CN110987427A - Data processing method, device and system for mechanical equipment - Google Patents
Data processing method, device and system for mechanical equipment Download PDFInfo
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- CN110987427A CN110987427A CN201911420153.0A CN201911420153A CN110987427A CN 110987427 A CN110987427 A CN 110987427A CN 201911420153 A CN201911420153 A CN 201911420153A CN 110987427 A CN110987427 A CN 110987427A
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Abstract
The embodiment of the invention discloses a data processing method for mechanical equipment, which comprises the following steps: acquiring a vibration signal and a rotating speed signal of mechanical equipment; obtaining a first order spectrum of the vibration signal based on the vibration signal and the rotating speed signal; searching spectral lines corresponding to input shaft rotating frequency sidebands in the first-order spectrum; for the searched spectral lines, filtering out the spectral lines of which the amplitude values are smaller than a first threshold; and calculating an abnormal index of the input shaft based on the filtered spectral line of the input shaft frequency conversion sideband, wherein the abnormal index is used for judging whether the input shaft is abnormal and/or the abnormal severity. The embodiment of the invention also discloses a corresponding data processing device, a corresponding system and a corresponding computing device.
Description
Technical Field
The invention relates to the field of state monitoring of mechanical equipment, in particular to a data processing method, device and system for the mechanical equipment.
Background
Order analysis is an important means of condition monitoring of rotating machinery, particularly rotating machinery such as gearboxes. Taking a gear box as an example, a shaft, a gear and a bearing in the gear box can generate vibration during operation, and if a fault occurs, a modulation phenomenon of a vibration signal can be generated, and a frequency spectrum of the vibration signal can show that a frequency conversion sideband related to the fault occurs on two sides of a meshing frequency or a natural frequency. The fundamental frequency and each harmonic component thereof can be clearly distinguished through order analysis, which is beneficial to extracting the characteristics of the frequency conversion sideband related to the fault, thereby effectively finding the abnormity, positioning the abnormal component and identifying the abnormal severity.
At present, parameters of a gearbox, such as the rotating speed frequency (abbreviated as "frequency conversion") of an input shaft, the number of gear teeth and the like, are generally acquired in advance, then the meshing frequency is calculated according to the acquired parameters, and finally, a frequency conversion sideband related to abnormality around the meshing frequency is identified and extracted from the frequency spectrum of a vibration signal.
However, this conventional approach has been difficult to adapt to practical industrial scenarios. In the actual industrial scene, various operations such as regular or irregular overhaul maintenance, part replacement, gear replacement and the like exist on the site, and in addition, the huge number of equipment is added, so that the detailed gear box parameter information is very difficult to obtain and maintain, and the gear box parameter information cannot be kept to be consistent with the actual situation on the site. The wrong parameter information can directly lead to the traditional method to obtain wrong results, and then wrong conclusions are generated.
In addition, the traditional method has the problem of instability under complex working conditions. For unsteady state/variable working condition/frequency conversion equipment, the rotating speed variation range is wide and the frequency is high during the operation, and no matter the traditional method uses the average rotating frequency, the highest rotating frequency or the lowest rotating frequency, the correspondingly calculated meshing frequency and the like have deviation, so that certain errors and instability are brought to the identification and extraction of the rotating frequency sideband.
Accordingly, it is desirable to provide a more advanced data processing scheme for mechanical devices that can more accurately monitor device anomalies without the associated parameters.
Disclosure of Invention
To this end, embodiments of the present invention provide a data processing method, apparatus and system for a mechanical device, in an effort to solve or at least alleviate the above-mentioned problems.
According to an aspect of an embodiment of the present invention, there is provided a data processing method for a mechanical device, including: acquiring a vibration signal and a rotating speed signal of mechanical equipment; obtaining a first order spectrum of the vibration signal based on the vibration signal and the rotating speed signal; searching spectral lines corresponding to input shaft rotating frequency sidebands in the first-order spectrum; for the searched spectral lines, filtering out the spectral lines of which the amplitude values are smaller than a first threshold; and calculating an abnormal index of the input shaft based on the filtered spectral line of the input shaft frequency conversion sideband, wherein the abnormal index is used for judging whether the input shaft is abnormal and/or the abnormal severity.
Optionally, in the data processing method according to the embodiment of the present invention, the step of obtaining the first order spectrum of the vibration signal includes: based on the rotating speed signal, carrying out equal-angle resampling on the vibration signal to obtain a resampling signal; and carrying out Fourier transform on the resampled signal to obtain a first-order spectrum.
Optionally, in the data processing method according to the embodiment of the present invention, the step of filtering out spectral lines whose amplitudes are smaller than the first threshold includes: acquiring the outer contour of the first order spectrum; determining a first threshold based on the outer contour and the maximum amplitude of the first order spectrum; and setting the amplitude of the spectral line with the amplitude smaller than the first threshold in the searched spectral lines to be zero.
