CH717054B1 - Procedure for diagnosing a bearing. - Google Patents

Abstract

A method for diagnosing one or more bearings (10, 20) of a machine from vibration data from a vibration sensor includes creating and feeding digitized vibration data (46) into a processor (50). A frequency spectrum (1) is calculated from the digitized vibration data (46) in the processor (50). The frequency spectrum (1) is divided into a plurality of frequency ranges, each of the frequency ranges comprising a plurality of frequency lines. A data vector is determined from the frequency lines, with a signal strength being determined from each data vector, the signal strength corresponding to the bearing defect or the position of the bearing (10, 20) in the machine and therefore clearly assigned to the corresponding bearing (10, 20) of the machine can be, so that the operating condition or the installation location of the bearing (10, 20) in the machine can be detected at any time. A curve is formed from the data vector, using polynomial coefficients which describe the non-linearity of the curve and allow conclusions to be drawn about the type and severity of a bearing defect.

Description

background

The present invention relates to a method for diagnosing a bearing, in particular a roller bearing, ball bearing, roller bearing. For example, the method can be used in a gearbox, pump, fan or other machine containing rotating components.

In practice, defective bearings can lead to production stoppages, accidents and consequential damage to machines and systems. For this reason, attempts have been made for decades to detect imminent bearing damage before a bearing defect can even occur.

Proven measures to achieve high reliability are oversizing and age-related and increasingly condition-dependent replacement of the camp, the replacement of the camp must be done early to avoid a sudden failure of the bearing.

[0004] Due to the technical development, new possibilities for the diagnosis, control and condition monitoring of such bearings have arisen. With regard to the crack detection of bearings, these were, for example, optical, metallurgical, magnetic and radiological methods. The disadvantages of these methods are the dismantling and assembly work, the lack of accessibility, the limited informative value (e.g. regarding the depth of the crack) and a restriction of the period for diagnosis, control or monitoring to service intervals.

By means of vibration measurement technology, monitoring of rotating parts is now easily possible and is also used successfully in mechanical engineering to monitor the vibration amplitudes of roller bearings and gears.

State of the art

[0006] JP2009020090 A discloses a method and a device for diagnosing the condition of a ball bearing. In this case, vibrations which are generated during operation of the ball bearing are detected by means of an acceleration sensor. The vibration signals are transmitted to a signal processing unit and converted into a digital signal. The level of the frequency spectrum is determined using a frequency analysis. Abnormal vibration frequencies originating from a defective ball bearing part are determined based on a predetermined mathematical relationship, thereby obtaining the level of the frequency spectrum associated with the abnormal vibration frequency. The level of the frequency spectrum thus obtained is compared and adjusted with a threshold value which is set individually for each frequency of the fundamental wave having the abnormal vibration frequency and its harmonics, whereby the magnitude thereof is determined so that the existence of an abnormality of a ball bearing part can be determined.

In a further development of this method according to JP2009020090 A, according to US2014214363 A1, instead of a threshold value, a data set from a database is used, which contains the information on the frequencies of characteristic vibrations from a plurality of bearing manufacturers, in particular also information on the error frequencies of the vibrations that occur , when a certain bearing defect occurs. This information about the error frequencies can come from the manufacturers of the bearings or can be determined during operation of the bearing and stored in a memory unit. The information on the error frequencies of the vibrations from different manufacturers is available in the database for the known bearing error frequency types. These bearing error frequency types therefore include the error frequencies for an outer shell bearing error, an inner shell bearing error, a cage error or a rolling element error. These bearing error frequencies are stored in the database for different manufacturers, for different types of bearings and for different installation locations of the bearings in the relevant machine.

[0008] With the measurement method according to US2014214363 A1, the installation location of a bearing from a specific manufacturer can therefore be determined if the bearing manufacturer is known. Conversely, if the installation location of the bearing is known, it can be determined which bearing has been installed by which manufacturer at a specific installation location. The measuring method can also be used to determine which bearing of the machine has a defect by overlaying the measured frequency ranges with the frequency ranges specified by the manufacturers and comparing them with them. For this purpose, the maximum vibration amplitudes are determined for each frequency range and compared with the corresponding frequency ranges of the different manufacturers. The frequency ranges that show the greatest similarity also have the highest vibration amplitude. It is therefore possible in this way to identify the installation location of a defective bearing from a specific manufacturer on the machine or, if the installation location is known, to identify any defective bearing from any manufacturer.

Finally, US2018/0347548A1 describes a method according to which the amplitudes of bearing defect frequencies and their harmonics are used to determine an increase in the signal energy in order to calculate a defect factor therefrom. The defect factor is compared with a threshold value, determined empirically, in order to draw conclusions about the condition of the bearing. Methods for comparing a signal energy or an amplitude with threshold values are widespread, since a signal analysis is easy to do. The present invention is based on the pattern of distribution of signal energy or amplitude and not a correlation between defect frequencies and signal energy.

In the condition monitoring (condition monitoring) and the condition prediction (predictive maintenance) of rolling, roller or ball bearings vibration signals are thus used. The vibration signals are recorded by means of an acceleration sensor, e.g. based on piezo or MEMS, which is ideally attached close to a bearing and fed to a digitization unit as an analogue electrical signal. The vibration signal digitized in this way is then broken down into its frequency components using a Fourier analysis (FFT). The resulting frequency spectrum is used to analyze whether there is a defect in the bearing. A bearing manufacturer provides the frequencies that indicate a bearing defect. A bearing failure can be diagnosed when a peak or peak is found at the frequency specified by the bearing manufacturer. The relevant bearing defect frequencies can be found on-line at a bearing manufacturer's website. These bearing defect frequencies can also be calculated knowing the bearing geometry, diameter and number of rolling elements for the bearing, but this bearing manufacturer information is not always available. A peak is understood below to mean a peak value, in particular a local peak value in a data series.

