of the reciprocating part (12) of the reciprocating compressor (11), the step of providing the vibration data (1a) or the feature quantity (1b); (b) generating an input matrix (3) from the vibration data (1a) or from the feature quantity (1b) that can be calculated from the vibration data (1a), the input matrix (3) having a dimension (M×N) determined by the number M of segments and the number N of sampling times, the step of generating the input matrix (3); (c) performing a simulation having a plurality of simulation steps by applying a model (4) to the input matrix (3), wherein the propagation of errors in a closed system can be described by using the model, and each simulation step generates an output matrix (5, 5'), and the output matrix (5) of the nth simulation step is used as the input matrix (3') of the (n + 1)th simulation step, the step of performing the simulation; (d) optionally, forming a Laplacian matrix (6a, 6a') for each output matrix (5, 5') by multiplying each output matrix (5, 5') by its transpose matrix (5a, 5a'), and calculating eigenvalues (6b, 6b') of each Laplacian matrix (6a, 6a'); (e) optionally, calculating a normalized distance of the output matrix (5') of the (n + 1)th simulation step with respect to the output matrix (5) of the nth simulation step; (f) accurately selecting exactly one parameter (6c) from among the values of the output matrices (5, 5'), the calculated eigenvalues (6b, 6b') of the optionally formed Laplacian matrices (6a, 6a'), or the optionally calculated normalized distance; Determine the number of simulation steps as the number of simulation steps that must be executed before the selected parameter (6c) falls below a predetermined threshold (8a) close to zero, and output the number of simulation steps in time units, as the executed simulation result (9), Repeating steps (a) to (g) at regular discrete time intervals to obtain a plurality of simulation results (9, 9'), and In particular, fitting a curve to the obtained simulation results (9, 9') by local linear regression and calculating the crossing time (7) as the time when the curve crosses a predetermined lower boundary (8c), comprising The selected parameter (6c) represents a measure for synchronization of the vibration data (1a) traceable to the segment M or a measure for synchronization of the feature quantity (1b) calculable from the vibration data (1a). The calculated crossing time is regarded as an indicator of the occurrence of a failure, in particular as an indicator of a complete failure, and as an indicator of the predicted end time of the remaining service life of the sealing device (10). Method.

2. The second smallest eigenvalue (6d) of the Laplacian matrix (6a) is selected as the parameter (6c), The predetermined threshold (8a) is approximately zero, In particular, the predetermined threshold (8a) has an amount of 0.000025. The method according to claim 1.

3. The provided vibration data (1a) or the feature quantity (1b) that can be calculated from the vibration data (1a) is obtained by sub-steps, and the sub-steps are Detecting at least one time signal x(t) of the vibration of the reciprocating part (12) of the reciprocating compressor (11) by at least one vibration sensor (13), Optionally, calculating the feature quantity (1b) from the recorded time signal x(t), Dividing the detected time signal x(t) of the vibration or, optionally, the calculated feature quantity (1b) into a plurality of time segments (1d), and forming an average value of the detected time signal x(t) in each time segment (1d) or, optionally, an average value of the calculated feature quantity (1b) to obtain an average vibration value (2a) or an average feature quantity (2b). Detecting position data of the reciprocating part (12) of the reciprocating compressor (11) by at least one position sensor (14) and providing the detected position data (1c); Based on the detected position data (1c), dividing the movement of the reciprocating part (12) into a plurality of the segments M, preferably of equal size to each other; and Assigning the average vibration value (2a) or the average feature quantity (2b) to the segment M of the movement such that each average vibration value (2a) or each average feature quantity (2b) always corresponds to the same segment M of the movement of the reciprocating part (12); The method according to claim 1 or 2, comprising the steps above.

4. The prediction of the crossing time (7) when the selected parameter (6c) is below the predetermined threshold value (8a) is performed only after the parameter (6c) reaches a predetermined critical value (8b) indicating the start of irreversible deterioration processing in the sealing device (10). The method according to any one of claims 1 to 3.

5. The model (4) applied to the input matrix (3) is a time-dependent Ginzburg-Landau model (TDGL). The method according to any one of claims 1 to 4.

6. The method further comprises a data transmission step for transmitting the vibration data (1a) or the feature quantity (1b) to at least one ground station (15) and / or for transmitting the predicted remaining service life of the sealing device (10) to a user interface (11a) of the reciprocating compressor (11), and at least the simulation step and the prediction of the crossing time (7) when the selected parameter (6c) is below the predetermined threshold value (8a) are executed entirely or partially on the ground using the ground station (15). The method according to any one of claims 1 to 5.

7. The sealing device (10) is a piston rod packing (10a) that seals against the piston rod or a cylinder valve (10b) that defines a compression chamber. The method according to any one of claims 1 to 6.

8. The execution of the method is automatically started at a time selectable by the user or at fixed time intervals. The method according to any one of claims 1 to 7.

