A bearing fault monitoring method, device and system

Abstract

The present disclosure provides a bearing fault monitoring method, device and system, the method comprising: obtaining a bearing vibration signal collected from a sensing component during the operation of the bearing, the sensing component being arranged in the bearing; performing noise reduction and reconstruction on the bearing vibration signal using empirical modes to obtain the bearing vibration signal after noise reduction and reconstruction; performing envelope spectrum analysis on the bearing vibration signal after noise reduction and reconstruction to obtain an envelope spectrum analysis result; if an amplitude mutation signal is obtained based on the envelope spectrum analysis result, determining the type of the bearing fault based on the amplitude mutation signal. The method provided by the present disclosure can collect the bearing vibration signal using the sensing component arranged in the bearing, efficiently identify the early weak fault signal of the bearing, determine the type of the bearing fault, and improve the identification efficiency of the bearing fault.

Description

Technical Field

[0001] The present disclosure relates to the technical field of bearing monitoring, and in particular to a bearing fault monitoring method, device and system. Background Art

[0002] Compared with ordinary bearings, spindle bearings have the characteristics of high speed, large load, severe cage impact, high friction heat generation and high working environment temperature during operation. It is difficult to lubricate the spindle bearings well, which leads to large-scale changes in operating conditions, slipping and other faults in the spindle bearings in a short period of time. When the spindle bearings produce fatigue, wear and other faults, abnormal vibrations will occur.

[0003] At present, the vibration monitoring method is to install a vibration sensor at an appropriate position of the bearing seat or housing, and the vibration sensor collects signals and analyzes them to determine the bearing fault. Since the installation position of the vibration sensor is limited by the engine structure, usually only one vibration sensor is installed in the casing of the aircraft engine, and the vibration signal emitted by the vibration sensor in the aircraft engine system has problems such as long vibration transmission path, complex frequency components and severe signal attenuation. At the same time, there is also a lot of noise in the vibration signal, and conventional signal processing methods have certain limitations in identifying the early weak fault signals of the bearing. Summary of the invention

[0004] According to one aspect of the present disclosure, a bearing fault monitoring method is provided, comprising:

[0005] Acquiring a bearing vibration signal collected by a sensing component during the operation of the bearing, wherein the sensing component is disposed in the bearing;

[0006] Using empirical modes to perform noise reduction and reconstruction on the bearing vibration signal, and obtaining the noise-reduced and reconstructed bearing vibration signal;

[0007] Perform envelope spectrum analysis on the bearing vibration signal after noise reduction and reconstruction to obtain envelope spectrum analysis results;

[0008] If an amplitude mutation signal is obtained based on the envelope spectrum analysis result, the type of bearing fault is determined based on the amplitude mutation signal.

[0009] According to another aspect of the present disclosure, a bearing fault monitoring device is provided, comprising:

[0010] An acquisition module is used to acquire a bearing vibration signal collected by a sensing component during the operation of the bearing, wherein the sensing component is arranged in the bearing;

[0011] The acquisition module is also used to perform noise reduction and reconstruction on the bearing vibration signal using the empirical mode to obtain the bearing vibration signal after noise reduction and reconstruction;

[0012] The acquisition module is also used to perform envelope spectrum analysis on the bearing vibration signal after noise reduction and reconstruction to obtain envelope spectrum analysis results;

[0013] The determination module is used to determine the type of bearing fault based on the amplitude mutation signal if an amplitude mutation signal is obtained based on the envelope spectrum analysis result.

[0014] According to another aspect of the present disclosure, a bearing fault monitoring system is provided, comprising:

[0015] The device, bearing and at least one sensing component described in the exemplary embodiment of the present disclosure, each sensing component is wirelessly connected to the device, the bearing includes a bearing outer ring, a bearing inner ring, balls and a retaining frame for fixing the balls, the retaining frame is located between the bearing outer ring and the bearing inner ring, and each sensing component is arranged on the bearing outer ring.

[0016] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0017] processor; and,

[0018] A memory for storing programs;

[0019] The program includes instructions, and when the instructions are executed by a processor, the processor executes the method according to the exemplary embodiment of the present disclosure.

[0020] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the method according to the exemplary embodiments of the present disclosure.

[0021] One or more technical solutions provided in the exemplary embodiment of the present disclosure can obtain the bearing vibration signal collected from the induction component during the operation of the bearing. The induction component is arranged in the bearing, which can collect the bearing vibration signal in the form of magnetoelectric induction, realize the direct conversion of electrical signal to electrical signal, shorten the signal transmission path, reduce signal attenuation, and has the characteristics of simple signal acquisition, good robustness and low cost. Then, the bearing vibration signal is reconstructed by using the empirical mode to obtain the bearing vibration signal after noise reduction and reconstruction; the bearing vibration signal after noise reduction and reconstruction is analyzed by envelope spectrum to obtain the envelope spectrum analysis result. At this time, if an amplitude mutation signal is obtained based on the envelope spectrum analysis result, the type of bearing fault is determined based on the amplitude mutation signal. It can be seen that the method of the exemplary embodiment of the present disclosure can collect the bearing vibration signal by using the induction component arranged in the bearing, and efficiently identify the early weak fault signal of the bearing, determine the type of bearing fault, and improve the identification efficiency of the bearing fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0023] Figure 1 A structural diagram of a bearing according to an exemplary embodiment of the present disclosure is shown;