A distinction is usually made between four types of bearing defect frequencies: rolling element, cage, inner bearing shell or outer bearing shell, see also Fig. 3.

Bearing defect frequency determined for the outer bearing shell BPFO = ballpass frequency, outer race Bearing defect frequency, determined for the bottom bracket shell BPFI = ballpass frequency, inner race Bearing defect frequency of the bearing cage FTF = fundamental train frequency, cage speed Bearing defect frequency of the rolling element BSF = Ball (roller) spin frequency where: n number of balls, rollers or rolling elements in the bearing fr rotational frequency of the shaft in [1/s] d ball diameter [m] D mean ball track diameter [m] φ bearing contact angle [-]

An expert can use the frequency spectrum and the bearing defect frequencies to visually determine whether a bearing defect is present. To do this, he looks at the frequency spectrum, as shown for example in FIG. 1, and compares the position of the peaks in the frequency spectrum with the bearing defect frequencies characteristic of each of the defects mentioned above. If the peaks are at the positions of one of the bearing defect frequencies that are typical for the corresponding defect, then the corresponding defect is probably present in the bearing.

[0014] In automated bearing defect monitoring, a computer program performs the same analysis that an expert does, but without having the ability to interpret it. The current bearing defect frequencies must be derived from the bearing defect frequencies specified by the manufacturer. These are normalized to 60 RPM or 1 Hz, so they are scaled with the speed. In the case of an outer bearing shell defect, this is BPFO' = BPFO * RPM/60. Because only the harmonic frequencies are relevant for an outer bearing shell defect, the relevant bearing defect frequencies for an outer bearing shell defect are: BPFO', 2* BPFO' and 3* BPFO'.

The computer program now compares whether there are significant peaks in the frequency spectrum at the specified (relevant) bearing defect frequencies, or measures the strength of the peaks or outputs their vibration speed value. Depending on the signal strength, the bearing is then classified as harmless, conspicuous, defective or critical, e.g. for RMS values according to the ISO-10816-3 standard. Analogously to this assessment, the computer program provides a report on the condition of the bearing or makes a recommendation for a service technician.

[0016] The quality of automated bearing defect monitoring therefore depends to a large extent on the quality of the manufacturer's information about the relevant bearing defect frequencies. There are several sources of error that can influence or even affect the accuracy or correctness of these bearing defect frequencies: Frequencies supplied by the bearing manufacturer are incorrect or inaccurate. The current bearing natural frequencies no longer match the bearing defect frequencies, for example due to wear or aging processes that change the bearing geometry. An inaccurate or incorrect measured value of a speed measurement means that the bearing defect frequencies sought are located at a different location in the frequency spectrum.

[0017] Sources of error of this type cannot be identified using the previously known computer programs, since these computer programs do not automatically recognize and correct such deviations, whereas a trained expert recognizes these deviations during visual analysis.

object of the invention

It is the object of the invention to develop a method which can deal with age-related frequency deviations or those caused by inaccurate measured values, so that the output of incorrect measured values or recommendations can be prevented.

[0019] This makes it possible to detect bearing defects for any bearing at an early stage, so that a failure of the bearing can be avoided with a high level of reliability.

A further object of the invention is to provide a method for monitoring or status diagnosis of a bearing in order to already detect a defect before a further load can lead to a possible failure of the bearing.

Description of the invention

The object of the invention is achieved by a method for diagnosing a bearing according to claim 1. Advantageous exemplary embodiments of the method are the subject of claims 2 to 11. A preferred use of the method is the subject of claims 12 and 13.

When the term "for example" is used in the following description, this term refers to exemplary embodiments and/or embodiments, which is not necessarily to be construed as a more preferred application of the teachings of the invention. Similarly, the terms "preferably", "preferred" should be understood as referring to one example from a set of exemplary embodiments and/or embodiments, which should not necessarily be construed as a preferred application of the teachings of the invention. Accordingly, the terms "for example," "preferably," or "preferred" may refer to a plurality of exemplary embodiments and/or embodiments.

The following detailed description contains various exemplary embodiments of the method according to the invention for monitoring or diagnosing the condition of a bearing. The description of any particular method is to be considered as exemplary only. In the specification and claims, the terms "include", "comprise", "have" are interpreted as "including but not limited to".

The term "method for diagnosis" can also be understood as a method for monitoring or a method for diagnosing the condition of the bearing. The following characteristics can therefore apply to each of the methods mentioned.

A method for diagnosing a bearing of a machine includes recording vibration data by a vibration sensor mounted on or in the vicinity of the bearing, the vibration data being characteristic of the operating behavior of the bearing, feeding the vibration data into a digitizing unit for digitizing the vibration data and Creating digitized vibration data, feeding the digitized vibration data into a processor, frequency spectra being calculated from the digitized vibration data in the processor. In particular, the processor can perform an FFT (Fast Fourier Transform) or an envelope spectrum from voltage, current, acceleration or vibration velocity measurements to calculate the frequency spectrum.

The frequency spectrum is divided into a plurality of frequency ranges 1 to N, each of the frequency ranges 1 to N comprising a plurality of frequency lines 1 to K.

