Method and Apparatus for Compressing Data

Abstract

Apparatus and a method for compressing data that represent a time-dependent signal that includes a multiplicity of time-dependent signal elements, wherein a multiplicity of spectra are received, where each spectrum corresponds to one of the time-dependent signal elements, and where each spectrum includes a multiplicity of frequencies f.sub.j and a multiplicity of amplitudes of the multiplicity of frequencies, wherein a compressed data record is generated, wherein a respective number of coefficients of an autoregressive model for the multiplicity of amplitudes of each of the multiplicity of frequencies is ascertained, and wherein the compressed data record is generated, where the compressed data record includes at least the number of coefficients and the frequencies associated with the coefficients.

Claims

1.-16. (canceled)

17. A method for compressing data representing a time-dependent signal comprising a multiplicity of time-dependent signal elements, the method comprising: receiving a multiplicity of spectra, each spectrum of the multiplicity of spectra corresponding to one time-dependent signal element of the time-dependent signal elements, and each spectrum comprising a multiplicity of frequencies and a multiplicity of amplitudes of the multiplicity of frequencies; generating a compressed data record; ascertaining a respective number of coefficients of an autoregressive model for a multiplicity of amplitudes of each of the multiplicity of frequencies; and generating the compressed data record, said compressed data record comprising at least a number of coefficients and the frequencies associated with the coefficients.

18. The method as claimed in claim 17, further comprising: generating a timestamp for each of the time-dependent signal elements; wherein the timestamp represents a time at which the respective time-dependent signal element was captured.

19. The method as claimed in claim 17, wherein the number of coefficients is varied based on a classification value; wherein the classification value represents a state of health of a technical system and is ascertained by processing the multiplicity of spectra.

20. The method as claimed in claim 18, wherein the number of coefficients is varied based on a classification value; wherein the classification value represents a state of health of a technical system and is ascertained by processing the multiplicity of spectra.

21. The method as claimed in claim 19, further comprising: comparing the multiplicity of spectra with at least one threshold value to ascertain the classification value.

22. The method as claimed in claim 19, further comprising: prompting a first, high number of coefficients to be ascertained by a first classification value, which represents a first, defective, state of health of the technical system.

23. The method as claimed in claim 21, further comprising: prompting a first, high number of coefficients to be ascertained by a first classification value, which represents a first, defective, state of health of the technical system.

24. The method as claimed in claim 19, further comprising: prompting a, in comparison with the first number, lower second number of coefficients to be ascertained by a second classification value, which represents a second, non-defective, state of health of the technical system.

25. The method as claimed in 21, further comprising: prompting a, in comparison with the first number, lower second number of coefficients to be ascertained by a second classification value, which represents a second, non-defective, state of health of the technical system.

26. The method as claimed in claim 22, further comprising: prompting a, in comparison with the first number, lower second number of coefficients to be ascertained by a second classification value, which represents a second, non-defective, state of health of the technical system.

27. The method as claimed in claim 22, wherein at least one of (i) the first number of coefficients and (ii) the second number of coefficients of an autoregressive model is determined by the Akaike information criterion.

28. The method as claimed in claim 24, wherein at least one of (i) the first number of coefficients and (ii) the second number of coefficients of an autoregressive model is determined by the Akaike information criterion.

29. The method as claimed in claim 22, wherein at least one of (i) the first number of coefficients and the second number of coefficients of the autoregressive model is determined by a Bayesian information criterion.

30. The method as claimed in claim 24, wherein at least one of (i) the first number of coefficients and the second number of coefficients of the autoregressive model is determined by a Bayesian information criterion.

31. The method as claimed in claim 22, wherein at least one of (i) the first number of coefficients and (ii) the second number of coefficients of the autoregressive model is determined such that at least one of (i) the first number of coefficients and (ii) the second number of coefficients corresponds to a number of frequencies.