[0024] Figure 2 An exploded view of a sensing assembly of a bearing monitoring system according to an exemplary embodiment of the present disclosure is shown;

[0025] Figure 3 A structural diagram of a magnetic sensor according to an exemplary embodiment of the present disclosure is shown;

[0026] Figure 4 A structural diagram of a vibration acceleration sensor according to an exemplary embodiment of the present disclosure is shown;

[0027] Figure 5 A flow chart showing a method for monitoring bearing faults according to an exemplary embodiment of the present disclosure is shown;

[0028] Figure 6 A schematic block diagram of a module of a bearing fault monitoring device according to an exemplary embodiment of the present disclosure is shown;

[0029] Figure 7 A schematic block diagram of a chip according to an exemplary embodiment of the present disclosure is shown;

[0030] Figure 8 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown.

[0031] Reference numerals:

[0032] 101-bearing inner ring, 102-cage, 103-ball, 104-bearing outer ring, 105-oil filling hole, 106-sensing component, 1061-magnetic sensor, 1062-vibration acceleration sensor, 107-induction coil, 108-bayonet, 301-magnetic probe, 302-collector, 303-card foot, 401-elastic part, 402-mass block, 403-connecting part, 404-piezoelectric element, 405-base. DETAILED DESCRIPTION

[0033] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0034] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0035] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0036] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0037] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0038] Compared with ordinary bearings, aircraft engine main shaft bearings have the characteristics of high speed, large load, severe cage impact, high friction heat generation and high working environment temperature during operation. It is difficult to lubricate the main shaft bearings well, resulting in large-scale operating condition changes, slipping and other faults in the main shaft bearings in a short period of time. When the main shaft bearings produce fatigue, wear and other faults, abnormal vibrations will occur.

[0039] At present, the vibration monitoring method is to install a vibration sensor at an appropriate position of the bearing seat or housing, and the vibration sensor collects signals and analyzes them to determine the bearing fault. Since the installation position of the vibration sensor is limited by the engine structure, usually only one vibration sensor is installed in the casing of the aircraft engine, and the vibration signal emitted by the vibration sensor in the aircraft engine system has problems such as long vibration transmission path, complex frequency components and severe signal attenuation. At the same time, there is also a lot of noise in the vibration signal. Generally, the characteristic information in the vibration signal is extracted by first decomposing and filtering and then reconstructing and analyzing. This conventional signal processing method has certain limitations in the identification of early weak fault signals of bearings.

[0040] Based on the above problems, the present invention provides a bearing fault monitoring method, device and system, which utilizes the sensing component arranged in the bearing to collect the bearing vibration signal, thereby efficiently identifying the early weak fault signals of the bearing, determining the type of bearing fault, and improving the efficient identification efficiency of bearing faults.

[0041] Figure 1 A structural diagram of a bearing according to an exemplary embodiment of the present disclosure is shown, Figure 2 An exploded view of a sensing component of a bearing monitoring system according to an exemplary embodiment of the present disclosure is shown. Figure 1 and Figure 2 As shown, the monitoring system for bearing faults of the exemplary embodiment of the present disclosure includes a monitoring device for bearing faults, a bearing, and at least one sensing component 106. It should be understood that the bearings here can be cylindrical roller bearings, double-flap outer ring bearings, etc., which are not listed here one by one. The bearing includes a bearing outer ring 104, a bearing inner ring 101, balls 103, and a retainer 102 for fixing the balls. The retainer 102 is located between the bearing outer ring 104 and the bearing inner ring 101. The balls 103 are fixed on the retainer 102. The retainer 102 is used to connect the bearing inner ring 101 and the bearing outer ring 104. The bearing inner ring 101 and the bearing outer ring 104 slide relative to each other under the action of the balls 103.

[0042] When the bearing fault monitoring system of the exemplary embodiment of the present disclosure includes a plurality of sensing components 106, each sensing component 106 is arranged on the outer ring 104 of the bearing. In this case, the sensing components are arranged in the bearing, and each sensing component is wirelessly connected to the bearing fault monitoring device, which can wirelessly transmit the real-time collected bearing vibration signal to the bearing monitoring device. The bearing fault monitoring device here can be an external computing platform, or other equipment with computing functions.

[0043] In practical applications, each sensing component 106 is fixed to the outer ring 104 of the bearing by means of a bayonet 108. The sensing component 106 is at least used to collect the vibration signal of the bearing. Since each sensing component 106 is wirelessly connected to the monitoring device for bearing faults, the sensing component 106 can also transmit the vibration signal of the bearing to the monitoring device for bearing faults. The monitoring device for bearing faults provided by the exemplary embodiment of the present invention can reduce the vibration signal noise of the bearing and improve the recognition effect of the early weak fault signal of the bearing by executing the monitoring method for bearing faults after receiving the vibration signal of the bearing.