1 shows a frequency spectrum with N=9 frequency ranges, the fundamental frequency (4) and 2 harmonics (5) and (6) and sidebands (8). Section (7) in Fig. 4 shows ±15 frequency lines, i.e. K=31, around the frequency range (6), with the frequency difference between two frequency lines corresponding to the resolution of the FFT. A data vector with N elements is determined for each of the K frequency lines, so that K data vectors are obtained for the N frequency ranges, with a signal strength being determined from each of the K data vectors, with the signal strengths being used to form a curve which can be described using a polynomial and their non-linearity, determined by assessing the level of the individual polynomial coefficients and their ratios to one another, allow statements to be made about the bearing defect and its size, so that these K signal strengths are used for statements about the bearing defect or correspond to the position of the bearing in the machine and therefore be clearly assigned to the corresponding bearing of the machine for diagnosing the bearing, so that the operating state and preferably the installation location of the bearing in the machine can be recorded at any time. A curve is thus formed from the data vector, the polynomial coefficients being used, which describe the non-linearity of the curve and allow conclusions to be drawn about the type and degree of severity of a bearing defect.

In particular, the signal strength characteristic corresponds to the bearing defect. Modern processors process data efficiently when they are presented as arrays (series, sequence) sequentially in memory. An array (in C, Pascal, Python, etc. syntax) can be one-dimensional or multi-dimensional. In the present application, a data vector is understood to mean a one-dimensional array, the data vector containing a plurality of (amplitude) values from a Fourier frequency spectrum. A multidimensional array is called a matrix. A data vector must contain multiple frequency values and not just a scalar (amplitude) value. In addition, according to the invention, a plurality of data vectors must be provided, i.e. N>1 and K>1, so that amplitude variations caused by noise or impulsive interference frequencies can be minimized. K can be a maximum of 1024. N can be a maximum of 256.

It is known to those skilled in the art that an assignment, as mentioned above, is most easily possible by comparing the signal strengths. It is also known to those skilled in the art that the operating state or the installation location of the bearing can be recorded at any time. The greater the signal strength, the more likely it is that the correct assignment is selected, in analogy to the maximum likelihood (ML) method, which is known per se to a person skilled in the art and should be mentioned at this point as an example of similar methods.

[0030] In particular, an index can be determined in each frequency range and an amplitude value associated with this index can be determined. An index corresponds to the position of an element in a data vector with K elements. The index of the largest of the K signal strengths can be referred to as the optimal index and can allow conclusions to be drawn about the effective positions of the bearing defect frequencies.

The vibration sensor can be designed in particular as an acceleration sensor or speed sensor.

The frequency spectrum is thus divided into several frequency ranges. A frequency range is a region in the spectrum that extends around a fundamental or harmonic or sideband. The frequency range includes a plurality of frequency lines. A BPFO defect has three frequency ranges, for example, and a BPFI defect has nine frequency ranges, for example. The three frequency ranges of the BPFO defect consist of the fundamental frequency and two harmonics. The nine frequency ranges of the BPFI defect consist of the fundamental frequency, two harmonics and one sideband each. For example, a frequency range can contain 31 frequency lines (±15 + 1). For example, for the fundamental frequency, the frequency line with index -15 at the first harmonic corresponds to the frequency line with index -30 because the first harmonic has a frequency twice that of the fundamental frequency. Starting from index -15 of the first fundamental frequency or its frequency range, all 9 amplitude values, one per frequency range in the case of the present BPFI example, are combined in a data vector, e.g. added up according to Fig. 5 and Fig. 6, and a measured value is formed from this. This results in 31 measured values in this example. If you have then calculated all 31 readings in this way, you can see which reading has the highest value and its associated position. The position corresponds to the index of the frequency line with the largest value in relation to the frequency range. This position can be regarded as the optimal position within each frequency range.

If there is no bearing defect, then the K measured values averaged over N frequency ranges are similar in value because it is noise and not a signal which is caused by an excitation or natural frequency.

[0034] Then, for example, a polynomial of the Pth degree can be calculated based on all measured values. The bearing defect is assessed by considering this polynomial. To do this, the polynomial is compared with a linear characteristic. If the deviation between the polynomial and the linear characteristic exceeds a threshold value, this deviation is interpreted as a bearing or gear defect. This method has the great advantage that it is more robust than the established comparison of amplitude values with threshold values and can be automated, i.e. no vibration expert has to examine the measured values.

According to one embodiment, the frequency spectra of the bearing can be compared with a plurality of error frequency spectra, for example from different bearing manufacturers. The error frequency spectra can be stored in a database. The error frequency spectra can be clearly assigned to different bearing defects.

[0036] According to one exemplary embodiment, an extended measurement window is determined for each frequency line, which takes into account a plurality of frequency lines lying next to one another. In particular, the extended measurement window can consist of a number of frequency lines lying next to one another. With the extended measurement window, not only one frequency line per frequency band in the data vector is taken into account, but 3, 5, 7 frequency lines or up to 5% of the frequency lines, based on a selected highest frequency in the frequency spectrum. The adjacent frequency lines are viewed and applied as a single frequency line. As a result, each frequency range expands in the same proportion as the width of the measurement window increases. By using an extended measurement window, the length of the data vector with K elements increases in proportion to the number of frequency lines additionally taken into account.

According to one exemplary embodiment, a search method for signal strengths is used to determine the signal strengths, as illustrated, for example, in FIGS. 2 and 5 .