Optionally, in the data processing method according to the embodiment of the present invention, the step of acquiring the outer contour of the first order spectrum includes: filtering the first order spectrum; setting a window at the starting point of the filtered first order spectrum; determining the maximum amplitude of the spectral line contained in the window; replacing the amplitude of each spectral line contained in the window with the maximum amplitude of the window; moving the window by a predetermined step; and repeating the steps of determining the maximum amplitude of the window, replacing the amplitude of each spectral line contained in the window and moving the window until the window reaches the end point of the filtered first-order spectrum, wherein each spectral line subjected to amplitude replacement forms the outer contour of the first-order spectrum.
Optionally, in the data processing method according to the embodiment of the present invention, the step of filtering the first order spectrum includes: the first order spectrum is subjected to a hanpul filter.
Optionally, in the data processing method according to the embodiment of the present invention, the step of determining the first threshold based on the outer contour and the maximum amplitude of the first order spectrum includes: multiplying the maximum amplitude of the first order spectrum by a preset proportion to obtain a first reference threshold; and comparing the first reference threshold with the maximum amplitude value of the outer contour of the first order spectrum, and selecting the larger value as the first threshold.
Optionally, in the data processing method according to the embodiment of the present invention, the method further includes: after the spectral line corresponding to the input shaft frequency conversion sideband in the first-order spectrum is searched, the spectral line of the input shaft frequency conversion sideband is removed from the first-order spectrum to obtain a second-order spectrum; removing noise from the second order spectrum to obtain a de-noised order spectrum; dividing spectral lines in the denoising order spectrum into multiple clusters of spectral line combinations; for each cluster of spectral line combination, combining corresponding spectral lines of the cluster of spectral line combination in the second-order spectrum, and taking the maximum amplitude of the cluster of spectral line combination as the amplitude to obtain a combined order spectrum; acquiring the order difference value of every two adjacent spectral lines in the combined order spectrum; determining order difference values which meet a predetermined condition and have the most frequency of occurrence among the obtained order difference values; searching spectral lines corresponding to other shaft rotational frequency sidebands in the combined order spectrum based on the determined order difference value; and calculating the abnormal indexes of the other axes based on the spectral lines corresponding to the rotating frequency sidebands of the other axes, wherein the abnormal indexes are used for judging whether the other axes are abnormal and/or the abnormal severity.
Optionally, in the data processing method according to the embodiment of the present invention, the step of removing noise from the second-order spectrum includes: determining the outer contour of the second order spectrum; carrying out linear transformation on the outer contour of the second order spectrum to obtain a noise curve; and removing noise from the second order spectrum based on the noise curve.
Optionally, in the data processing method according to the embodiment of the present invention, the step of determining the outer contour of the second order spectrum includes: filtering the second order spectrum; setting a window at the starting point of the filtered second order spectrum; determining the maximum amplitude of the spectral line contained in the window; replacing the amplitude of each spectral line contained in the window with the maximum amplitude of the window; moving the window by a predetermined step; and repeating the steps of determining the maximum amplitude of the window, replacing the amplitude of each spectral line contained in the window and moving the window until the window reaches the end point of the filtered second-order spectrum, wherein each spectral line subjected to amplitude replacement forms the outer contour of the second-order spectrum.
Optionally, in the data processing method according to the embodiment of the present invention, the step of filtering the second-order spectrum includes: and performing Hampler filtering on the second-order spectrum.
Optionally, in the data processing method according to the embodiment of the present invention, the step of removing noise from the second order spectrum based on the noise curve includes: for each line of the second order spectrum, the amplitude of the line is subtracted from the corresponding amplitude of the line in the noise curve.
Optionally, in the data processing method according to the embodiment of the present invention, the step of dividing spectral lines in the denoised order spectrum into multiple clusters of spectral line combinations includes: and dividing adjacent spectral lines meeting the combination condition into a cluster of spectral line combinations, wherein the combination condition comprises that the difference of orders is not greater than an order threshold value, and the difference of amplitudes is not greater than an amplitude threshold value.
Optionally, in the data processing method according to an embodiment of the present invention, the step of satisfying the predetermined condition includes: the order difference value lies within a predetermined range and there are at least a predetermined number of consecutive spectral lines having the order difference value.
Optionally, in the data processing method according to the embodiment of the present invention, the method further includes: selecting spectral lines in a preset order range from the second order spectrum as a third order spectrum; removing noise from the third order spectrum to obtain a low-frequency denoising order spectrum; dividing spectral lines in the low-frequency denoising order spectrum into a plurality of clusters of spectral line combinations; for each cluster of spectral line combination, merging corresponding spectral lines of the cluster of spectral line combination in the third-order spectrum, and taking the maximum amplitude of the cluster of spectral line combination as the amplitude to obtain a low-frequency merged-order spectrum; acquiring the order difference value of every two adjacent spectral lines in the low-frequency combined order spectrum; determining order difference values which meet a predetermined condition and have the most frequency of occurrence among the obtained order difference values; searching spectral lines corresponding to the low-speed shaft rotating frequency sideband in the low-frequency merging order spectrum based on the determined order difference value; and calculating an abnormal index of the low-speed shaft based on the spectral lines corresponding to the rotating frequency sidebands of the low-speed shaft, wherein the abnormal index is used for judging whether the low-speed shaft is abnormal and/or the abnormal severity.