[0044] Each sensing component 106 of the exemplary embodiment of the present invention can also collect information on the impurity content of the lubricating oil and send it to a bearing fault monitoring device. The bearing fault monitoring device can determine the wear state of the bearing by analyzing the impurity content information of the lubricating oil.

[0045] For example, Figure 2 As shown, each sensing component 106 has a magnetic sensor 1061, a vibration acceleration sensor 1062, a controller, and a wireless communication device along the radial direction ( Figure 1 The controller is electrically connected to the magnetic sensor 1061, the vibration acceleration sensor 1062 and the wireless communication device through the signal circuit. The magnetic sensor 1061 is arranged in the oil filling hole 105, and the vibration acceleration sensor 1062 is arranged on the surface of the magnetic sensor 1061 away from the oil filling hole 105.

[0046] When the bearing is in operation, lubricating oil is injected into the bearing through the oil filling hole 105, and the magnetic sensor 1061 collects information on the impurity content of the lubricating oil (for example, it can magnetically absorb metal debris such as iron filings in the lubricating oil). At the same time, during the operation of the bearing, the bearing may vibrate, and the vibration acceleration sensor 1062 on the surface away from the oil filling hole 105 can collect the vibration signal when the bearing vibrates. The vibration acceleration sensor transmits the vibration signal to the controller through the signal circuit, and the controller transmits the vibration signal to the wireless communicator through the signal circuit, and the wireless communicator then transmits the vibration signal to the bearing fault monitoring device.

[0047] It can be seen that the wear state of the bearing can be determined by the above-mentioned magnetic sensor, and the vibration state of the bearing can be determined by the vibration acceleration sensor, thereby realizing real-time monitoring of the bearing operation process.

[0048] In an alternative approach, Figure 3 The structure diagram of the magnetic sensor of the exemplary embodiment of the present disclosure is shown as follows: Figure 3 As shown, the magnetic sensor 1061 provided by the exemplary embodiment of the present invention includes a magnetic probe 301 and a collector 302. The magnetic probe 301 is located in the oil filling hole 105, and the collector 302 is arranged on the magnetic probe 301. The collector 302 is electrically connected to the magnetic probe 301, and the collector 302 is electrically connected to the controller through a signal circuit.

[0049] When the magnetic probe 301 is inserted into the oil filling hole 105, the magnetic probe 301 absorbs iron chips in the oil hole, and the collector 302 can determine the impurity (iron chips) content information of the lubricating oil based on the iron chips content absorbed by the magnetic probe 301. The collector 302 converts the collected impurity (iron chips) content information of the lubricating oil into an electrical signal, and the collector 302 transmits the electrical signal to the controller through the signal circuit, and the controller transmits the vibration signal to the bearing fault monitoring device through the wireless communication device. The bearing fault monitoring device can determine the wear state of the bearing based on the electrical signal in the collector 302.

[0050] In an alternative approach, Figure 4The structure diagram of the vibration acceleration sensor of the exemplary embodiment of the present disclosure is shown as follows: Figure 4 As shown, the vibration acceleration sensor 1062 provided by the exemplary embodiment of the present invention includes an elastic member 401 , a mass block 402 , a piezoelectric element 404 , a base 405 , and a connecting member 403 , wherein one end of the mass block 402 away from the base 405 is connected to the elastic member 401 .

[0051] like Figure 4 As shown, when the bearing is a double-petal outer ring type spindle bearing, there is relative movement between the two halves of the outer ring. When the bearing is running, the elastic member 401 starts to move with the vibration of the bearing, and the elastic member 401 drives the mass block 402 to move at the same time. At the same time, since the piezoelectric element 404 is located between the base 405 and the mass block 402, the connector 403 connects the elastic member 401, the mass block 402, the piezoelectric element 404, and the base 405 in sequence, so when the mass block 402 moves, it will generate pressure on the piezoelectric element 404. The piezoelectric element 404 will convert the pressure into a vibration signal in the form of an electrical signal when it is subjected to the pressure of the mass block 402, and transmit the vibration signal to the controller through the signal circuit. The controller then transmits the vibration signal to the bearing fault monitoring device through the wireless communication device.

[0052] For example, Figure 2 As shown, the above-mentioned induction component 106 is connected with the bearing outer ring 104 by interference fit through the bayonet 108, which can facilitate the disassembly and reuse of the induction component. At the same time, since the induction coil 107 is arranged between the bayonet 108 and the magnetic sensor 1061, when the bearing rotates, it will drive the induction component to shake, and the permanent magnet in the magnetic sensor 1061 in the induction component will cut the induction coil 107 to generate electricity, and the generated electric energy will flow into the battery, and the battery will then input the magnetic sensor 1061 and the vibration acceleration sensor 1062 through the power supply circuit.

[0053] Exemplarily, one end of the connector 403 facing away from the base 405 is connected to the magnetic probe 301 . The magnetic probe 301 has a clamping foot 303 connected to the sensing component. The connector 403 can be connected to the magnetic probe 301 via the clamping foot 303 .