The signal strengths can be one or more elements from the group of SNR, FNR (fault frequency-to-noise ratio), amplitudes, sum of amplitudes, peak values, RMS measured values, MA6*, FM4*, NB4, MA6, FM4, NA4, PAPR, crest factor, energy ratio, kurtosis. For example, a signal strength can be formed from the N measured values, e.g. SNR, with the strongest being selected from the K SNR values. Alternatively, to improve the quality of the position estimate, the SNR and e.g. MA6 can be calculated from the N measured values, and then from the double number of signal strengths (one half SNR, the other half according to this example MA6) combine them in such a way to determine the position of the bearing defect frequency to be determined with the highest probability.

According to one embodiment, the optimal position in the frequency ranges, ie an optimal index, can be determined using an optimization criterion. In particular, the optimization criterion can be one or more of the elements from the group SNR, FNR, amplitudes, sum of all amplitudes, peak values, RMS measured values, MA6*, FM4*, NB4, MA6, FM4, NA4, PAPR, crest factor, energy ratio , include kurtosis. In particular, a combination or fusion of signal strengths can take place.

According to one embodiment, the signal strengths, or the maximum measured values, can be used for a number of analyses, with further data, for example data from a number of days, being able to be taken into account.

[0041] In particular, a plurality of statistical calculations can be carried out, the result of which gives a probability for a specific frequency.

According to one embodiment, the measured values of the frequency ranges can be analyzed using a classification method which, based on the distribution function of the cumulated sum, results in a curve which can be described using a polynomial and compared with a linear curve, from which a deviation value can be determined. In particular, a classification method from the field of machine learning can be used. The classification method can be used to determine the type of bearing defect or its degree of severity, for example whether a rolling element, cage, inner bearing shell, outer bearing shell defect is present by dividing the degree of deviation into classes and each class corresponds to a degree of severity. The classification method can thus be used to determine the stage at which the bearing defect is, ie how advanced the defect is. For this purpose, a polynomial of the P-th degree or an interpolation function or a curve fitting function can be calculated, it being possible, depending on the size of the polynomial coefficients, to draw conclusions about the linearity or non-linearity and thus about the strength of the existing bearing defect. For this purpose, for example, a P-th degree polynomial can be calculated based on all measured values. Depending on the size of the polynomial coefficients, conclusions can be drawn about the linearity or non-linearity and thus about the strength, ie the degree of severity, of the existing bearing defect.

To automate the analysis, the vibration data can be processed by machine learning methods, for example by means of a neural network, in particular a deep neural network, in which case network coefficients rather than a polynomial are analyzed.

[0044] The probabilities derived from the resulting measured values can be assigned to an assessment scheme, on the basis of which the bearing is classified as good, conspicuous, critical or defective, for example.

A method according to one of the preceding exemplary embodiments can be used to determine the rotational frequency RPM of a machine. In particular, the method according to one of the preceding exemplary embodiments can be used to determine the rotational frequency RPM of a machine without a rotational speed sensor.

The method according to one of the preceding exemplary embodiments can be used to determine the bearing type or the bearing manufacturer. The procedure can also be used to prove that the wrong bearing was installed during maintenance or repairs without having to disassemble the machine.

In order to be able to determine the peaks of the bearing defect frequencies, various methods for diagnosing, monitoring or status diagnosis of a bearing can be used, on the one hand a diagnostic method which uses an extended measurement window or a diagnostic method which includes a search method for peaks (peak -Searchers).

The present method can also be used to determine the rotational frequency RPM of a machine, in which case an expensive rotational speed sensor can be dispensed with. Furthermore, the rotational frequency RPM of a machine with plain bearings can also be determined, thereby eliminating the need for expensive distance measuring sensors, for which no roller bearing defect frequencies can be determined. In particular, no speed sensor is used to determine the operating state of each of the bearings.

The present method is also suitable for determining the bearing type and bearing manufacturer, taking advantage of the small differences in the bearing defect frequencies. Alternatively, it can be determined whether and, if so, which of the four typical bearing defects is present.

The method is therefore suitable for combination with other monitoring methods in terms of expansion or backup functions. Examples of how the method can be used in the machine industry are monitoring of bearings, gears, broken shafts, broken axles, unbalance, misalignment and looseness. In the present application, misalignment monitoring is understood as meaning monitoring of an alignment error. In the present application, looseness monitoring is understood to mean loosening monitoring, for example of screws.

Brief description of the drawings

[0051] The methods according to the invention are presented below using a few exemplary embodiments. Fig. 1 shows a representation of a frequency spectrum of a bearing with a defect, the x-axis describes the frequency in Hertz and the y-axis describes the amplitude, e.g. in volts, Fig. 2 shows a flow chart of the data flow from the bearing to the display of the results of the method according to the invention for diagnosing the bearing or bearings, FIG. 3 a visualization of the parameters required for determining a specific bearing defect, FIG. 4 a method for determining an extended measurement window, FIG Creation of signal strengths in a calculation unit, FIG. 6 shows a representation of the method according to one of the preceding exemplary embodiments, FIG. 7 shows a graphic of the amplitude as a function of the frequency.

Detailed description of the drawings

If elements are drawn in dashed lines in the figures, these are options which are possible as an alternative or in addition to the exemplary embodiment drawn in solid lines.

1 shows a representation of a frequency spectrum 1 of a bearing with a defect. 1 shows an example of a defect in the inner bearing shell. The frequency spectrum contains a bearing defect basic frequency 4, a first harmonic 5, a second harmonic 6, which correspond to the error frequencies characteristic of the inner bearing shell defect. The bearing defect fundamental frequency 4, the first harmonic 5 and the second harmonic 6 are also referred to below as main frequencies. Each main frequency has sidebands 8, in Fig. 1 one sideband is shown on the left and one on the right. The frequency in Hz is plotted on the abscissa of the diagram and the amplitude in V on the ordinate.