32. The method as claimed in claim 24, wherein at least one of (i) the first number of coefficients and (ii) the second number of coefficients of the autoregressive model is determined such that at least one of (i) the first number of coefficients and (ii) the second number of coefficients corresponds to a number of frequencies.

33. The method as claimed in claim 27, further comprising: performing an ascertainment of reconstructed data from the compressed data record.

34. The method as claimed in claim 33, wherein a projected spectrum is ascertained from a number of coefficients in the compressed data record per timestamp.

35. The method as claimed in claim 32, wherein the projected spectrum is ascertained for at least one of the timestamps.

36. A non-transitory computer-readable medium which is loadable directly into internal memory of a digital signal processing unit and which comprises software code sections which, when executed on the digital signal processing unit causes compression of data representing a time-dependent signal comprising a multiplicity of time-dependent signal elements, the software code sections comprising: program code for receiving a multiplicity of spectra, each spectrum of the multiplicity of spectra corresponding to one time-dependent signal element of the time-dependent signal elements, each spectrum comprising a multiplicity of frequencies and a multiplicity of amplitudes of the multiplicity of frequencies; program code for generating a compressed data record; program code for ascertaining a respective number of coefficients of an autoregressive model for a multiplicity of amplitudes of each of the multiplicity of frequencies; and program code for generating the compressed data record, said compressed data record comprising at least a number of coefficients and the frequencies associated with the coefficients.

37. A signal processing unit for compressing data comprising: a reception unit for receiving a multiplicity of spectra; an autoregressive coefficient determination unit for determining a respective number of coefficients of an autoregressive model for a multiplicity of amplitudes of each frequency of the multiplicity of spectra; and a memory unit for storing a compressed data record; wherein the signal processor is configured to: receive the multiplicity of spectra, each spectrum of the multiplicity of spectra corresponding to one time-dependent signal element of the time-dependent signal elements, and each spectrum comprising a multiplicity of frequencies and a multiplicity of amplitudes of the multiplicity of frequencies; generate a compressed data record; ascertain a respective number of coefficients of an autoregressive model for a multiplicity of amplitudes of each of the multiplicity of frequencies; and generate the compressed data record, said compressed data record comprising at least a number of coefficients and the frequencies associated with the coefficients.

38. The signal processing unit as claimed in claim 37, further comprising: a correlation unit for determining a correlation coefficient between the data and the reconstructed data.

39. The signal processing unit as claimed in claim 37, further comprising: a spectrum unit for each of the time dependent signal elements for computing the respective spectrum.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The invention for compressing data that represent a time-dependent signal A.sub.i(t) will now be described with reference to the attached drawings. The drawings and the embodiments described below are used to describe an exemplary embodiment, but are not restricted thereto. In the figures below, like elements are provided with like reference symbols, in which:

[0028] FIG. 1 shows a schematic depiction of a state monitoring system that comprises a data capture unit and a signal conditioning unit;

[0029] FIG. 2 shows data that represent a time-dependent signal;

[0030] FIG. 3 shows various units of a signal conditioning unit;

[0031] FIG. 4 shows a multiplicity of spectra;

[0032] FIG. 5 shows an amplitude array;

[0033] FIG. 6 shows a flowchart that illustrates the various method steps of the method in accordance with the invention;

[0034] FIG. 7 shows a flowchart that illustrates a further sub-aspect of the method in accordance with the invention as a flowchart;

[0035] FIG. 8 shows the time profile of a time-dependent FFT amplitude of measurement data and compressed data;

[0036] FIG. 9 shows a compression ratio that illustrates the compression on the basis of the number of autoregressive coefficients; and

[0037] FIG. 10 shows the profile of the R.sup.2 coefficient based on the number of correlation coefficients.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

[0038] FIG. 1 shows a state monitoring system 10 for monitoring the health of a technical system 20, e.g. a motor.