Optionally, in the data processing method according to an embodiment of the present invention, the mechanical device is a gear box.
According to another aspect of the embodiments of the present invention, there is provided a data processing apparatus including: the signal receiving module is suitable for acquiring a vibration signal and a rotating speed signal of mechanical equipment; the order acquisition module is suitable for obtaining a first order spectrum of the vibration signal based on the vibration signal and the rotating speed signal; the sideband searching module is suitable for searching spectral lines corresponding to the input shaft rotating frequency sideband in the first-order spectrum; the method is also suitable for filtering out spectral lines with amplitude values smaller than a first threshold for the searched spectral lines; and the index calculation module is suitable for calculating an abnormal index of the input shaft based on the filtered spectral line of the input shaft frequency conversion sideband, and the abnormal index is used for judging whether the input shaft is abnormal and/or the abnormal severity.
According to another aspect of the embodiments of the present invention, there is provided an abnormality monitoring system for monitoring whether an abnormality occurs in a mechanical device, the system including: a mechanical device; a rotation speed sensor and a vibration sensor arranged on the mechanical equipment; and a data processing apparatus according to an embodiment of the present invention.
According to another aspect of an embodiment of the present invention, there is provided a computing device including: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing the above-described data processing method according to an embodiment of the present invention.
According to a further aspect of embodiments of the present invention, there is provided a computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform the above-described data processing method according to embodiments of the present invention.
According to the data processing scheme provided by the embodiment of the invention, the frequency conversion sideband indexes (such as abnormal indexes of all shafts) of the equipment under all working conditions (such as an input shaft, an intermediate shaft and a low-speed shaft) can be accurately obtained on the basis of the vibration signal and the rotating speed signal under the condition of no equipment parameter, and then the equipment abnormality and abnormal part positioning are identified.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and the embodiments of the present invention can be implemented according to the content of the description in order to make the technical means of the embodiments of the present invention more clearly understood, and the detailed description of the embodiments of the present invention is provided below in order to make the foregoing and other objects, features, and advantages of the embodiments of the present invention more clearly understandable.
Drawings
To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings, which are indicative of various ways in which the principles disclosed herein may be practiced, and all aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description read in conjunction with the accompanying drawings. Throughout this disclosure, like reference numerals generally refer to like parts or elements.
FIG. 1 shows a schematic diagram of an anomaly monitoring system 100 according to one embodiment of the present invention;
FIG. 2 shows a schematic diagram of a computing device 200, according to one embodiment of the invention;
FIG. 3 shows a schematic diagram of a data processing method 300 according to one embodiment of the invention;
FIG. 4 shows a schematic diagram of a vibration signal according to one embodiment of the present invention;
FIG. 5 shows a schematic of a first order spectrum according to one embodiment of the invention;
FIG. 6 shows a schematic diagram of a noise curve according to an embodiment of the invention;
FIG. 7 shows a schematic diagram of a merged order spectrum according to one embodiment of the invention; and
fig. 8 shows a schematic diagram of a data processing device 800 according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
FIG. 1 shows a schematic diagram of an anomaly monitoring system 100 according to one embodiment of the present invention. As shown in fig. 1, the abnormality monitoring system 100 includes a mechanical device 110, a vibration sensor 120, a rotational speed sensor 130, and a data processing apparatus 800. In other embodiments, the anomaly monitoring system 100 may include different and/or additional modules.
The mechanical device 110 is typically a rotating mechanical device such as a gearbox. The vibration sensor 120 and the rotation speed sensor 130 are disposed on the mechanical device 110, and can acquire a vibration signal and a rotation speed signal of the mechanical device 110, respectively. Any vibration sensor and rotation speed sensor may be used to collect the vibration signal and the rotation speed signal, which is not limited in the present invention.
The data processing device 800 may be coupled to the vibration sensor 120 and the rotation speed sensor 130, receive the vibration signal and the rotation speed signal, and perform order analysis based on the vibration signal and the rotation speed signal to obtain an abnormal index of each axis of the equipment. By monitoring these anomaly indicators, it can be determined whether the corresponding axis is anomalous and/or the severity of the anomaly.
In some embodiments, the data processing device 800 and the vibration sensor 120 and the rotational speed sensor 130 may be coupled through the network 140. Network 140 may include wired and/or wireless communication paths such as, but not limited to, the internet, a local area network, a satellite path, a fiber optic path, a cable path, or any other suitable wired or wireless communication path or combination of such paths.
According to an embodiment of the present invention, each component (particularly, the data processing apparatus 800) in the above-described abnormality monitoring system 100 may be implemented by the computing device 200 as described below.