2 shows a flow chart of the data flow from the store to the display of the results of the diagnostic method according to the invention for a measurement i, where i designates the sequence number of the measurement or of the spectrum. In the present illustration, a first bearing 10 and a second bearing 20 arranged on a shaft are monitored. For the detection of vibrations of the first bearing 10 and the second bearing 20, a vibration sensor 15, 25 are each attached to the bearing or in the vicinity of the bearing. Each of the vibration sensors 15, 25 is therefore used to record vibration data from the corresponding bearing 10, 20. In the present exemplary embodiment, the vibration sensor 15, 25 is designed as an acceleration sensor. The vibration data 16, 26 are fed into a digitizing unit 40 for digitizing the vibration data 16, 26. Digitized vibration data 46 is created from the vibration data in the digitization unit 40 . The digitized vibration data 46 is fed to a processor 50 . The processor 50 can comprise a local PC or a cloud solution. In the processor 50, frequency spectra 1 are calculated from the digitized vibration data 46. For this purpose, the processor 50 includes a calculation unit 51. The frequency spectra of the corresponding bearing are transmitted to a comparison unit 52 as result data 61. Each frequency spectrum is subdivided into a plurality of frequency ranges in the calculation unit. Each of the frequency ranges includes a plurality of frequency lines. An index is determined in each frequency range and an amplitude value associated with this index is determined. The index in the frequency domain references the position in the frequency spectrum (FFT), an amplitude value that can be determined. An index, i.e. a position in the frequency spectrum, is calculated according to round(f/fa), where f is the frequency for which the index is sought and fa is the resolution of the spectrum, e.g. 0.1 Hz.

The amplitude value belonging to the index can be summarized in a data vector as follows: The amplitude value xi,k,nd frequency lines of the nth frequency range, the kth measured value, the ith spectrum is in a first step collected. In a second step, the kth data vector is created x(i,k)=[xi,k,1,xi,k,2,...,xi,k,N]. In a third step, a new measured value is determined from the kth data vector, the signal strength s(i,k), i.e. for example SNR, FNR amplitudes, sum of amplitudes, peak values, RMS measured values, MA6*, FM4*, NB4, MA6, FM4, NA4, PAPR, Crest factor, Energy Ratio, Kurtosis calculated. When all K signal strengths are present, a new data vector can be constructed in a fourth step, y(i) = [s(i,1),s(i,2),...s(i,K)], as well as whose largest value in y(i) is found and whose position in the data vector is found, which is carried out in the comparison unit 52. In a fifth step, the data vector y(i) is sent to the classification unit 53 . In a sixth step, the data vector y(i) is analyzed in the classification unit 53 according to the following rule: if the data vector y(i) is linear or the result of a function f(y(i)), there is no bearing defect, otherwise there is a Bearing defect likely. In a seventh step, the type of bearing defect can be determined. In other words, a measured value is determined for the corresponding frequency range, with a signal strength being determined from all measured values. The signal strength corresponds to the position of the bearing 10, 20 in the machine and can therefore be clearly assigned to the corresponding bearing 10, 20 of the machine, so that the operating condition or the installation location of the bearing 10, 20 in the machine can be detected at any time, with in particular, the characteristic curve of the signal strength corresponds to the bearing defect.

According to one exemplary embodiment, the i-th frequency spectrum is subdivided into a plurality of frequency ranges by means of the calculation unit 51 . A frequency line is successively taken from each frequency range, from left to right, i.e. from low to higher frequencies in the frequency range, in a frequency range 7 as shown in FIG. 4, for example a number of nine frequency lines for the BPFI shown in FIG Malfunction. The calculation unit 51 creates a data vector x(i,k)=[xi,k,1,xi,k,2,...,xi,k,N] with k= from the frequency lines or their amplitude and phase 1,2,...K, which has a number K of data vectors in the case without an extended measurement window. The calculation unit 51 creates a number of data vectors which corresponds to the number K of frequency lines in the frequency range of the fundamental frequency Fg, where Fg lies in the middle of the K frequency lines. For BPFI bearing defect, this number K is, for example, 31 or 5% of the frequency lines related to Fmax. Fmax can be the highest of all bearing failure frequencies or an empirically determined frequency. The number of frequency ranges depends on the type of bearing defect to be examined and the selected number of harmonics and sidebands. The calculation unit 51 calculates a signal strength from each data vector. The signal strength can be selected from a variety of possible parameters, the signal strength can be a scalar value, an RMS measured value, an SNR or FNR value, the largest value of all elements in the data vector, amplitudes, sum of amplitudes, peak values, MA6*, FM4*, NB4, MA6, FM4, NA4, PAPR, Crest factor, Energy ratio, Kurtosis. The resulting K result data 61 are fed to the comparison unit 52 . The comparison unit 52 collects all result data which the iterative calculation process supplies from left to right, ie from low to higher frequencies in the frequency range. The comparison unit 52 determines the greatest value of all signal strengths from all result data received and forwards this maximum value and a new data vector, which contains all K maximum values or K*N measured values, to the classification unit 53 . From all the result data received, the classification unit 53 determines which bearing defect is most likely to be present, or which bearing manufacturer produced the bearing whose digitized vibration data was processed. Alternatively or in addition, historical result data can also be used.

The result 63 prepared in this way in the classification unit 53 can be visualized in a visualization unit 54 . The visualization unit 54 can be designed, for example, as a display or as a storage medium for the data vector or tensor.