[0039] The state monitoring system 10 comprises a data capture unit 30 and a signal processing unit 40. The data capture unit 30 captures a time-dependent signal A(t) 45, which is a continual signal (characterized by the independent time variable t) that relates, for example, to vibrations of the motor 20. The data capture unit 30 provides the signal processing unit 40 with data D(nT) 65. The data D(nT) 65 are a discrete-time (characterized by the independent variable nT) representation of the time-dependent signal A(t) 45, which is progressively processed in order to compress the data D(nT) 65.

[0040] The data capture unit 30 comprises a sensor 31, a signal conditioning unit 32 and an analog-to-digital converter 33 (ADC). The sensor 31 captures the time-dependent signal A(t) 45 and makes it available to the signal conditioning unit 32 for processing. The signal conditioning unit 32 conditions the time-dependent signal A(t) 45 and makes a conditioned, time-dependent signal A(t) available to the analog-to-digital converter 33. The analog-to-digital converter 33 digitizes the conditioned time-dependent signal A(t), as a result of which the data D(nT) 65 are generated. These are provided to the signal processing unit 40, where the data D(nT) 65 represent the time-dependent signal A(t).

[0041] The time-dependent signal A(t) 45 can be captured persistently (continually) by the data capture unit 30. The latter can be regarded as a multiplicity of time-dependent signal elements A.sub.i(t) (where i=1, . . . , NoS) 46-50 (cf. FIG. 2). The time-dependent signal elements A.sub.i(t) can be regarded as the time-dependent signal A(t) 45 captured during a multiplicity of different time intervals t.sub.i (where i=1, . . . , NoS) 51-55 or as a time-dependent signal A(t) 45 provided with windows in a time domain, where the window intervals correspond to the time intervals t.sub.i 51-55.

[0042] Alternatively, the data capture unit 30 can capture the time-dependent signal A(t) 45 during the time intervals t.sub.i 51-55 using a time domain provided with windows, as a result of which the time-dependent signal A(t) 45 can be regarded as a successive capture of the signal elements A.sub.i(t) 46-50.

[0043] The periods of the time intervals t.sub.i 51-55 may have been defined by a user or be defined in a fixed or variable manner, depending on the type of the time-dependent signal A(t) 45, signal processing requirements, properties of the data capture unit 30 and/or of the signal processing unit 40, etc. The periods may be microseconds, milliseconds or seconds. The time intervals t.sub.i 51-55 are always contiguous. They could, however, also overlap or be separated from one another by particular time spans.

[0044] In the present disclosure, the term signal element is defined as a section of the time-dependent signal A(t) 45 that has been captured by the data capture unit 30 during respective time intervals t.sub.i 51-55.

[0045] In this disclosure, NoS is a dimensionless unit and relates to a cardinal number of the signal elements A.sub.i(t) 46-50 that have been captured during the time intervals t.sub.i 51-55 and in total form the time-dependent signal A(t) 45. In other words, NoS represents the number of signal elements A.sub.i(t).

[0046] A multiplicity of timestamps t.sub.i (where i=1, NoS) 56-60 from the time-dependent signal A(t) 45 are determined, wherein each timestamp t.sub.i 56-60 represents a starting time for a respective signal element A.sub.i(t) 46-50 that has been captured during the time intervals t.sub.i 51-55. For example, the timestamp t.sub.i 56 denotes the starting time for the signal element A.sub.1(t) 46.

[0047] The data D(nT) 56 are a discrete-time representation of the time-dependent signal A(t) 45. For this reason, the signal elements A.sub.i(t) 46-50 result in a multiplicity of discrete-time signal elements D.sub.i(nT) (i=1, . . . , NoS; n=1, . . . , NoP) 66-70 (see FIG. 2, lower graph), where the data elements D.sub.i(nT) 66-70 are consistent with the data D(nT) 65 that are available in the time intervals t.sub.i 51-55. The data elements D.sub.i(nT) 66-70 can likewise be obtained via discrete-time domain windowing of the time-dependent signal A(t) 45. For this reason, the data D(nT) 65 comprise discrete-time representations of the multiplicity of signal elements A.sub.i(t) 51-55, i.e., the data elements Di(nT) 66-70. Furthermore, the data D(nT) 65 may also be a digital representation of the discrete-time equivalent of the time-dependent signal A(t) 45.

