Swing mechanism operation state detection method, device, controller, and work machine

Abstract

The application provides a swing mechanism operation state detection method and device, a controller and a working machine, and is applied to the technical field of working machines. After obtaining operation information related to the swing motion of the swing mechanism, the method constructs a sampling signal sample based on the operation information, performs Fourier transform on the sampling signal sample to obtain a frequency spectrum sample, further takes a gear or a bearing in the swing mechanism as a target component, extracts the amplitudes of multiple spectral lines in the frequency spectrum sample based on the fault characteristic spectral number of the target component to obtain a constructed frequency spectrum, then performs inverse Fourier transform on the constructed frequency spectrum to obtain an inverse transform signal sample, and determines the operation state of the target component based on the inverse transform signal sample. Through the detection method provided by the application, the operation state of the swing mechanism can be detected based on the operation information of the swing mechanism, which helps to timely discover the swing mechanism fault and timely take corresponding operation and maintenance measures, and further improves the operation safety of the working machine.

Description

Technical Field

[0001] This application relates to the field of power electronics technology, specifically to a method, device, controller, and operating machinery for detecting the operating status of a rotary mechanism. Background Technology

[0002] In practical applications, many construction machines are equipped with slewing mechanisms, which enable engineering operations at different angles from the same location. Taking excavators as an example, an excavator consists of an upper body and a lower body. The upper body is mounted on the lower body via a slewing mechanism. During excavation operations, the lower body's working position is relatively fixed, while the upper body can rotate within a certain slewing angle range through the slewing mechanism, thereby enabling excavation operations at different angles.

[0003] Typically, a slewing mechanism consists of three main parts: a drive unit, a transmission unit, and a slewing support unit. The transmission unit mainly comprises drive gears and support bearings. During the operation of the machinery, if the drive gears or support bearings in the slewing mechanism malfunction, such as the inner and outer rings of the support bearing separating, it will lead to a decrease in the overall rotational efficiency of the slewing mechanism, and may even cause the slewing mechanism to loosen or fall off, affecting operational safety.

[0004] Therefore, how to detect the operating status of the slewing mechanism during the operation of machinery and ensure operational safety has become one of the technical problems that urgently need to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, this application aims to provide a method, device, controller and working machine for detecting the operating status of a slewing mechanism, which helps to improve work safety by detecting the operating status of the slewing mechanism.

[0006] Firstly, this application provides a method for detecting the operating status of a slewing mechanism, including:

[0007] Obtain operational information related to the rotational motion of the slewing mechanism;

[0008] Based on the aforementioned operational information, a sampled signal sample is constructed;

[0009] Perform a Fourier transform on the sampled signal to obtain a spectrum sample;

[0010] In the spectrum sample, the amplitude of multiple spectral lines is extracted based on the fault characteristic spectrum of the target component to obtain the constructed spectrum, wherein the target component includes gears or bearings in the rotary mechanism;

[0011] Perform an inverse Fourier transform on the constructed spectrum to obtain inverse transform signal samples;

[0012] The operating status of the target component is determined based on the inverse transform signal samples.

[0013] Optionally, the operational information includes: vibration signals and rotation signals corresponding to the same timestamp;

[0014] The construction of the sampling signal sample based on the operational information includes:

[0015] Extract the vibration signal corresponding to the target frequency from the vibration signal, and perform envelope processing on the vibration signal corresponding to the target frequency to obtain the processed vibration signal;

[0016] Based on the rotation signal and the preset sampling frequency, the processed vibration signal is sampled at equal angular intervals to obtain an initial signal sample;

[0017] The initial signal sample includes multiple vibration signal amplitudes arranged in time sequence, and each vibration signal amplitude corresponds to a rotation angle of the rotary mechanism.

[0018] The initial signal sample is processed according to the preset sample processing rules to obtain the sampled signal sample.