The classification unit processes the data vector y(i) by checking f(y(i)) for linearity: the person skilled in the art has various options for this. If the values in y(i) are similar to a uniform distribution, which is the case when there is no bearing defect because the spectral values approximate white noise, then the values in y(i) are fairly equal (i.e. flat). If there is a bearing defect then typically 1-5 values in y(i) are massively larger and you get a bell-like curve when plotting y(i). Since these two cases are easy to distinguish, also graphically, it is easily possible for a person skilled in the art to distinguish between defective and good bearings. For automated analysis, the values of y(i) are presented as a cumulative sum. If the values in y(i) are equal, the result is a linear line curve, otherwise an S-curve. A difference between a linear (theory) and the effective curve based on y(i) can be determined. The effective curve is described using a P-th degree polynomial. From the difference between the 1st polynomial and those of a higher order, a measure of the severity of the development of a bearing defect can be determined. The classification unit 51 calculates such a value, which is compared with empirical values, i.e. threshold values, and triggers a warning or alarm if necessary. The user can then assess the severity of the defect in depth in the visualization unit 54 and take suitable measures.

3 shows the parameters required for determining a specific bearing defect. A bearing 10 is arranged on a shaft 5 . The bearing 10 can be seen in a side view in the left-hand part of FIG. 3 . The bearing 10 is shown enlarged on the right side of FIG. 3 in plan view. Some of the balls 13 of the ball bearing shown as an example have been omitted in order not to overload the illustration. Instead of the balls 13, other rolling bodies can also be provided, which is not shown in the drawing. The balls 13 are arranged between an inner ring 11 and an outer ring 12 . To calculate a bearing defect frequency, determined for the outer bearing shell (BPFO), the inner bearing shell (BPFI), for the bearing cage (FTF) or the rolling element (BSF), the ball diameter d or the rolling element diameter and the mean ball track diameter D or the mean rolling element track diameter, which are shown in FIG.

The data vectors do not contain any arbitrary values, because the data vectors contain the resultant of a spectral analysis (amplitude values), whose position (index range) is determined deterministically, based on the position of a bearing defect according to the calculations on page 4 for PBFI, BPFO etc , or according to the explanations above. Thus, a person skilled in the art can create the data vector and it is not arbitrary.

The installation location of a bearing cannot be determined if only the same bearing types from the same manufacturer are installed in a machine, which rarely occurs in practice and is only the case with small engines. US 2014/0214363 A1 presents an alternative method with which the idea, bearing identification and installation location can be determined. The method in this application also uses the bearing-specific defect frequencies calculated according to the formulas on page 4, but the method differs fundamentally from US 2014/0214363 A1. In US 2014/0214363 A1, only the basic frequencies are used to identify the bearing, amplitude values at the defective bearing frequencies are summed up and no measures against interference signals are proposed. And if there is no bearing defect, which is not examined and assessed in US 2014/0214363 A1, a reliable bearing identification cannot take place because the spectrum does not show any prominent peaks at the bearing defect frequencies.

4 shows another example of a method for monitoring or status diagnosis of a bearing, for which an extended measurement window is used in the calculation unit 51 . According to this exemplary embodiment, M spectral lines are used for the present expanded measurement window. e.g. M=3 (±1 around a spectral line). Not only one measured value per frequency range is taken into account, but M measured values, and the M amplitudes of the M frequency lines of a frequency range are combined, e.g. averaged or summed up. In this example, the frequency range does not expand and retains its width K. Usually, the largest measured value within the measurement window is used, for example the maximum amplitude or the RMS value of the data in the measurement window, see also US20140214363 A1.

The method for monitoring or status diagnosis of a bearing according to one exemplary embodiment thus includes an expanded measurement window, by means of which not only the signal strength, i.e. the peak, of an individual specific frequency is determined or its harmonics, i.e. fdo', 2*fdo', 3*fdo' and possibly a number of sidebands, but the environment of each bearing defect frequency is taken into account, which is shown in FIG. Fdo' stands in particular for BPFO', BPFI', FTF', BSF'.

A spectrum according to the example shown in FIG. 4 has a frequency resolution of 0.25 Hz. The rotational speed of the machine is 750 RPM, i.e. the rotational frequency is 12.5 Hz, and a maximum frequency error of ±5% (±0.625Hz) is expected. Then, to the left and right of the actual bearing defect frequency, up to a further 3 frequency lines are taken into account, i.e. ± 3. In this example, the expanded measurement window would expand each frequency range by a factor of M=7. However, if one summarizes the M amplitudes per calculation step in order to calculate a signal strength, then one does not have to extend the frequency range.

4 shows the FFT spectrum 3, which includes a bearing defect fundamental frequency 4, which is referred to below as Fg1. Furthermore, the FFT spectrum 3 includes a first harmonic 5 and a second harmonic 6 as well as one sideband frequency each. The FFT spectrum 3 can in particular be in the form of an envelope spectrum, an amplitude spectrum or a density spectrum. For the second harmonic 6, a frequency range 7 around the frequency line 6 (FFT-bin) is shown enlarged, showing a range around the second harmonic 6 with ±15 FFT frequency lines. All relevant frequency lines in the envelope spectrum, which are shown with dashed lines, show the bearing defect frequencies. These bearing defect frequencies are specified by the bearing manufacturer for a specific bearing type. A window is defined around these frequency lines, for example with ± 15 FFT frequency lines or ± 5% of Fmax, in order to find the exact frequency, i.e. the position of the peak in the spectrum. In this case, Fmax is the highest of all possible defect frequencies or can be determined empirically or by the rotational frequency.