[0048] In this description, NoP is a dimensionless unit and relates to a cardinal number of the samples that are contained in each discrete-time signal element D.sub.i(nT) 66-70. NoP is likewise consistent with a cardinal number of the frequencies that is determinable by an NoP-point discrete Fourier transformation (DFT) that has been applied to the data elements D.sub.i(nT) 66-70.

[0049] NoP can be varied for the data elements D.sub.i(nT) 66-70 by varying the sampling rate of the analog-to-digital converter 33. NoP can also be modified by zero padding, as well as the duration of the time intervals t.sub.i 51-55 being able to be changed on the basis of the effect of a change of NoP. A variation of NoP accordingly varies the cardinal number of the frequencies that is determinable by an NoP-point DFT.

[0050] The timestamps t.sub.i 56-60 likewise represent the time of the start of the data elements D.sub.i(nT) 66-70. Therefore, the timestamps t.sub.i 56-60 comprise information regarding the time of capture of the data elements D.sub.i(nT) 66-70, i.e., the starting time for the time intervals t.sub.i 51-55, and are therefore advantageous for the compression of the data D(nT) 65, as well as for the reconstruction of the data D(nT) 65. The timestamps t.sub.i 56-60 can also represent periods for which respective signal elements A.sub.i(t) and data elements D.sub.i(nT) have been captured.

[0051] The signal processing unit 40, which is depicted schematically in FIG. 3, comprises a processor 90 and a memory unit 100 for storing compressed data. For the further explanations, NoP=6 and NoS=5 are chosen in FIG. 4.

[0052] The processor 90 comprises a spectrum unit 105, a spectral data reception unit 130, an autoregressive coefficient determination unit 140 and an optional correlation unit 150. The aforementioned units 105, 130, 140 and 150 are configured to compress the data D(nT).

[0053] The spectrum unit 105 receives the data elements D.sub.i(nT) 66-70 and computes a multiplicity of spectra S.sub.i(f) (where i=1, . . . , NoS) 106-110. The spectrum unit 105 computes the spectra S.sub.i(f) 106-110 as a multiplicity of NoP-point (discrete Fourier transformations) DFTs. The multiplicity of spectra S.sub.i(f) 106-110 comprises a multiplicity of frequencies f.sub.j (where j=1, . . . , NoP) 111-116 and a multiplicity of amplitudes a.sub.j,i (where j=1, . . . , NoP; i=1, . . . , NoS) 121-126 of the frequencies f.sub.j 111-116. The computed amplitudes a.sub.j,i 121-126 are associated with the frequencies f.sub.j 111-116 and scales of the proportions of the multiplicity of the frequencies f.sub.j 111-116 in the signal elements A.sub.i(t) 46-50. The association of the amplitudes a.sub.j,i 121-126 with frequencies f.sub.j 111-116 is a definition that a person skilled in the art knows for a spectrum of a time-dependent signal, i.e., the amplitudes are a measurement of the proportion of the associated frequency with respect to the subordinate time-dependent signal.

[0054] Schematic operation of the spectrum unit 105 is identified by 105 in FIG. 4. For each spectrum S.sub.i(f) 106-110, the horizontal axis f represents a frequency (f.sub.j 111-116). The vertical axis A represents an amplitude, i.e., the amplitudes a.sub.j,i 121-126 of the frequencies f.sub.j 111-116 of the underlying time-dependent signal A(t).