[0019] Optionally, processing the initial signal samples according to preset sample processing rules to obtain sampled signal samples includes:

[0020] The initial signal sample is divided into multiple initial signal sub-samples according to each of the rotation angles, and each initial signal sub-sample includes at least one vibration signal amplitude.

[0021] Retain the target signal sub-samples that include impact vibration characteristics from each of the initial signal sub-samples;

[0022] Sampling signal samples are constructed based on each of the target signal sub-samples.

[0023] Optionally, dividing the initial signal sample into multiple initial signal sub-samples according to each of the rotation angles includes:

[0024] Determine the extreme point angle among the various rotation angles;

[0025] Using the time corresponding to the angles of any two adjacent extreme points as boundaries, the initial signal sample is divided into multiple initial signal sub-samples.

[0026] Optionally, constructing the sampling signal samples based on each of the target signal sub-samples includes:

[0027] Each target signal sub-sample is processed according to a preset processing rule, and the processed target signal sub-samples are arranged in time sequence to obtain the sampled signal samples;

[0028] The preset processing rules include:

[0029] The target signal subsamples corresponding to the inversion process are reversed;

[0030] For target signal subsamples that do not include a preset number of vibration signal amplitudes, zero values ​​are used to supplement the number of vibration signal amplitudes in the target signal subsamples to the preset number.

[0031] Optionally, arranging the processed target signal sub-samples in time sequence to obtain sampled signal samples includes:

[0032] If the number of vibration signal amplitudes included in the target signal sub-samples arranged in time sequence is greater than the preset sample length, the target signal sub-samples arranged in time sequence are truncated according to the preset sample length to obtain the sampled signal sample.

[0033] Optionally, in the spectral sample, the amplitudes of multiple spectral lines are extracted based on the fault characteristic spectral symbols of the target component to obtain a constructed spectrum, including:

[0034] In the spectrum sample, the fault feature spectrum of the target component and the amplitude of the spectral line corresponding to the spectrum that is an integer multiple of the fault feature spectrum are extracted to obtain the constructed spectrum.

[0035] Optionally, determining the operating state of the target component based on the inverse transform signal samples includes:

[0036] The inverse transform signal sample is divided into multiple inverse transform signal sub-samples;

[0037] Calculate the average value of the largest vibration signal amplitude in each of the inverse transform signal sub-samples to obtain the vibration signal mean.

[0038] Calculate the target decibel value corresponding to the mean value of the vibration signal;

[0039] The operating state of the target component corresponding to the target decibel value is determined according to a preset mapping relationship;

[0040] The preset mapping relationship records the correspondence between different decibel values ​​and different motion states of the target component.

[0041] Optionally, the inverse transform signal sample is divided into multiple inverse transform signal sub-samples, including:

[0042] The subsample length is determined based on the sample length of the inverse transform signal sample and the fault characteristic spectrum of the target component;

[0043] The inverse transform signal sample is divided into multiple inverse transform signal sub-samples according to the sub-sample length.

[0044] In a second aspect, the present invention provides a rotating structure operation status detection device, comprising:

[0045] The acquisition unit is used to acquire operating information related to the rotational motion of the slewing mechanism;

[0046] A construction unit is used to construct sampling signal samples based on the operational information;

[0047] The transformation unit is used to perform Fourier transform on the sampled signal samples to obtain spectrum samples;

[0048] An extraction unit is used to extract the amplitude of multiple spectral lines from the spectrum sample based on the fault characteristic spectral symbols of the target component to obtain a constructed spectrum, wherein the target component includes gears or bearings in the rotary mechanism;

[0049] The inverse transform unit is used to perform an inverse Fourier transform on the constructed spectrum to obtain inverse transform signal samples;

[0050] The determining unit is used to determine the operating state of the target component based on the inverse transform signal samples.

[0051] Thirdly, the present invention provides a controller, comprising: a memory and a processor; the memory stores a program suitable for execution by the processor to implement the rotary mechanism operation status detection method according to any one of the first aspects of the present invention.