Figure 5 shows a signal strength search method. In particular, the signal strengths can be in the form of peaks. According to this exemplary embodiment, the comparison unit 52 comprises a search unit. A search method for determining the greatest signal strength based on the measured values supplied by the calculation unit 51 can be carried out by means of the search unit. A method for monitoring or diagnosing the condition of a bearing according to this exemplary embodiment includes, in particular, a search method for peaks (peak searcher).

According to the exemplary embodiment shown in FIG. 4, a signal strength is determined in the calculation unit 51 from nine frequency ranges in FIG. The signal strength per FFT frequency line is calculated step by step across all frequency ranges. According to the present exemplary embodiment, this results in 31 measured values that correspond to a frequency range with ±15 FFT frequency lines. The same percentage tolerance includes fewer FFT frequency lines at lower frequencies. Thus, Fg1 with the FFT frequency lines -5, 2*Fgl, i.e. the second harmonic, with the FFT frequency lines -10, 3*Fgl with the FFT frequency lines -15 each describe the same percentage deviation from Fg1, 2*Fg1, 3* fg1 By means of interpolation, combination or summation of several frequency lines, all frequency lines can be brought up to the number of 31 measured values and thus the multiplicative nature of the frequency deviation can be taken into account. The position of the largest of the 31 measured values most likely corresponds to the correct deviation of the bearing defect frequency in relation to the theoretical bearing defect frequency, for example a Fg specified by the manufacturer.

In this way, the search method for peaks (peak searcher) determines the most likely correct bearing defect frequencies or the rotational frequency of a shaft based on an optimization criterion. The optimization criterion can include one of the elements SNR, FNR, amplitude value, amplitudes, sum of amplitudes, peak value, RMS measured value, MA6*, FM4*, NB4, MA6, FM4, NA4, PAPR, crest factor, energy ratio, kurtosis. SNR is used in this context as an abbreviation for signal-to-noise ratio. FNR is used in this context as an abbreviation for fault-frequency-to-noise ratio.

The signal strength Ps is determined according to the following relationship. Here i is the serial number of the measurement (e.g. day i), N is the number of frequency ranges or all defect frequencies including harmonics and sidebands of a bearing defect type, such as BPFI or BPFO, M is the width of the extended measurement window, and k=1..K is the number of frequency lines in each frequency range around the N defect frequencies:

The multiplicative nature of the frequency deviation and the absolute index in the spectrum are not taken into account in the formula. Alternatively, Ps(i,k) can also be formed from the variance of the relevant N or N*M frequency lines. The noise power is determined as follows. Half the FFT spectrum, F(i), with a bandwidth of fs/2 is used. In the ith spectrum F(i), all N or N*M frequency lines are then zeroed: F(i)→F'(i,k).

The noise power Pn is then given as

The FNR for the spectrum i is then calculated according to one embodiment.

In particular, M can be in the range of at most +/-10% of Fmax.

The measurement of the peaks or Ps of the bearing defect frequencies is then carried out without taking into account other, possibly incorrect or interfering, values in the frequency spectrum, as shown in FIG. For this purpose, a number of frequency lines are determined. In the exemplary embodiment shown in FIG. 5, the signal strength is determined from nine frequency ranges.

[0075] By means of the extended measurement window, further frequency lines can be taken into account for calculating the signal strength, for example the frequency lines on the left and right (±1) of each frequency line.

The resulting measured values of the bearing defect frequencies can be analyzed by means of a classification method, which will also be referred to below as a bearing defect classification method (BDC, bearing defect classifier). The resulting measured values can be used for a number of analyses, with further data, for example data from a number of days, being taken into account. The bearing defects can thus be identified and classified with a high degree of probability. A number of statistical calculations can be carried out using the BDC, the expanded result of which gives a probability for a specific frequency. If one considers a certain frequency range ±Fps, which corresponds to K frequency lines, linear or non-linear characteristics are obtained. If the characteristic curve is linear or almost linear, the existence of a bearing defect is unlikely. The type of bearing defect or its degree of severity can be determined by means of the classification method, for example whether a rolling element, cage, inner bearing shell or outer bearing shell defect is present. In addition, it can be determined what stage this bearing defect is in, i.e. how advanced the bearing defect is. For this purpose, for example, a polynomial of the Pth degree can be calculated or another method from the area of interpolation or curve fitting can be used for the calculation. Depending on the size of the P-coefficients, conclusions can be drawn about the linearity or non-linearity and thus about the strength of the existing bearing defect. To automate the analysis, the vibration data can be processed using machine learning methods, for example using a neural network. The legibility of the analysis and its interpretation can be further improved by presenting the results graphically, for example as a two-dimensional diagram, analogous to a spectrogram.

The probabilities derived from the resulting measured values can be assigned to an assessment scheme. For example, the probabilities can be communicated to the customer or expert in such a way that an evaluation scheme is assigned to the statistical values or the polynomial coefficients. For example, a warehouse can be classified as good, suspicious, critical or defective. A recommendation for action can be assigned to each of these assessments. The recommendation for action can include a graphic representation, for example a traffic light representation, or can be written in text form.

6 shows an illustration of the method according to one of the preceding exemplary embodiments. As described above, N=9 frequency ranges 7 are evaluated by way of example. In a first step, the amplitude value xk,n of the kth measured value of the frequency lines of the nth frequency range is collected. In a second step, the k-th data vector is created according to x(k) = [xk,n, xk,2,... xk,N]

In a third step, a new measured value, which is referred to below as signal strength, is determined from the k-th data vector, i.e. SNR, FNR, peak values, RMS measured values, MA6*, FM4*, NB4, MA6, FM4, NA4, PAPR, crest factor, energy ratio or kurtosis are calculated.