FIG. 2 shows a schematic diagram of a computing device 200, according to one embodiment of the invention. As shown in FIG. 2, in a basic configuration 202, a computing device 200 typically includes a system memory 206 and one or more processors 204. A memory bus 208 may be used for communication between the processor 204 and the system memory 206.
Depending on the desired configuration, the processor 204 may be any type of processor, including but not limited to: a microprocessor (μ P), a microcontroller (μ C), a Digital Signal Processor (DSP), or any combination thereof. The processor 204 may include one or more levels of cache, such as a level one cache 210 and a level two cache 212, a processor core 214, and registers 216. Example processor cores 214 may include Arithmetic Logic Units (ALUs), Floating Point Units (FPUs), digital signal processing cores (DSP cores), or any combination thereof. The example memory controller 218 may be used with the processor 204, or in some implementations the memory controller 218 may be an internal part of the processor 204.
Depending on the desired configuration, system memory 206 may be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 206 may include an operating system 220, one or more applications 222, and program data 224. In some implementations, the application 222 can be arranged to execute instructions on the operating system with the program data 224 by the one or more processors 204.
Computing device 200 may also include an interface bus 240 that facilitates communication from various interface devices (e.g., output devices 242, peripheral interfaces 244, and communication devices 246) to the basic configuration 202 via the bus/interface controller 230. The example output device 242 includes a graphics processing unit 248 and an audio processing unit 250. They may be configured to facilitate communication with various external devices, such as a display or speakers, via one or more a/V ports 252. Example peripheral interfaces 244 can include a serial interface controller 254 and a parallel interface controller 256, which can be configured to facilitate communications with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 258. An example communication device 246 may include a network controller 260, which may be arranged to facilitate communications with one or more other computing devices 262 over a network communication link via one or more communication ports 264.
A network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media, such as carrier waves or other transport mechanisms, in a modulated data signal. A "modulated data signal" may be a signal that has one or more of its data set or its changes made in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or private-wired network, and various wireless media such as acoustic, Radio Frequency (RF), microwave, Infrared (IR), or other wireless media. The term computer readable media as used herein may include both storage media and communication media.
Computing device 200 may be implemented as a server, such as a database server, an application server, a WEB server, and the like, or as a personal computer including desktop and notebook computer configurations. Of course, computing device 200 may also be implemented as at least a portion of a small-sized portable (or mobile) electronic device.
In an embodiment according to the invention, the computing device 200 may be implemented as a data processing apparatus 800 and configured to perform the data processing method 300 according to an embodiment of the invention. The application 222 of the computing device 200 includes a plurality of instructions for executing the data processing method 300 according to the embodiment of the present invention, and the program data 224 may further store configuration data of the anomaly monitoring system 100.
FIG. 3 shows a flow diagram of a data processing method 300 according to one embodiment of the invention. As shown in FIG. 3, the data processing method 300 is for the mechanical device 110 and begins at step S310.
In step S310, a vibration signal and a rotational speed signal of the mechanical device 110 are acquired. For example, the vibration signal may be acquired via the vibration sensor 120, and the rotation speed signal may be acquired via the rotation speed sensor 130. Fig. 4 shows a schematic diagram of a vibration signal according to an embodiment of the invention.
Then, in step S320, a first order spectrum of the vibration signal is obtained based on the vibration signal and the rotation speed signal. In some embodiments, the vibration signal may be resampled at equal angles based on the rotation speed signal to obtain a resampled signal. Then, Fourier transform is carried out on the resampled signal to obtain a first order spectrum. In the embodiment of the present invention, any order analysis method may be adopted to obtain the order spectrum, which is not limited in the present invention.
Then, in step S330, spectral lines corresponding to the input shaft rotational frequency sidebands in the first order spectrum may be searched. It can be understood that since the meshing frequency is an integer multiple of the input shaft rotation frequency, the meshing frequency and the rotation frequency sideband of the input shaft are all at integer positions of the first order spectrum, and the spectral lines of the rotation frequency sideband of the input shaft can be searched for by the spectral lines with integer orders.
Then, in step S340, for the found spectral lines, spectral lines whose amplitudes are smaller than the first threshold may be filtered out. FIG. 5 illustrates a schematic diagram of a first order spectrum of the vibration signal illustrated in FIG. 3, according to one embodiment of the present invention. As shown in fig. 5, the searched spectral lines (spectral lines with integer orders) of the input shaft frequency conversion sidebands have different amplitudes, and the low-amplitude spectral lines affect the judgment of the number of the sidebands and the abnormal severity. In order to be able to further accurately identify the input shaft rotational frequency sideband, the spectral lines with low amplitude need to be removed.
In some embodiments, the outer contour of the first order spectrum may be acquired. Specifically, the first-order spectrum may be filtered first, for example, the first-order spectrum may be filtered by multiple filtering methods such as hampel (hanpul) filtering, moving average filtering, peak envelope filtering, weighted moving average filtering, Savitzky-gold filtering, and median filtering, which is not limited in this embodiment of the present invention.