[0052] Fourthly, the present invention provides a working machine, comprising: a slewing mechanism and a controller as described in the third aspect of the present invention, wherein,

[0053] The controller is connected to the rotary structure.

[0054] Based on the above, the slewing mechanism operation status detection method provided in this application, after acquiring operation information related to the slewing motion of the slewing mechanism, constructs a sampling signal sample based on the operation information, and performs a Fourier transform on the sampling signal sample to obtain a spectrum sample. Further, taking the gear or bearing in the slewing mechanism as the target component, the amplitude of multiple spectral lines is extracted from the spectrum sample based on the fault characteristic spectrum of the target component to obtain a constructed spectrum. Then, an inverse Fourier transform is performed on the constructed spectrum to obtain an inverse transform signal sample, and the operation status of the target component is determined based on the inverse transform signal sample. Through the detection method provided by this invention, the operation status of the slewing mechanism can be detected based on the operation information of the slewing mechanism, which helps to promptly detect slewing mechanism faults and take corresponding maintenance measures, thereby improving the operational safety of the machinery. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a schematic diagram of the structure of a rotary mechanism in the prior art.

[0057] Figure 2 This is a flowchart of a method for detecting the operating status of a rotary mechanism provided in an embodiment of the present invention.

[0058] Figure 3 This is a schematic diagram illustrating the sampling effect of sampling vibration signals at equal time intervals.

[0059] Figure 4 This is a schematic diagram illustrating the sampling effect of sampling vibration signals at equal angular intervals.

[0060] Figure 5 This is a schematic diagram of a sampling signal sample provided in an embodiment of the present invention.

[0061] Figure 6 This is a schematic diagram of the spectrum sample provided in an embodiment of the present invention.

[0062] Figure 7 This is a flowchart of constructing a sampled signal sample provided in an embodiment of the present invention.

[0063] Figures 8a-8d This is a schematic diagram illustrating the correlation sampling effect in the constructed sampled signal sample.

[0064] Figure 9 This is a structural block diagram of a rotary mechanism operation status detection device provided in an embodiment of the present invention. Detailed Implementation

[0065] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0066] See Figure 1 , Figure 1 This is a schematic diagram of the structure of a rotary mechanism in the prior art, such as... Figure 1 As shown, the slewing mechanism includes major components such as a slewing reducer, a drive gear, a transition gear, a driven gear, and a slewing support bearing. Figure 1 The document also shows a protective cover to prevent foreign objects from entering and to ensure a safe operating environment inside the slewing bearing. It should be noted that... Figure 1 The configuration of the rotary mechanism shown is merely one optional configuration. In practical applications, the rotary mechanism can also include many other implementations. This invention does not limit the specific implementation of the rotary mechanism. Of course, regarding only... Figure 1 As shown in the configuration, the rotary mechanism may also need to include various other components, which can be found in relevant technologies and will not be listed here.

[0067] As mentioned earlier, if the transmission gears or support bearings in the slewing mechanism malfunction, such as the inner and outer rings of the support bearing separating, it will lead to a decrease in the overall rotational efficiency of the slewing mechanism, and may even cause the slewing mechanism to loosen or fall off, affecting operational safety. To solve this problem, this invention provides a method for detecting the operating status of a slewing mechanism. Based on the operating information of the slewing mechanism, this method detects the operating status of the slewing mechanism, which helps to promptly detect malfunctions and take corresponding maintenance measures, thereby improving the operational safety of the machinery.

[0068] The detection method provided by this invention can be applied to a controller, which can be a controller inherent to the slewing mechanism itself, or another controller on the working machinery independent of the slewing mechanism, such as the vehicle controller of the working machinery or other auxiliary controllers installed on the working machinery. Of course, in some cases, it can also be applied to a server on the network side. See also Figure 2 , Figure 2 This is a flowchart of a method for detecting the operating status of a rotary mechanism according to an embodiment of the present invention. The process may include:

[0069] S100: Obtain operating information related to the rotational motion of the slewing mechanism.