[0054] The sensing component also has a cavity for accommodating a controller and a wireless communicator. The cavity is arranged on the base 405 of the vibration acceleration sensor 1062. The above-mentioned sensing components are connected as a whole. The magnetic sensor 1061 and the vibration acceleration sensor 1062 are powered by a battery. Each sensor transmits the collected vibration signal to the controller in the sensing component in the form of signal transmission. The controller receives the bearing vibration signal collected from each sensor. The controller finally transmits the received bearing vibration signal to the bearing fault monitoring device in the form of wireless transmission. On this basis, when each sensor transmits the collected vibration signal to the controller, there is no need for long-path signal transmission, and at the same time, signal attenuation is reduced, effectively solving the problems of long vibration transmission path, complex frequency components and severe signal attenuation of the vibration signal.

[0055] It can be seen that the exemplary embodiment of the present disclosure can shorten the signal transmission path and reduce signal attenuation by disposing the sensing component in the bearing, thereby effectively solving the problems of the long vibration transmission path, complex frequency components and severe signal attenuation of the bearing vibration signal.

[0056] Figure 5 A flow chart of a bearing fault monitoring method according to an exemplary embodiment of the present disclosure is shown. Figure 5 As shown, the bearing fault monitoring method provided by the exemplary embodiment of the present invention may include:

[0057] Step 501: Acquire a bearing vibration signal collected by a sensing component during the operation of the bearing, where the sensing component is disposed in the bearing.

[0058] The manner in which the sensing component is arranged in the bearing can be referred to in the previous text, and will not be described in detail here. The bearing vibration signal collected by the sensing component can be transmitted to the external computing platform described above through wireless communication.

[0059] Step 502: Use empirical modes to perform noise reduction and reconstruction on the bearing vibration signal to obtain a noise reduced and reconstructed bearing vibration signal.

[0060] The above-mentioned bearing vibration signal is time-scale data. The exemplary embodiment of the present disclosure can use empirical modes to perform noise reduction and reconstruction on the bearing vibration signal, and the obtained noise-reduced and reconstructed bearing vibration signal is frequency-scale data. Therefore, the exemplary embodiment of the present disclosure can convert the bearing vibration signal from time-scale data to frequency-scale data, which is convenient for characteristic analysis of the bearing vibration signal. At the same time, empirical mode decomposition can extract characteristic signals from the noise of nonlinear and non-stationary processes. The signal noise of the bearing vibration signal obtained after noise reduction and reconstruction is relatively small, which can effectively reduce the impact of signal noise on the monitoring results.

[0061] Step 503: performing envelope spectrum analysis on the bearing vibration signal after noise reduction and reconstruction to obtain envelope spectrum analysis results.

[0062] The above-mentioned envelope spectrum is a demodulation method, which can be used to effectively extract some periodic impact signals excited by bearing faults from the high-frequency vibration signals inherent in the bearing operation, so as to analyze the type and degree of the bearing fault based on these periodic impact signals.

[0063] Step 504: If an amplitude mutation signal is obtained based on the envelope spectrum analysis result, the type of the bearing fault is determined based on the amplitude mutation signal.

[0064] It can be understood that the above-mentioned amplitude mutation signals can be some periodic impact signals excited by bearing faults. When the types of bearing faults are different, the characteristic parameters of these amplitude mutation signals are different, and different types of bearing faults correspond to different characteristic parameters. Therefore, the exemplary embodiment of the present disclosure can determine the type of bearing fault based on the characteristic parameters of these amplitude mutation signals. Here, the characteristic parameters can be characteristic frequency parameters.

[0065] It can be seen that the method of the exemplary embodiment of the present disclosure can obtain the bearing vibration signal collected from the induction component during the operation of the bearing, and the induction component is arranged in the bearing, which can collect the bearing vibration signal in the form of magnetoelectric induction, realize the direct conversion of electrical signal to electrical signal, shorten the signal transmission path, reduce signal attenuation, and has the characteristics of simple signal acquisition, good robustness and low cost. Then, the bearing vibration signal is reconstructed by using the empirical mode to obtain the bearing vibration signal after noise reduction and reconstruction; the bearing vibration signal after noise reduction and reconstruction is analyzed by envelope spectrum to obtain the envelope spectrum analysis result. At this time, if an amplitude mutation signal is obtained based on the envelope spectrum analysis result, the type of bearing fault is determined based on the amplitude mutation signal. Therefore, the method of the exemplary embodiment of the present disclosure can collect the bearing vibration signal by using the induction component arranged in the bearing, and efficiently identify the early weak fault signal of the bearing, determine the type of bearing fault, and improve the identification efficiency of the bearing fault.

[0066] In an optional manner, the exemplary embodiment of the present disclosure uses empirical modes to perform noise reduction and reconstruction on the bearing vibration signal to obtain the noise reduced and reconstructed bearing vibration signal, which may include:

[0067] The bearing vibration signal is decomposed by empirical mode to obtain M intrinsic mode components. The M intrinsic mode components are weighted and summed to obtain the bearing vibration signal after noise reduction and reconstruction.

[0068] It can be understood that the above empirical mode decomposition can decompose the fluctuations and trends of different scales in the bearing vibration signal step by step to form a series of data sequences with different characteristic scales. These data sequences can be called intrinsic mode components.

[0069] The above-mentioned empirical mode decomposition may include collective empirical mode decomposition. The exemplary embodiment of the present disclosure may add M white noise signals with different amplitudes to the bearing vibration signal respectively to obtain M eigenmode components.