[0055] The operation of the spectrum unit 105 is explained below. The data elements D.sub.i(nT) 66-70 are received by the signal processing unit 40 and buffer-stored as data blocks of a particular length in the memory unit 100. Each data block corresponds to each of the data elements D.sub.i(nT) 66-70 that has been captured during each of the time intervals t.sub.i 51-55. Each data block is provided with a respective timestamp t.sub.i 56-60 for identifying the starting time of the capture of the data elements D.sub.i(nT) 66-70. The data blocks that correspond to the data elements D.sub.i(nT) 66-70 are then received and processed for the purpose of computing the spectra S.sub.i(f) 106-110.

[0056] In the presently contemplated exemplary embodiment, the amplitudes a.sub.j,i 121-126 and the frequencies f.sub.j 111-116 of the spectra S.sub.i(f) 106-110 are depicted as two dimensional amplitude arrays 128 (cf. FIG. 5) that comprise rows and columns. By way of example, the array shown in FIG. 5 comprises NoP rows, which represent the NoP f.sub.j 111-116 frequencies, and NoS columns, which represent the NoS data elements D.sub.i(nT) 66-70.

[0057] Each of the spectra S.sub.i(f) 106-110 is computed by respective data elements D.sub.i(nT) 66-70. For this reason, NoS also represents the number of spectra.

[0058] The spectral data reception unit 130 receives the spectra S.sub.i(f) 106-110 from the spectrum unit 105. The spectral data reception unit 130 generates a vector of the amplitudes a.sub.j,i 121-126 and one of the frequencies f.sub.j 111-116 contained in the spectra S.sub.i(f) 106-110. This associates a vector a.sub.j,i 121-126 with a respective timestamp t.sub.i 65-60.

[0059] In the amplitude array 128, each column i of the NoS columns is denoted by a respective timestamp t.sub.i 65-60 that corresponds to the respective data elements D.sub.i(nT) 66-70. Correspondingly, each row j of the NoP rows is denoted by a respective frequency f.sub.j 111-116. For example, the first column is identified by the timestamp t.sub.1 56 and contains all amplitudes a.sub.j,i 121-126 of the frequencies f.sub.j 111-116 that have been computed from the spectra S.sub.1(f) 106. Correspondingly, the first row is identified by f.sub.1 111 and contains all amplitudes a.sub.j,i 121-126 of the frequency f.sub.1 111 that have been computed from the number of spectra S.sub.i(f) 106-110.

[0060] FIG. 5 shows an example of the amplitude array 128 for five spectra S.sub.i(f) 106-111 (NoS=5) and six frequencies f.sub.j 111-116 (NoP=6), as is generated by the spectral data reception unit 130. The column values a.sub.1,1 121 to a.sub.6,1 126 represent, by way of example, the values of the six frequencies f.sub.1 111 to f.sub.6 116 that have been determined by the spectrum S.sub.1(f) 106 and computed for the data elements D.sub.1(nT) 66 with the timestamp t.sub.1 56. The column values a.sub.3,1 123 to a.sub.3,5 123 represent the values of the third frequency f.sub.3 113 that have been determined by the spectrum S.sub.1(f) 106 to S.sub.5(f) 110 and respectively computed for the data elements D.sub.1(nT) 66 to D.sub.5(nT) 70 and each bear the timestamp t.sub.1 56 to is 60.

[0061] The volume of data accumulating in the process can assume a very large extent. In order to be able to reduce the memory space required for storage, but also the bandwidth required for transmission, there is provision for compression. After the amplitude array 128, as described above, has been ascertained, the method described in FIG. 6 is now used to perform the further processing.

[0062] In a step 300, the spectra S.sub.i(f) 106-110 are ascertained, as described above, so that the multiplicity of amplitudes a.sub.j,i 121-126 of each of the multiplicity of frequencies f.sub.j 111-116 is available for each timestamp t.sub.i 56-60.