[0070] In the actual operation of the rotary mechanism, there are often various types of operating information related to the rotary motion of the rotary mechanism. In this embodiment, the operating information mainly includes the vibration signal and rotation signal of the rotary mechanism. The rotation signal is further subdivided into speed signal and rotation angle signal.

[0071] In practical applications, vibration signals can be acquired using vibration sensors, rotation speed signals can be acquired using rotation speed sensors, and rotation angle signals can be acquired using angle sensors. Of course, the above-mentioned operational information can also be acquired through other methods, which will not be listed here, and this invention does not impose specific limitations on them.

[0072] It should be noted that, based on actual operating experience, vibrations caused by gear or bearing failures during the operation of a rotary mechanism are often related to the rotation process of the rotary mechanism. The vibration signal and the rotation signal have a direct temporal correlation. Therefore, this method requires the synchronous acquisition of vibration and rotation signals, that is, the vibration signal, speed signal, and rotation angle signal correspond to the same timestamp.

[0073] S110. Construct sampling signal samples based on operational information.

[0074] Understandably, the operating environment of machinery is usually harsh, with very high ambient noise levels. The vibration signals collected by vibration sensors often contain a lot of noise, which is useless for detecting the operating status of the slewing mechanism and needs to be eliminated. Furthermore, for a given slewing mechanism, the frequency of the vibration signal it generates under fault conditions can often be predetermined. For example, the frequency of the vibration signal generated by most slewing mechanisms under fault conditions is usually between 12K±2KHz.

[0075] Based on the above, after acquiring the vibration signal, the vibration signal corresponding to the target frequency can be extracted first, and then envelope processing can be performed on the vibration signal corresponding to the target frequency to obtain the processed vibration signal. As mentioned earlier, the target frequency corresponds to the frequency of the vibration signal generated under fault conditions of the rotary mechanism. In practical applications, this frequency can be determined based on the design parameters and characteristics of the rotary mechanism. This invention does not limit the specific selection of the target frequency. Furthermore, the process of extracting the vibration signal at the target frequency can be implemented using bandpass filtering technology, and the process of enveloping the vibration signal corresponding to the target frequency can be implemented using Hilbert or low-pass filtering technology. This invention also does not limit these methods.

[0076] After obtaining the processed vibration signal, the processed vibration signal is sampled at equal angular intervals based on the rotation signal and a preset sampling frequency to obtain initial signal samples. As mentioned earlier, the rotation signal includes a rotation speed signal and a rotation angle signal. The process of equal angular interval sampling can be completed based on either the rotation speed signal or the rotation angle signal. The goal of equal angular interval sampling is to ensure that the number of sampling points is consistent throughout one revolution of the component.

[0077] If the rotational speed signal is used to complete the angular interval sampling, analog pulses are generated according to a preset sampling frequency, such as 200 times the rotational speed, to complete the angular interval sampling of the vibration signal. At the same time, in order to facilitate subsequent data processing, the rotational angle signal also needs to be sampled synchronously according to the aforementioned analog pulses, so that the sampled vibration signal and the rotational angle signal correspond one-to-one, thereby ensuring that the number of sampling points is consistent for one rotation of the component. For example, with a 200 times rotational speed, 200 points are sampled for one rotation.

[0078] Furthermore, if equal-angle interval sampling is performed using the rotation angle signal, the number of sampling points per revolution needs to be determined based on the preset sampling frequency. Then, the corresponding angle values ​​to be sampled are determined based on the rotation angle signal. Based on this, analog pulses are generated to perform equal-angle sampling on the processed vibration signal. For example, if there are 200 points per revolution, sampling is performed at 1.8°, 3.6°, ..., 360°. It can be understood that when performing equal-angle interval sampling on the processed vibration signal based on the rotation angle signal, the rotation angle signal is first divided to obtain equally spaced angle values. Therefore, similar to equal-angle interval sampling using the rotation speed signal, equal-angle interval sampling based on the rotation angle signal can also obtain a one-to-one correspondence between the vibration signal and the rotation angle signal.