[0070] Here, the probability distribution of each white noise signal obeys the standard normal distribution. The ensemble empirical mode decomposition adds the white noise signal to the decomposition process of the bearing vibration signal and performs ensemble averaging, which can be used to suppress the endpoint effect and modal aliasing phenomenon that occur in the decomposition process, effectively avoiding the scale mixing problem, so that the final decomposed components maintain physical uniqueness.

[0071] Exemplarily, it can be set that M white noise signals are added to the bearing vibration signal, and the amplitudes of the M added white noise signals are different, so as to form M intrinsic mode components. The M intrinsic mode components can form a set of intrinsic mode components, and the set can be expressed as:

[0072] {c 1，j (t), c 2，j (t), c 3，j (t)...c M，j (t)}, j = 1, 2, 3, ...

[0073] Among them, c 1，j (t) represents the jth intrinsic mode component obtained by decomposing the white noise signal for the first time, c 2，j (t) represents the jth intrinsic mode component obtained by decomposing the white noise signal twice, c 3，j (t) represents the jth intrinsic mode component obtained by decomposing the white noise signal three times, c M，j (t) represents the jth intrinsic mode component obtained by decomposing the white noise signal after adding it M times.

[0074] Taking the i-th addition of white noise signal as an example, a white noise signal n that obeys the standard normal distribution is i (t) is added to the bearing vibration signal x(t) to generate an additional noise signal x i (t), which is expressed as follows:

[0075] i = 1, 2, ... M; x i (t) = x(t) + n i (t)

[0076] Among them, n i (t) represents the ith additive white noise sequence, x i (t) represents the additional noise signal of the i-th trial.

[0077] Then, the resulting additional noise signal xi (t) Perform empirical mode decomposition to obtain the i-th eigenmode component x as shown in the following formula: i (t) Its expression is as follows:

[0078]

[0079] Among them, c i，j (t) is the jth intrinsic mode component obtained by decomposing the white noise signal after adding it for the i-th time, r i，j (t) is the residual function, which represents the average trend of the signal.

[0080] On this basis, we can use the principle that the statistical mean value of unrelated sequences is zero to perform ensemble average operation on the corresponding intrinsic mode components to obtain the final noise-reduced bearing vibration signal after ensemble empirical mode decomposition, that is:

[0081]

[0082] Among them, c j (t) is the j-th eigenmode component of the collective empirical mode decomposition, i = 1, 2, ...M, j = 1, 2, 3, ...

[0083] In an optional manner, in the exemplary embodiment of the present disclosure, the weight of each eigenmode component in the bearing vibration signal after noise reduction and reconstruction is obtained by weighted summation of the M eigenmode components. The calculation formula of the correlation coefficient between the eigenmode component and the bearing vibration signal is:

[0084]

[0085] Among them, Corr(X i , Y) represents the correlation coefficient between the i-th eigenmode component and the bearing vibration signal, X i represents the i-th eigenmode component, Y represents the bearing vibration signal, Cov(X i , Y) represents the covariance between the i-th eigenmode component and the bearing vibration signal, σ X represents the standard deviation of the M eigenmode components, σ Y represents the standard deviation of the bearing vibration signal, and i represents an integer greater than or equal to 1 and less than or equal to M.

[0086] The correlation coefficient can be used to describe the correlation between the intrinsic mode component and the bearing vibration signal. The closer the correlation coefficient is to 1, the stronger the correlation between the intrinsic mode component and the bearing vibration signal is.

[0087] In an optional manner, the amplitude mutation signal of the exemplary embodiment of the present disclosure may be a low-frequency impact signal contained in the envelope spectrum analysis result, and the frequency of the low-frequency impact signal is less than a preset frequency. It should be understood that when the frequency of the low-frequency impact signal is less than the preset frequency, it can be determined that the low-frequency impact signal may be a bearing fault signal, and the bearing may be faulty at this time. The preset frequency here can be determined based on the actual application scenario and is not specifically limited here.

[0088] Based on this, determining the type of bearing fault based on the amplitude mutation signal of the exemplary embodiment of the present disclosure may include: determining the type of bearing fault based on a mapping relationship between the characteristic frequency of the low-frequency impact signal and the characteristic frequency of the bearing fault.

[0089] The types of bearing faults mentioned above may include bearing outer ring faults, bearing inner ring faults, and bearing rolling element faults. Each type of bearing fault has a corresponding characteristic frequency. Therefore, the exemplary embodiment of the present disclosure may determine the type of bearing fault based on the mapping relationship between the characteristic frequency of the low-frequency impact signal and the characteristic frequency of the bearing fault. In other words, when the characteristic frequency of the low-frequency impact signal matches the characteristic frequency of a certain bearing fault, the bearing fault type corresponding to the low-frequency impact signal is the fault type of the bearing fault.

[0090] Exemplarily, the calculation formula of the characteristic frequency of the bearing outer ring fault can be:

[0091] f eccor =|f s ±kf0|

[0092] Among them, f eccor The characteristic frequency of the bearing outer ring fault, f s is the motor power supply frequency, k is a positive integer, and is the bearing outer ring fault frequency.