[0063] A step 310 involves a respective number of coefficients of an autoregressive model for the multiplicity of amplitudes a.sub.j,i 121-126 of each of the multiplicity of frequencies f.sub.j 111-116 being ascertained for each spectrum S.sub.i(f) 106-110. This means that the number of coefficients of the chosen autoregressive model is computed for the column values a.sub.1,1 121 to a.sub.6,1 126, that the values of the six frequencies f.sub.1 111 to f.sub.6 116 that have been determined for the spectrum S.sub.1(f) 106, for the data elements D.sub.1(nT) 66 and for the timestamp t.sub.1 56. Correspondingly, a corresponding procedure is used for the further spectra S.sub.2(f) 107 to S.sub.5(f) 110.

[0064] In a step 320, the entirety of the coefficients of the autoregressive model is stored in the memory unit 100 as a compressed data record CDS. Alternatively or additionally, the ascertained coefficients of the autoregressive model for the multiplicity of amplitudes a.sub.j,i 121-126 of each of the multiplicity of frequencies f.sub.j 111-116 of the spectra S.sub.i(f) 106-110 (i.e., the compressed data record CDS) can also be transmitted via a communication link, which is not shown in the figures, to a receiver, which is likewise not shown, for storage and further processing.

[0065] Further analysis requires all of the accumulating data (i.e., the autoregressive coefficients ascertained using the method above). Consequently, it is possible for this, sometimes still substantial, volume of data to be further reduced using the method described in FIG. 7. This further reduction is based on a classification value that represents a state of health of the technical system. This is ascertained by processing the spectra S.sub.i(f) 106-110 ascertained in step 300. Adaptation of the compression rate, which can be influenced via the chosen number of coefficients of the autoregressive model for the multiplicity of amplitudes of each of the multiplicity of frequencies, is then performed based on the classification value.

[0066] The adaptation method starts with step 400. In step 410, the number of spectra S.sub.i(f) 106-110 and the resultant amplitude array 128 are determined, as has been explained above. In step 420, the classification value is determined by comparing the multiplicity of spectra S.sub.i(f) 106-110 with a threshold value. The threshold value may have been determined beforehand by experiments or numerical simulation. The threshold value can be used to stipulate whether the technical system has a fault, e.g., excessively strong vibrations of the motor 20, or whether the technical system is in a fault-free state. To this end, it is possible to stipulate a first classification value that represents a first, defective state of health of the technical system, i.e., the motor 20. A second classification value may be associated with a second, fault-free state of health of the technical system, i.e., the motor 20.

[0067] In step 430, a check is performed to determine which of the classification values is available. If the first classification value is available, then a defective state of health of the technical system is inferred and the method continues with step 440. If the second classification value has been ascertained, which represents the second, non-defective (i.e., fault-free) state of health of the technical system, then the method continues with step 450.

[0068] In step 440, which is performed when there is a first, defective state of health of the technical system, a first, high number of coefficients of the autoregressive model for the multiplicity of amplitudes a.sub.j,i 121-126 of each of the multiplicity of frequencies f.sub.j 111-116 is ascertained. This attains a comparatively lower compression rate for the data D(nT) that are to be compressed.

[0069] In step 450, which is performed when there is the second, non-defective state of health of the technical system, a second number of coefficients of the autoregressive model for the multiplicity of amplitudes a.sub.j,i 121-126 of each of the multiplicity of frequencies f.sub.j 111-116 is ascertained, where the second number of coefficients is (substantially) lower in comparison with the first number of coefficients. This attains a comparatively higher compression rate.

[0070] Although the present exemplary embodiment has been provided merely to distinguish between a defective and a non-defective state of health of the technical system, the definition of a plurality of threshold values allows finer grading of different fault states of the technical system to be performed. The scale of the state of health is then taken as a basis for choosing the number of coefficients of the autoregressive model for the multiplicity of amplitudes a.sub.j,i 121-126 of each of the multiplicity of frequencies f.sub.j 111-116.

[0071] Following the determination of the coefficients of the autoregressive model for the multiplicity of amplitudes a.sub.j,i 121-126 of each of the multiplicity of frequencies f.sub.j 111-116, the method ends at step 460.