Then, the filtered first-order spectrum may be subjected to moving maximum processing to obtain an outer contour of the first-order spectrum. The procedure for moving the maximum may be as follows: a window is provided at the start of the filtered first order spectrum (i.e. the first spectral line of the filtered first order spectrum), the window width being for example 20 spectral lines wide. The maximum amplitude of the spectral lines contained by the window may be determined and replaced with the maximum amplitude of the window. The window may then be shifted by a predetermined step (e.g., by one spectral line), and the steps of determining the maximum amplitude of the window, replacing the amplitudes of spectral lines included in the window, and shifting the window may be repeated for the shifted window until the window reaches the end of the filtered first order spectrum (i.e., the last spectral line). Thus, the spectral lines after the amplitude replacement process form the outer contour of the first order spectrum.
After determining the outer contour of the first order spectrum, a first threshold may be determined based on the outer contour of the first order spectrum and the maximum amplitude of the first order spectrum. The maximum amplitude of the first order spectrum is the maximum amplitude of all the spectral lines of the first order spectrum.
For example, the maximum amplitude of the first order spectrum is multiplied by a predetermined ratio to obtain a first reference threshold. And comparing the first reference threshold with the maximum amplitude value of the outer contour of the first-order spectrum, and selecting the larger value as the first threshold. The predetermined ratio may be configured based on a difference between the maximum amplitude of the first-order spectrum and the outer contour of the outer contour, for example, the predetermined ratio may be configured to be 5%.
After the first threshold is determined, in the first-order spectrum, the amplitudes of the searched spectral lines with the amplitudes smaller than the first threshold in the spectral lines of the input shaft frequency conversion sidebands can be set to be zero.
As shown in fig. 5, the first threshold is determined to be 0.04. In the first-order spectrum, after spectral lines with amplitude values smaller than a first threshold in the searched spectral lines of the input shaft frequency conversion side bands are filtered, 14 spectral lines are left as the final spectral lines of the input shaft frequency conversion side bands.
Then, in step S350, an anomaly indicator of the input shaft may be calculated based on the filtered spectral lines of the input shaft frequency conversion sidebands. The abnormality indicator includes, but is not limited to, one of: the energy, number, average amplitude, etc. of the transfrequency sidebands. These abnormality indicators can be used to determine whether an abnormality has occurred in the input shaft and the severity of the abnormality.
Considering that other axes except the input axis are abnormal, according to an embodiment of the present invention, after the spectral lines corresponding to the input axis rotating frequency sidebands in the first order spectrum are searched, the searched spectral lines of the input axis rotating frequency sidebands can be removed from the first order spectrum, and a second order spectrum is obtained. For example, the amplitude of the spectral line of the found input shaft rotational frequency sideband is set to zero.
Then, noise can be removed from the second order spectrum to obtain a de-noised order spectrum. In particular, the outer contour of the second order spectrum may be determined first. Similar to the above-described determination of the outer contour of the first-order spectrum, the second-order spectrum is filtered (for example, multiple filtering methods such as hampel filtering, moving average filtering, peak envelope filtering, weighted moving average filtering, Savitzky-gold filtering, median filtering, etc.), and then the filtered second-order spectrum is processed by moving the maximum value, so as to obtain the outer contour of the second-order spectrum. The procedure for moving the maximum may be as follows: a window is provided at the start of the filtered second order spectrum (i.e. the first spectral line of the filtered second order spectrum), the window width being for example 20 spectral lines wide. The maximum amplitude of the spectral lines contained by the window may be determined and replaced with the maximum amplitude of the window. The window may then be shifted by a predetermined step (e.g., by one spectral line), and the steps of determining the maximum amplitude of the window, replacing the amplitudes of spectral lines included in the window, and shifting the window may be repeated for the shifted window until the window reaches the end of the filtered second order spectrum (i.e., the last spectral line). Thus, each spectral line after the amplitude replacement processing forms the outer contour of the second order spectrum.
After the outer contour of the second order spectrum is determined, the outer contour of the second order spectrum can be subjected to linear transformation to obtain a noise curve. For example, the outer contour of the second order spectrum may be linearly transformed according to the following formula:
and y is the amplitude of the x-order spectral line on the noise curve, and y is the amplitude of the x-order spectral line on the outer contour of the second-order spectrum. a and b are transformation parameters, which can be determined based on actual conditions.
FIG. 6 shows a schematic diagram of a noise curve according to one embodiment of the invention. The noise curve as shown in fig. 6 is transformed by the following equation: y 2+ 0.005. And y is the amplitude of the x-order spectral line on the noise curve, and y is the amplitude of the x-order spectral line on the outer contour of the second-order spectrum.
After the noise curve is obtained, noise can be removed from the second order spectrum based on the noise curve, and a denoising order spectrum is obtained. For example, for each spectral line of the second order spectrum, the amplitude of the spectral line is subtracted by the corresponding amplitude of the spectral line in the noise curve.