[0093] Exemplarily, the calculation formula of the characteristic frequency of the bearing inner race fault can be:

[0094] f eccir =|f s ±f r ±kf i |

[0095] Among them, f eccir The characteristic frequency of the bearing inner race fault, f s is the motor power supply frequency, k is a positive integer, f r is the rotation frequency of the rotor, f i is the bearing inner ring fault frequency.

[0096] Exemplarily, the calculation formula of the characteristic frequency of the bearing rolling element fault can be:

[0097] feccball =|f s ±f cage ±kf b |

[0098] Among them, f eccball Characteristic frequency of bearing rolling element failure, f s is the motor power supply frequency, k is a positive integer, f cage is the angular frequency of rotation of the bearing cage, f b is the bearing rolling element failure frequency.

[0099] It can be seen that the exemplary embodiment of the present disclosure can determine the type of bearing fault based on the low-frequency impact signal contained in the envelope spectrum analysis result. Therefore, the type of bearing fault can be quickly identified from the early weak fault signal of the bearing, and the location of the bearing fault can be located, thereby helping the staff to deal with the bearing fault in time and avoid the aggravation of the bearing fault.

[0100] In an optional manner, the method of the exemplary embodiment of the present disclosure may further include: determining the fault characteristic frequency multiple energy based on the amplitude mutation signal; and determining the degree of the bearing fault based on the proportion of the fault characteristic frequency multiple energy to the total energy of the envelope spectrum. It can be understood that the smaller the proportion of the fault characteristic frequency multiple energy to the total energy of the envelope spectrum, the lighter the degree of the bearing fault; the greater the proportion of the fault characteristic frequency multiple energy to the total energy of the envelope spectrum, the more serious the degree of the bearing fault.

[0101] Exemplarily, if the proportion of the fault characteristic frequency doublet energy to the total energy of the envelope spectrum is less than a preset proportion, it is determined that the bearing is operating normally; if the proportion of the fault characteristic frequency doublet energy to the total energy of the envelope spectrum is greater than or equal to the preset proportion, it is determined that the bearing is faulty.

[0102] The above preset proportion is the maximum proportion of the fault characteristic frequency-doubled energy to the total energy of the envelope spectrum that supports the normal operation of the bearing. Its value can be determined based on actual experience or a large number of simulation experiments, and is determined in combination with the actual application scenario. It is not limited here. If the proportion of the fault characteristic frequency-doubled energy to the total energy of the envelope spectrum is less than the preset proportion, it means that the bearing is operating normally, but there is a slight bearing fault. At this time, you can choose to take corresponding measures to delay the aggravation of the bearing fault; if the proportion of the fault characteristic frequency-doubled energy to the total energy of the envelope spectrum is greater than or equal to the preset proportion, it means that the bearing is faulty, and relevant technical personnel need to deal with it in a timely manner.

[0103] It can be seen that the exemplary embodiment of the present disclosure can determine the extent of the bearing fault based on the amplitude mutation signal contained in the envelope spectrum analysis result, so that relevant technical personnel can make corresponding treatment in time to avoid unnecessary impact caused by the aggravation of the bearing fault.

[0104] One or more technical solutions provided in the exemplary embodiment of the present disclosure can obtain the bearing vibration signal collected from the induction component during the operation of the bearing. The induction component is arranged in the bearing, which can collect the bearing vibration signal in the form of magnetoelectric induction, realize the direct conversion of electrical signal to electrical signal, shorten the signal transmission path, reduce signal attenuation, and has the characteristics of simple signal acquisition, good robustness and low cost. Then, the bearing vibration signal is reconstructed by using the empirical mode to obtain the bearing vibration signal after noise reduction and reconstruction; the bearing vibration signal after noise reduction and reconstruction is analyzed by envelope spectrum to obtain the envelope spectrum analysis result. At this time, if an amplitude mutation signal is obtained based on the envelope spectrum analysis result, the type of bearing fault is determined based on the amplitude mutation signal. It can be seen that the method of the exemplary embodiment of the present disclosure can collect the bearing vibration signal by using the induction component arranged in the bearing, and efficiently identify the early weak fault signal of the bearing, determine the type of bearing fault, and improve the identification efficiency of the bearing fault.

[0105] The above mainly introduces the scheme provided by the embodiment of the present disclosure. It is understandable that in order to realize the above functions, the electronic device includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiment disclosed in this article, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0106] The disclosed embodiment can divide the electronic device into functional units according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the disclosed embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0107] In the case of dividing each functional module according to each function, an exemplary embodiment of the present disclosure provides a bearing fault monitoring device, which may be an electronic device or a chip applied to an electronic device. Figure 6 FIG. 1 is a schematic block diagram of a module of a bearing fault monitoring device according to an exemplary embodiment of the present disclosure. Figure 6 As shown, the device 600 includes:

[0108] An acquisition module 601 is used to acquire a bearing vibration signal collected by a sensing component during the operation of the bearing, wherein the sensing component is disposed in the bearing;

[0109] The acquisition module 601 is also used to perform noise reduction and reconstruction on the bearing vibration signal using the empirical mode to obtain the bearing vibration signal after noise reduction and reconstruction;

[0110] The acquisition module 601 is also used to perform envelope spectrum analysis on the bearing vibration signal after noise reduction and reconstruction to obtain envelope spectrum analysis results;

[0111] The determination module 602 is used to determine the type of bearing fault based on the amplitude mutation signal if an amplitude mutation signal is obtained based on the envelope spectrum analysis result.