As soon as all K signal strengths are present, for example 31 signal strengths as described above, a new data vector can be created in a fourth step in the calculation unit 51 according to the calculation rule below: y(i)=[x(1), x( 2),... x(31)]thus the largest value in y(i) and its position in the vector can be found.

In a fifth step, the data vector y(i) is sent to the classification unit. In a sixth step, the data vector y(i) is analyzed, with a linear distribution of the cumulative sum being an indication that there is no bearing defect, otherwise a bearing defect is likely.

In a seventh step, the type of bearing defect is determined, for example BPFI, BSF.

Figure 7 shows a graph of the frequency spectrum F(i) showing amplitude versus frequency. The amplitude is plotted on the ordinate and the frequency on the abscissa. Three of the frequency ranges 7 are shown as an example in the frequency spectrum, the frequency range (1), the frequency range (2) and the frequency range (N). Furthermore, the bearing defect frequencies Fg1, Fg2, FgN in the corresponding frequency ranges (1, 2,...,N) are shown as examples. The bearing defect basic frequency 4 already described in connection with FIG. 4 is denoted by Fg1. The harmonics of the defective bearing frequency 4 or the sideband center frequency of the defective bearing frequency are denoted by Fg2 and FgN.

It is obvious to a person skilled in the art that many other variants are possible in addition to the exemplary embodiments described, without departing from the inventive concept. The subject matter of the invention is determined by the scope of protection defined by the claims. For the interpretation of the claims or the description, the broadest possible reading of the claims is decisive. In particular, the terms "include" or "include" shall be construed as referring to elements, components or steps in a non-exclusive sense, which is intended to imply that the elements, components or steps may be present or used, that they can be combined with other elements, components or steps not explicitly mentioned. When the claims relate to an element or component from a group that may consist of A, B, C to N elements or components, this language should be interpreted as requiring only a single element of that group, and not one Combination of A and N, B and N or any other combination of two or more elements or components of this group.

Claims (13)

A method for diagnosing a bearing (10, 20) of a machine, comprising recording vibration data (16, 26) by a vibration sensor (15, 25) attached to the bearing, the vibration data (16, 26) being characteristic of the operating behavior of the bearing (10, 20), feeding the vibration data (16, 26) into a digitizing unit (40) for digitizing the vibration data (16, 26) and creating digitized vibration data (46), feeding the digitized vibration data (46) into a processor ( 50), a frequency spectrum (1) being calculated from the digitized vibration data (46) in the processor (50), the frequency spectrum (1) being divided into a plurality of frequency ranges 1 to N, with each of the frequency ranges 1 to N having a Includes a plurality of frequency lines 1 to K, with a data vector having N elements being determined for each of the K frequency lines, so that K data vectors are obtained for the N frequency ranges, with a signal strength being determined from each of the K data vectors, with a curve being formed from signal strengths , which is described by means of a polynomial and whose non-linearity, determined by assessing the level of the individual polynomial coefficients and their ratios to one another, are used to make statements about a bearing defect and its size, so that these K signal strengths allow statements about the bearing defect and the position of the bearing (10, 20) in the machine and are therefore uniquely assigned to the corresponding bearing (10, 20) of the machine for diagnosis of the bearing, so that the operating condition and preferably the installation location of the bearing (10, 20) in the machine to each time can be recorded. 2. The method according to claim 1, wherein an extended measurement window is used for each frequency line, which takes into account a plurality of frequency lines lying next to one another. 3. The method according to any one of claims 1 or 2, wherein a search method for the signal strengths is used to determine the signal strengths. 4. The method according to any one of the preceding claims, wherein the signal strengths are an element from the group of SNR, FNR, amplitudes, sum of amplitudes, peak values, RMS measured values, MA6*, FM4*, NB4, MA6, FM4, NA4, PAPR , crest factor, energy ratio, kurtosis. 5. The method according to any one of the preceding claims, wherein measured values of the frequency ranges are analyzed using a classification method which, based on a distribution function of the cumulative sum, results in a curve which can be described using the polynomial and compared with a linear curve, from which a deviation value can be determined can. 6. The method according to claim 5, wherein the type of bearing defect or its degree of severity is determined by means of the classification method by dividing the degree of deviation into classes and each class corresponds to a specific degree of severity. 7. The method according to any one of claims 5 or 6, wherein the classification method is used to determine the stage in which the bearing defect is, wherein a polynomial of the Pth degree or an interpolation function or a curve fitting function is calculated, depending on the size of the polynomial coefficients on the Linearity or non-linearity and thus the strength of the existing bearing defect can be inferred. 8. The method as claimed in one of the preceding claims, in which, in order to automate the analysis, the vibration data are processed using machine learning methods, for example using a neural network, in particular using a deep neural network. 9. Method according to one of the preceding claims, a plurality of statistical calculations being carried out by means of the classification method according to one of claims 5 to 7, the result of which yields a probability for a specific error frequency. 10. Method according to one of the preceding claims, wherein probabilities derived from the measured values according to one of claims 5 to 9 are assigned to an assessment scheme, on the basis of which the bearing can be classified as good, abnormal, critical or defective. 11. The method according to any one of the preceding claims, wherein no speed sensor is used to determine the operating condition of each of the bearings. 12. Use of the method according to any one of the preceding claims for determining the rotational frequency of a machine. 13. Use of the method according to any one of the preceding claims 1 to 11 for determining the bearing type and bearing manufacturer.