The spectral lines in the denoised order spectrum may then be divided into multiple clusters of spectral line combinations. For example, adjacent spectral lines that satisfy the combination condition may be divided into a cluster of spectral line combinations. Wherein, the combination condition may be that the difference of the orders is not greater than the order threshold (the order threshold may be 0.001, for example), and the difference of the amplitudes is not greater than the amplitude threshold (the amplitude threshold may be 0.004, for example).
For each cluster of spectral line combination obtained by division, corresponding spectral lines of the cluster of spectral line combination in the second-order spectrum can be determined, the determined spectral lines are combined, and the maximum amplitude of the cluster of spectral line combination is taken as the amplitude. This results in a combined order spectrum. FIG. 7 shows a schematic diagram of a merged order spectrum according to one embodiment of the invention.
Next, order difference values of every two adjacent spectral lines in the combined order spectrum may be acquired, and the order difference value that satisfies a predetermined condition and has the highest frequency of occurrence is determined among all the acquired order difference values. Wherein the order difference value satisfying the predetermined condition means that the order difference value is within a predetermined range and there are at least a predetermined number of consecutive spectral lines having the order difference value. The predetermined number may typically be 3, the range being determined based on the reduction ratio of the mechanical device. For example, the reduction ratio of the gear box is 0.1-0.5, and the predetermined range can be 0.1-0.5.
Taking the merged order spectrum shown in fig. 7 as an example, the order difference value of all the order difference values obtained by every two adjacent spectral lines is 0.1-0.5, and there are 3 consecutive spectral lines having the order difference value, which is 0.18. That is, the spectral lines with the adjacent order difference of 0.18 times the frequency conversion are the most, and the most number indicates that the frequency conversion sideband characteristics of the corresponding axis are the most obvious.
Next, spectral lines in the combined order spectrum corresponding to the other axis passband sideband, i.e., spectral lines in the combined order spectrum having the order difference value, may be searched for based on the determined order difference value. The searched spectral lines are the most remarkable rotating frequency sidebands of other axes except the rotating frequency sidebands of the input axis.
Anomaly indicators for other axes may be calculated based on spectral lines corresponding to the other axis rotational frequency sidebands. The abnormality indicator includes, but is not limited to, one of: the energy, number, average amplitude, etc. of the transfrequency sidebands. These anomaly indicators can be used to determine whether other axes are anomalous and the severity of the anomaly.
The process described above is to identify sidebands of the full band order spectrum, which are both sidebands of the high speed or intermediate shaft. However, in the case of a mechanical device comprising multiple gears, the intermediate shaft may also be partially sideband when its low speed shaft fails. When the sideband identification is performed on the order spectrum of the full frequency band according to the method described above, the axis corresponding to the found sideband is likely to be the intermediate axis. Therefore, sideband identification is required for further low-frequency band order spectrum to prevent missing sideband characteristics of the low-speed axis.
Specifically, a spectral line within a predetermined order range (for example, a lower order range such as 0 to 25) may be selected as the third order spectrum in the above-described second order spectrum. And then, removing noise from the third order spectrum to obtain a low-frequency de-noising order spectrum. The process of removing noise is similar to the process of removing noise of the second order spectrum, the outer contour of the third order spectrum is determined firstly, then the outer contour of the third order spectrum is subjected to linear transformation to obtain a low-frequency noise curve, and finally the noise of the third order spectrum is removed based on the low-frequency noise curve. The detailed denoising process can refer to the foregoing, and is not described in detail here.
And then, dividing the spectral lines in the low-frequency denoising order spectrum into multiple clusters of spectral line combinations, combining the corresponding spectral lines of the cluster of spectral line combinations in the third order spectrum for each cluster of spectral line combinations, and taking the maximum amplitude of the cluster of spectral line combinations as the amplitude to obtain the low-frequency combined order spectrum. And then, acquiring order difference values of every two adjacent spectral lines in the low-frequency combined order spectrum, and determining the order difference value which meets a preset condition and has the highest occurrence frequency in the acquired order difference values. Then, spectral lines corresponding to the low-speed shaft rotational frequency sidebands in the low-frequency combined order spectrum are searched based on the determined order difference value. The detailed process of each step can refer to the description of identifying the frequency conversion sideband of other axes, and is not described herein again.
Finally, an anomaly indicator for the low-speed shaft is calculated based on spectral lines corresponding to the low-speed shaft rotational frequency sidebands. The abnormality indicator includes, but is not limited to, one of: the energy, number, average amplitude, etc. of the transfrequency sidebands. These abnormality indicators can be used to determine whether an abnormality has occurred in the low-speed shaft and the severity of the abnormality.
In summary, according to the data processing method provided by the embodiment of the invention, under the condition of no equipment parameter, the frequency conversion sideband indexes of each shaft (such as an input shaft, an intermediate shaft and a low-speed shaft) of the equipment under each working condition can be accurately obtained based on the vibration signal and the rotating speed signal, and then the equipment abnormity and abnormal component positioning can be identified.