[0112] As a possible implementation method, the acquisition module 601 is also used to decompose the bearing vibration signal using empirical modes to obtain M intrinsic mode components; and perform weighted summation on the M intrinsic mode components to obtain the bearing vibration signal after noise reduction and reconstruction.

[0113] As a possible implementation, the acquisition module 601 is further used to add M white noise signals with different amplitudes to the bearing vibration signal to obtain M intrinsic mode components, and the probability distribution of each white noise signal obeys the standard normal distribution.

[0114] As a possible implementation manner, the weight of each eigenmode component is determined based on a correlation coefficient between the eigenmode component and the bearing vibration signal.

[0115] As a possible implementation method, the amplitude mutation signal is a low-frequency impact signal contained in the envelope spectrum analysis result, and the frequency of the low-frequency impact signal is less than the preset frequency. The determination module 602 is also used to determine the type of bearing fault based on the mapping relationship between the characteristic frequency of the low-frequency impact signal and the characteristic frequency of the bearing fault.

[0116] As a possible implementation, the determination module 602 is further configured to determine the fault characteristic frequency double energy based on the amplitude mutation signal; and determine the extent of the bearing fault based on the proportion of the fault characteristic frequency double energy to the total energy of the envelope spectrum.

[0117] As a possible implementation manner, the determination module 602 is further configured to determine a bearing fault if the proportion of the fault characteristic frequency harmonic energy to the total energy of the envelope spectrum is greater than or equal to a preset proportion.

[0118] Figure 7 Schematic block diagram of a chip of an exemplary embodiment of the present disclosure is shown. Figure 7As shown, the chip 700 includes one or more (including two) processors 701 and a communication interface 702. The communication interface 702 can support the server to execute the data sending and receiving steps in the above method, and the processor 701 can support the server to execute the data processing steps in the above method.

[0119] Optional, such as Figure 7 As shown, the chip 700 also includes a memory 703, which may include a read-only memory and a random access memory, and provides operation instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory (NVRAM).

[0120] In some embodiments, Figure 7 As shown, the processor 701 performs corresponding operations by calling the operation instructions stored in the memory (the operation instructions may be stored in the operating system). The processor 701 controls the processing operations of any one of the terminal devices, and the processor may also be called a central processing unit (CPU). The memory 703 may include a read-only memory and a random access memory, and provides instructions and data to the processor 701. A portion of the memory 703 may also include NVRAM. For example, in an application, the memory, the communication interface, and the memory are coupled together through a bus system, wherein the bus system may include a power bus, a control bus, and a status signal bus in addition to a data bus. However, for the sake of clarity, in Figure 7 Various buses are labeled as bus system 704 .

[0121] The method disclosed in the above-mentioned embodiment of the present disclosure can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above-mentioned processor may be a general-purpose processor, a digital signal processor (digital signal processing, DSP), an ASIC, a field-programmable gate array (field-programmable gate array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiment of the present disclosure can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware.

[0122] The exemplary embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program that can be executed by the at least one processor, and the computer program is used to cause the electronic device to perform the method according to the embodiment of the present disclosure when executed by the at least one processor.

[0123] The exemplary embodiments of the present disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform the method according to the embodiments of the present disclosure.

[0124] The exemplary embodiments of the present disclosure further provide a computer program product, including a computer program, wherein when the computer program is executed by a processor of a computer, the computer is used to enable the computer to perform the method according to the embodiments of the present disclosure.

[0125] refer to Figure 8, a block diagram of an electronic device 800 that can be used as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0126] like Figure 8 As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0127] A plurality of components in the electronic device 800 are connected to the I / O interface 805, including: an input unit 806, an output unit 807, a storage unit 808, and a communication unit 809. The input unit 806 may be any type of device capable of inputting information to the electronic device 800, and the input unit 806 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 807 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 808 may include, but is not limited to, a disk, an optical disk. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0128] like Figure 8As shown, the computing unit 801 can be various general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above. For example, in some embodiments, the method of the exemplary embodiments of the present disclosure may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 800 via ROM 802 and / or communication unit 809. In some embodiments, the computing unit 801 can be configured to execute the method in any other appropriate manner (e.g., by means of firmware).

[0129] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0130] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0131] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0132] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0133] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0134] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0135] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instruction is loaded and executed on a computer, the process or function described in the embodiment of the present disclosure is executed in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user device or other programmable device. The computer program or instruction may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program or instruction may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server, data center, etc. that integrates one or more available media. The available medium may be a magnetic medium, for example, a floppy disk, a hard disk, a tape; it may also be an optical medium, for example, a digital video disc (DVD); it may also be a semiconductor medium, for example, a solid state drive (SSD).