[0079] It should be noted that the above content is only a general introduction to the process of sampling vibration signals at equal angular intervals based on rotation speed signals or rotation angle signals. The specific implementation process can be implemented with reference to relevant technologies. This invention does not limit the specific implementation process of sampling vibration signals at equal angular intervals.

[0080] contrast Figure 3 and Figure 4 As shown, where, Figure 3 The image shows a schematic diagram illustrating the sampling effect after sampling the vibration signal at equal time intervals. Figure 4 The diagram shows the sampling effect after sampling the vibration signal at equal angular intervals. By comparison, it can be seen that the spectral components of the vibration signal obtained after sampling at equal time intervals are more complex, which is not conducive to subsequent data analysis. Conversely, the spectral components of the vibration signal obtained after sampling at equal angular intervals are more uniform, which can effectively reduce the amount of data for subsequent fault detection.

[0081] After sampling the processed vibration signal at equal angular intervals as described above, an initial signal sample is obtained. This initial signal sample includes multiple vibration signal amplitudes arranged in time sequence, and each vibration signal amplitude corresponds to a rotation angle of a rotary mechanism. Of course, the corresponding vibration signals and rotation angles also correspond to the same timestamp.

[0082] Finally, the initial signal sample is processed according to the preset sample processing rules to obtain a sampled signal sample. This sampled signal sample includes multiple sampled signal sub-samples, and each sampled signal sub-sample includes multiple vibration signal amplitudes. The specific process of processing the initial signal sample according to the preset sample processing rules to finally obtain a sampled signal sample that meets the above requirements will be elaborated in subsequent content and will not be described in detail here.

[0083] S120. Perform Fourier transform on the sampled signal to obtain the spectrum sample.

[0084] Assuming the sampled signal is labeled Xcon and has a sample length of M (meaning it contains M amplitude values ​​of various vibration signals), a Fourier transform is performed on the sampled signal. The result is the spectrum sample, which can be labeled Yori for ease of description later. It can be understood that the spectrum sample Yori is a data sequence, for example, [0, 0.03, 1.2], meaning that the amplitude of spectral line 0 is 0, the amplitude of spectral line 1 is 0.03, and the amplitude of spectral line 2 is 1.2.

[0085] For example, see Figure 5 , Figure 5 The image shows a schematic diagram of the waveform of the sampled signal. The waveform of the spectrum sample obtained after performing a Fourier transform on the sampled signal can be found in [reference needed]. Figure 6 As shown.

[0086] S130. In the spectrum sample, the amplitude of multiple spectral lines is extracted based on the fault characteristic spectrum of the target component to obtain the constructed spectrum.

[0087] As mentioned earlier, for a rotary mechanism, the components that have the greatest impact on its safe operation are gears and bearings. Therefore, the target components mentioned in this embodiment can be gears or bearings in the rotary mechanism. In practical applications, the detection method provided by this invention can be used to perform condition detection on the rotary mechanism and bearings respectively, which is also feasible, and this invention does not limit this.

[0088] After identifying the target component, the amplitude of the spectral line corresponding to the fault feature spectrum of the target component and the spectrum number that is an integer multiple of the fault feature spectrum is extracted from the obtained spectrum sample to obtain the constructed spectrum.

[0089] For a specific target component in a rotary mechanism, its fault characteristic spectrum can be calculated using the following formula:

[0090] Np=(M / Nc)×(f1 / f2) (1)

[0091] Wherein, Np represents the fault characteristic spectrum;

[0092] Nc represents the number of sampling points in each sampling period;

[0093] f1 represents the fault characteristic frequency of the target component;

[0094] f2 represents the rotational frequency of the target component.