9. The method comprises, as a sub-step, ​ a step of transmitting to the user in real time in particular the specified crossing time (7) and / or the predicted end time of the remaining service life of the sealing device (10), and / or a step of estimating the degree of wear of the sealing device (10) from the behavior of the parameter (6c) over the number of simulation steps executed, and transmitting the obtained degree of wear to the user in real time in particular; The method according to any one of claims 1 to 8, comprising:

10. A preventive maintenance system (20) for use with a reciprocating compressor (11), the preventive maintenance system (20) having computing means (21) comprising at least one processor (22), the computing means (21) comprising: a step of receiving position data (1c) of the reciprocating part (12) of the reciprocating compressor (11); a step of receiving vibration data (1a) of the reciprocating part (12) of the reciprocating compressor (11) or a feature quantity (1b) that can be calculated from the vibration data (1a); a step of assigning the vibration data (1a) or the feature quantity (1b) to a plurality of segments M of the movement of the reciprocating part (12) of the reciprocating compressor (11); a step of generating an input matrix (3) from the received vibration data (1a) or from the feature quantity (1b); a step of performing a plurality of simulations by applying a model (4) to the input matrix (3), each of the plurality of simulations comprising a plurality of simulation steps, the model (4) being capable of describing error propagation in a closed system, each simulation step generating an output matrix (5), and the output matrix (5) of the n-th simulation step being used as the input matrix (3') of the (n + 1)-th simulation step, the step of performing a plurality of simulations; optionally, forming a Laplacian matrix (6a) of each output matrix (5) and calculating eigenvalues (6b) of each Laplacian matrix (6a); optionally, calculating a normalized distance from the output matrix (5') of the (n + 1)-th simulation step to the output matrix (5) of the n-th simulation step; A step of selecting a parameter (6c) from among values of the output matrix (5, 5'), or from calculated eigenvalues (6b, 6b') of the optionally formed Laplacian matrix (6a, 6a'), or from optionally calculated normalized distances, wherein the selected parameter (6c) represents a measure for synchronization of the vibration data (1a) traceable to the segment M or a measure for synchronization of the feature quantity (1b) calculable from the vibration data (1a), the step of selecting the parameter (6c); Determining the number of simulation steps as the number of simulation steps that must be executed until the selected parameter (6c) falls below a predetermined threshold (8a) close to zero, for each case, as simulation results (9, 9') executed, and outputting the number of simulation steps in time units; A step of fitting a curve to the obtained simulation results (9, 9'), particularly by local linear regression, and calculating an intersection time (7) at which the curve intersects a predetermined lower boundary (8c), wherein the calculated intersection time (7) is regarded as an indicator of the occurrence of a failure and as an indicator of the prediction end time of the remaining useful life of the seal device, the step of calculating the intersection time (7); Outputting the remaining useful life of the seal device (10) of the reciprocating compressor (11); configured to perform; A preventive maintenance system (20).

11. The preventive maintenance system (20) further is designed to detect the position of the reciprocating part (12) of the reciprocating compressor (11), and at least one position sensor (14) communicably coupled to a computing device (21) as the computing means; is designed to detect a time signal x(t) of the vibration of the reciprocating part (12) of the reciprocating compressor (11), and at least one vibration sensor (13) communicably coupled to the computing device (21); and comprises The computing device (21) not only divides the detected time signal x(t) of the vibration into a plurality of time segments (1d), but also forms an average vibration value (2a) for each of the time segments (1d), and preferably divides the motion of the reciprocating part (12) into a plurality of segments M of equal size based on the determined position, and is configured to assign the average vibration value (2a) to the segment M of the motion so that each average vibration value (2a) always corresponds to the same segment M of the motion of the reciprocating part (12). The preventive maintenance system (20) according to claim 10.

12. At least one of the vibration sensors (13) is arranged on the cylinder head (16) or the crosshead (17) of the reciprocating compressor (11). The reciprocating compressor (11) is associated with the reciprocating part (12). The preventive maintenance system (20) according to claim 11.

13. Comprising the preventive maintenance system (20) according to any one of claims 10 to 12. Reciprocating compressor (11).

14. Designed as the reciprocating compressor (11) with a vertical piston motion. The reciprocating compressor (11) according to claim 13.

15. A computer-readable storage medium (30) embodying a computer program (31), wherein the computer program (31) is configured to cause a processor (22) of a computer to execute the method according to any one of claims 1 to 9 when executed by at least one processor (22) of the computer, and comprises a computer-readable program code (32). Computer-readable storage medium (30).

16. For monitoring the deterioration of the sealing device (10) of the reciprocating compressor (11), and / or for predicting the remaining service life of the sealing device (10) of the reciprocating compressor (11), particularly for predicting the remaining operating time or replacement date, and / or for reproducing the leakage occurring in the sealing device (10) of the reciprocating compressor (11), The method according to any one of claims 1 to 9.

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