[0136] Although the present disclosure has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present disclosure. Accordingly, this specification and the drawings are merely exemplary illustrations of the present disclosure as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present disclosure. Obviously, those skilled in the art may make various modifications and variations to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is also intended to include these modifications and variations.

Claims

1. A bearing fault monitoring system, characterized in that: The invention comprises a monitoring device for bearing fault, a bearing and at least one sensing component, each of the sensing components is wirelessly connected with the device, the bearing comprises a bearing outer ring, a bearing inner ring, balls and a retaining frame for fixing the balls, the retaining frame is located between the bearing outer ring and the bearing inner ring, and each of the sensing components is arranged on the bearing outer ring; Each of the sensing components has a magnetic sensor, a vibration acceleration sensor, a controller, a signal circuit and a wireless communicator. The controller is electrically connected to the magnetic sensor, the vibration acceleration sensor and the wireless communicator through the signal circuit. The magnetic sensor is arranged in the oil filling hole, and the dynamic acceleration sensor is arranged on the surface of the magnetic sensor away from the oil filling hole. The magnetic sensor comprises a magnetic probe and a collector, wherein the magnetic probe is located in the oil filling hole, the collector is arranged on the magnetic probe, the collector is electrically connected to the magnetic probe, and the collector is electrically connected to the controller through the signal circuit; The vibration acceleration sensor comprises an elastic member, a mass block, a piezoelectric element, a base and a connecting member, wherein one end of the mass block away from the base is connected to the elastic member, and the piezoelectric element is located between the base and the mass block; The connecting member sequentially connects the elastic member, the mass block, the piezoelectric element, and the base, one end of the connecting member away from the base is connected to the magnetic probe, and the piezoelectric element is electrically connected to the controller; The sensing component also has a cavity for accommodating the controller and the wireless communicator, and the cavity is arranged on the base of the vibration acceleration sensor.

2. The system according to claim 1, characterized in that The bearing fault monitoring device comprises: An acquisition module, used to acquire a bearing vibration signal collected from a sensing component during the operation of the bearing, wherein the sensing component is disposed in the bearing; The acquisition module is also used to perform noise reduction and reconstruction on the bearing vibration signal using empirical modes to obtain the noise reduced and reconstructed bearing vibration signal; The acquisition module is also used to perform envelope spectrum analysis on the bearing vibration signal after noise reduction and reconstruction to obtain envelope spectrum analysis results; A determination module is used to determine the type of bearing fault based on the amplitude mutation signal if an amplitude mutation signal is obtained based on the envelope spectrum analysis result.

3. A bearing fault monitoring method, applied to the bearing fault monitoring system according to claim 1 or 2, characterized in that: The method comprises: Acquiring a bearing vibration signal collected by a sensing component during the operation of the bearing, wherein the sensing component is disposed in the bearing; Using empirical modes to perform noise reduction and reconstruction on the bearing vibration signal to obtain a noise-reduced and reconstructed bearing vibration signal; Performing envelope spectrum analysis on the bearing vibration signal after noise reduction and reconstruction to obtain envelope spectrum analysis results; If an amplitude mutation signal is obtained based on the envelope spectrum analysis result, the type of the bearing fault is determined based on the amplitude mutation signal.

4. The method according to claim 3, characterized in that The step of performing noise reduction and reconstruction on the bearing vibration signal by using the empirical mode to obtain the noise reduced and reconstructed bearing vibration signal includes: Decomposing the bearing vibration signal by empirical mode to obtain M eigenmode components; The M eigenmode components are weighted and summed to obtain the bearing vibration signal after noise reduction and reconstruction.

5. The method according to claim 4, characterized in that The method of using empirical mode decomposition to decompose the bearing vibration signal to obtain M intrinsic mode components includes: M white noise signals with different amplitudes are added to the bearing vibration signal respectively to obtain M intrinsic mode components, and the probability distribution of each of the white noise signals obeys the standard normal distribution.

6. The method according to claim 4, characterized in that The weight of each of the eigenmode components is determined based on a correlation coefficient between the eigenmode component and the bearing vibration signal.

7. The method according to any one of claims 3 to 6, characterized in that The amplitude mutation signal is a low-frequency impact signal contained in the envelope spectrum analysis result, the frequency of the low-frequency impact signal is less than a preset frequency, and the type of bearing fault is determined based on the amplitude mutation signal, including: The type of the bearing fault is determined based on the mapping relationship between the characteristic frequency of the low-frequency impact signal and the characteristic frequency of the bearing fault.

8. The method according to any one of claims 3 to 6, characterized in that The method further comprises: Determine the fault characteristic frequency-multiplied energy based on the amplitude mutation signal; The degree of the bearing fault is determined based on the proportion of the fault characteristic frequency-doubled energy to the total energy of the envelope spectrum.

9. The method according to claim 8, characterized in that The method further comprises: If the proportion of the fault characteristic frequency-doubled energy to the total energy of the envelope spectrum is greater than or equal to a preset proportion, a bearing fault is determined.

10. An electronic device, characterized in that: include: processor; as well as, A memory for storing programs; The program includes instructions, which, when executed by the processor, cause the processor to execute the method according to any one of claims 3 to 9.

11. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 3 to 9.

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