[0072] The first and/or the second number of coefficients of the autoregressive model can be determined in different ways. To this end, it is possible, for example, to use the known Akaike information criterion (AIC) or the known Bayesian information criterion (BIC). Similarly, manual judgement by an operator is possible, the operator being able to choose the arrangement, i.e., the number of coefficients of the autoregressive model, such that the number corresponds to the number of frequencies f.sub.j. In the previously described exemplary embodiment, this would correspond to the number 6.

[0073] In order to be able to perform a data analysis and visualization of the compressed data from the coefficients of the autoregressive model (i.e., the compressed data record CDS), the spectra can be reconstructed from the coefficients for a respective timestamp t.sub.i 56-60. To this end, a projected spectrum S.sub.i(f) is ascertained from the number of coefficients in the compressed data record CDS per timestamp t.sub.i 56-60. This reconstruction can be used particularly also for denoising the original spectrum S.sub.i(f) 106-110. An exemplary embodiment is shown in FIG. 8, which illustrates the amplitude A of the ascertained spectrum S.sub.i(f) in comparison with the reconstructed spectrum S.sub.i(f) for a timestamp t.sub.i. It can clearly be seen that the reconstructed spectrum S.sub.i(f) corresponds to a projection of the originally ascertained spectrum S.sub.i(f). The projection is all the more accurate the larger the number of coefficients of the autoregressive model that has been chosen for compression.

[0074] This approach described here can be used for any spectrum S.sub.i(f) for any timestamp t.sub.i.

[0075] FIGS. 9 and 10 show the efficiency of the proposed method by way of example. The efficiency of the method is judged by a compression coefficient K and what is known as the coefficient of determination R.sup.2.

[0076] The compression coefficient K is the ratio of the initial number of input values to the number of values that need to be stored after compression. The compression coefficient Kcan therefore be computed from the ratio of the size of the input data to the size of the compressed data. This results, on the basis of the number of coefficients ARC of the regressive model, in the profile shown in FIG. 9, where the compression coefficient K is depicted over the number of coefficients ARC used for the compression.

[0077] R.sup.2 indicates how well data points fit into a statistical model, such as a line or curve. R.sup.2 is determined in accordance with the following relationship:

[00001]$R^2=1-\frac{V\left(\left(y|x\right)\right)}{V\left(y\right)}=1-\frac{{}^2}{{}_y^2}$

[0078] Here, denotes the variance in the model error of a model and .sub.y denotes the variance in the real spectrum S.sub.i(f). FIG. 10 shows the coefficient R.sup.2 over the number of coefficients ARC used for the compression. From this, it can clearly be seen that a significant compression of the spectrum is obtained with just a small number of coefficients ARC of the autoregressive model.

[0079] The proposed method allows adaptive compression that is based on the actual state of the technical system to be monitored. The compression of the spectra ascertained in a known manner by representation using a number of coefficients of an autoregressive model allows high compression rates. At the same time, the reconstruction of the originally determined spectra from the number of coefficients of the autoregressive model can involve filtering being performed, with the filter strength resulting from the number of coefficients previously used for the compression.

[0080] The data compressed using the method described can be stored as a compressed data record CDS and/or transmitted to another computation unit for further processing.

[0081] Thus, while there have been shown, described and pointed out fundamental novel features of the invention as applied to a preferred embodiment thereof, it will be understood that various omissions and substitutions and changes in the form and details of the devices illustrated, and in their operation, may be made by those skilled in the art without departing from the spirit of the invention. For example, it is expressly intended that all combinations of those elements and/or method steps which perform substantially the same function in substantially the same way to achieve the same results are within the scope of the invention. Moreover, it should be recognized that structures and/or elements shown and/or described in connection with any disclosed form or embodiment of the invention may be incorporated in any other disclosed or described or suggested form or embodiment as a general matter of design choice. It is the intention, therefore, to be limited only as indicated by the scope of the claims appended hereto.