Fig. 8 shows a schematic diagram of a data processing device 800 according to an embodiment of the invention. As shown in fig. 8, the data processing apparatus 800 includes a signal receiving module 810, an order obtaining module 820, a sideband searching module 830, and an index calculating module 840.
The signal receiving module 810 obtains a vibration signal and a rotation speed signal of the mechanical device 110. The order obtaining module 820 is coupled to the signal receiving module 810, and can obtain a first order spectrum of the vibration signal based on the vibration signal and the rotation speed signal. The sideband searching module 830 is coupled to the order obtaining module 820 and is configured to search for spectral lines in the first order spectrum corresponding to the input shaft passband sideband and filter out spectral lines having amplitudes less than a first threshold for the searched spectral lines. The index calculation module 840 is coupled to the sideband searching module 830, and may calculate an abnormal index of the input shaft based on the filtered spectral lines of the input shaft frequency conversion sidebands, where the abnormal index is used to determine whether the input shaft is abnormal and/or the severity of the abnormality.
For the detailed processing logic and implementation process of each module in the data processing apparatus 800, reference may be made to the related description of the anomaly monitoring system 100 and the data processing method 300 in conjunction with fig. 1 to fig. 7, which is not described herein again.
The various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of embodiments of the present invention, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as removable hard drives, U.S. disks, floppy disks, CD-ROMs, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing embodiments of the invention.
In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Wherein the memory is configured to store program code; the processor is configured to perform the methods of embodiments of the present invention according to instructions in the program code stored in the memory.
By way of example, and not limitation, readable media may comprise readable storage media and communication media. Readable storage media store information such as computer readable instructions, data structures, program modules or other data. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. Combinations of any of the above are also included within the scope of readable media.
In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with examples of embodiments of the invention. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of embodiments of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best modes of embodiments of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of an embodiment of this invention.
The present invention may further comprise: a7, the method of a1, further comprising: after the spectral line corresponding to the input shaft frequency conversion sideband in the first-order spectrum is searched, removing the spectral line of the input shaft frequency conversion sideband in the first-order spectrum to obtain a second-order spectrum; removing noise from the second order spectrum to obtain a de-noised order spectrum; dividing spectral lines in the denoising order spectrum into multiple clusters of spectral line combinations; for each cluster of spectral line combination, combining corresponding spectral lines of the cluster of spectral line combination in the second-order spectrum, and taking the maximum amplitude of the cluster of spectral line combination as the amplitude to obtain a combined order spectrum; obtaining the order difference value of every two adjacent spectral lines in the combined order spectrum; determining order difference values which meet a predetermined condition and have the most frequency of occurrence among the obtained order difference values; searching spectral lines corresponding to other shaft rotational frequency sidebands in the combined order spectrum based on the determined order difference value; and calculating the abnormality indexes of other axes based on spectral lines corresponding to the rotating frequency sidebands of the other axes, wherein the abnormality indexes are used for judging whether the other axes are abnormal and/or the severity of the abnormality. A8, the method as defined in a7, wherein the step of denoising the second order spectrum comprises: determining an outer contour of the second order spectrum; performing linear transformation on the outer contour of the second order spectrum to obtain a noise curve; and removing noise from the second order spectrum based on the noise curve. A9, the method of A8, wherein the step of determining the outer contour of the second order spectrum comprises: filtering the second order spectrum; setting a window at the starting point of the filtered second order spectrum; determining the maximum amplitude of the spectral line contained in the window; replacing the amplitude of each spectral line contained in the window with the maximum amplitude of the window; moving the window by a predetermined step; and repeating the steps of determining the maximum amplitude of the window, replacing the amplitudes of the spectral lines contained in the window and moving the window until the window reaches the end point of the filtered second-order spectrum, wherein the spectral lines subjected to amplitude replacement form the outer contour of the second-order spectrum. A10, the method of a9, the step of filtering the second order spectrum comprising: and carrying out Hanpul filtering on the second-order spectrum. A11, the method as defined in A8, wherein denoising the second order spectrum based on the noise curve comprises: for each spectral line of the second order spectrum, the amplitude of that spectral line is subtracted from the corresponding amplitude of that spectral line in the noise curve. A12, the method as defined in a7, wherein the step of dividing the spectral lines in the de-noised order spectrum into multiple clusters of spectral line combinations comprises: and dividing adjacent spectral lines meeting the combination condition into a cluster of spectral line combinations, wherein the combination condition comprises that the difference of orders is not greater than an order threshold value, and the difference of amplitudes is not greater than an amplitude threshold value. A13, the method of a7, wherein the order difference values satisfying the predetermined condition comprise: the order difference values lie within a predetermined range and there are at least a predetermined number of consecutive spectral lines having the order difference values. A14, the method of a7, further comprising: selecting spectral lines in a preset order range from the second order spectrum as a third order spectrum; removing noise from the third order spectrum to obtain a low-frequency denoising order spectrum; dividing spectral lines in the low-frequency denoising order spectrum into multiple clusters of spectral line combinations; for each cluster of spectral line combination, merging corresponding spectral lines of the cluster of spectral line combination in the third-order spectrum, and taking the maximum amplitude of the cluster of spectral line combination as an amplitude to obtain a low-frequency merged-order spectrum; acquiring the order difference value of every two adjacent spectral lines in the low-frequency combined order spectrum; determining order difference values which meet a predetermined condition and have the most frequency of occurrence among the obtained order difference values; searching spectral lines corresponding to the low-speed shaft rotating frequency sideband in the low-frequency merging order spectrum based on the determined order difference value; and calculating an abnormal index of the low-speed shaft based on spectral lines corresponding to the rotational frequency sidebands of the low-speed shaft, wherein the abnormal index is used for judging whether the low-speed shaft is abnormal and/or the abnormal severity degree. A15, the method of any one of A1-14, wherein the mechanical device is a gearbox.
Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in a device as described in this embodiment or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into multiple sub-modules.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of and form different embodiments of the invention. For example, in the following claims, any of the claimed embodiments may be used in any combination.
Furthermore, some of the above embodiments are described herein as a method or combination of elements of a method that can be performed by a processor of a computer system or by other means for performing the functions described above. A processor having the necessary instructions for carrying out the method or method elements described above thus forms a means for carrying out the method or method elements. Furthermore, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is used to implement the functions performed by the elements for the purpose of carrying out the invention.
As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.
While embodiments of the invention have been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the embodiments of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive embodiments. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present embodiments are disclosed by way of illustration and not limitation, the scope of embodiments of the invention being defined by the appended claims.
Claims (10)
1. A data processing method for a mechanical device, comprising:
acquiring a vibration signal and a rotating speed signal of the mechanical equipment;
obtaining a first order spectrum of the vibration signal based on the vibration signal and the rotating speed signal;
searching spectral lines corresponding to input shaft rotating frequency sidebands in the first-order spectrum;
for the searched spectral lines, filtering out the spectral lines of which the amplitude values are smaller than a first threshold; and
and calculating an abnormal index of the input shaft based on the filtered spectral line of the input shaft frequency conversion sideband, wherein the abnormal index is used for judging whether the input shaft is abnormal and/or the abnormal severity.
2. The method of claim 1, wherein obtaining the first order spectrum of the vibration signal comprises:
performing equal-angle resampling on the vibration signal based on the rotating speed signal to obtain a resampling signal;
and carrying out Fourier transform on the resampled signal to obtain the first order spectrum.
3. The method of claim 1, wherein filtering spectral lines in which the magnitude is less than the first threshold comprises:
acquiring an outer contour of the first order spectrum;
determining the first threshold based on the outer contour and the maximum amplitude of the first order spectrum;
and setting the amplitude of the spectral line with the amplitude smaller than the first threshold in the searched spectral lines to be zero.
4. The method of claim 3, the step of obtaining an outer contour of the first order spectrum comprising:
filtering the first order spectrum;
setting a window at the starting point of the filtered first order spectrum;
determining the maximum amplitude of the spectral line contained in the window;
replacing the amplitude of each spectral line contained in the window with the maximum amplitude of the window;
moving the window by a predetermined step;
and repeating the steps of determining the maximum amplitude of the window, replacing the amplitudes of the spectral lines contained in the window and moving the window until the window reaches the end point of the filtered first-order spectrum, wherein the spectral lines subjected to amplitude replacement form the outer contour of the first-order spectrum.
5. The method of claim 4, the step of filtering the first order spectrum comprising:
and carrying out Hanpul filtering on the first-order spectrum.
6. The method of claim 3, wherein determining the first threshold based on the outer contour and the maximum amplitude of the first order spectrum comprises:
multiplying the maximum amplitude of the first-order spectrum by a preset proportion to obtain a first reference threshold;
and comparing the first reference threshold with the maximum amplitude value of the outer contour of the first-order spectrum, and selecting the larger value as the first threshold.
7. A data processing apparatus comprising:
the signal receiving module is suitable for acquiring a vibration signal and a rotating speed signal of the mechanical equipment;
the order acquisition module is suitable for obtaining a first order spectrum of the vibration signal based on the vibration signal and the rotating speed signal;
the sideband searching module is suitable for searching spectral lines corresponding to input shaft rotating frequency sidebands in the first-order spectrum; the method is also suitable for filtering out spectral lines with amplitude values smaller than a first threshold for the searched spectral lines; and
and the index calculation module is suitable for calculating an abnormal index of the input shaft based on the filtered spectral line of the input shaft frequency conversion sideband, wherein the abnormal index is used for judging whether the input shaft is abnormal and/or the abnormal severity.
8. An anomaly monitoring system for monitoring a mechanical device for anomalies, the system comprising:
a mechanical device; a rotational speed sensor and a vibration sensor disposed on the mechanical device; and
a data processing apparatus as claimed in claim 7.
9. A computing device, comprising:
one or more processors; and
a memory;
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the data processing methods of claims 1-6.
10. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the data processing methods of claims 1-6.
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