[0095] It should be noted that for a given target component, its fault characteristic frequency can be calculated precisely, and can be implemented with reference to relevant technologies. This invention does not limit the specific calculation process of the fault characteristic frequency of the target fault. When applying this method to a given rotary mechanism, the fault characteristic frequency of the target component can be pre-stored in the memory, and when running to this step, the fault characteristic frequency can be directly called.

[0096] Furthermore, taking the target component as a gear as an example, assuming that the fault feature spectrum number calculated according to the above formula is 5, the spectrum sample Yori = [0,0.01,0.03,0.06,0.07,1.5,0.04,0.02,0.06,0.07,1.1,0.01,0.03] is extracted. The spectrum number 5 and the spectrum corresponding to the integer multiple spectrum number of the spectrum number 5 are extracted. The amplitude of the remaining spectral lines is set to zero, that is, the constructed spectrum Ycon = [0,0,0,0,0,1.5,0,0,0,0,1.1,0,0].

[0097] S140. Perform an inverse Fourier transform on the constructed spectrum to obtain inverse transform signal samples.

[0098] Performing an inverse Fourier transform on the constructed spectrum Ycon yields the inverse transform signal sample, denoted as Xinv. In practical applications, different constructed spectra can produce different inverse transform signal samples after inverse Fourier transform. The specific implementation process of performing the inverse Fourier transform on the constructed spectrum can be referenced from relevant technologies; this invention does not limit it in this regard.

[0099] S150. Determine the operating status of the target component based on the inverse transform signal samples.

[0100] After obtaining the inverse transform signal samples, the subsample length is first determined based on the sample length of the obtained inverse transform signal samples and the fault characteristic spectrum of the target component. As an optional implementation, the subsample length can be obtained by rounding down the quotient of the inverse transform signal sample length and the fault characteristic spectrum of the target component. For example, if the sample length is 12000 and the gear fault characteristic spectrum is 60, then the subsample length is 12000 / 60 = 200. Further, the inverse transform signal samples are divided into multiple inverse transform signal subsamples according to the subsample length.

[0101] Furthermore, the maximum vibration signal amplitude in each inverse transform signal sub-sample is determined to obtain the maximum amplitude value. Then, the average value of all the obtained maximum amplitude values ​​is calculated to obtain the vibration signal mean. The target decibel value corresponding to the vibration signal mean is then calculated according to the following formula:

[0102] Db=20 × log( C × J / Fspe) (2)

[0103] Where Db represents the target decibel value corresponding to the mean value of the vibration signal;

[0104] C represents the preset coefficient, which can be set according to the actual detection requirements in practical applications;

[0105] J represents the mean value of the vibration signal;

[0106] Fspe indicates the rotational frequency of the target component.

[0107] After obtaining the target decibel value, the operating state of the target component can be determined based on it. As a preferred embodiment, this invention provides a preset mapping relationship, which records the correspondence between different decibel values ​​and different motion states of the target component. Therefore, after obtaining the target decibel value, querying this preset mapping relationship allows determination of the operating state of the target component corresponding to the target decibel value. Generally, the larger the obtained target decibel value, the higher the fault level of the target component, and the more severe the fault.

[0108] In summary, the detection method provided by this invention can detect the operating status of the slewing mechanism based on its operating information, which helps to detect slewing mechanism faults in a timely manner and take corresponding maintenance measures, thereby improving the operational safety of the machinery.

[0109] Based on practical experience, it is known that rotary mechanisms typically rotate non-periodically during operation. Furthermore, due to operational requirements, the rotation angle of each rotation often varies, potentially falling within any angle range of its maximum rotation angle. This means that the rotation may or may not pass through the fault point. Consequently, the vibration signal generated when passing through the fault point is intermittent, resulting in highly variable vibration signals that are difficult to capture, making real-time monitoring of the rotary mechanism's operating status extremely challenging. Therefore, it is crucial to process the collected operational information according to pre-defined sample processing rules to obtain sampling signal samples suitable for operational status detection. The following section will discuss... Figure 7 The specific implementation method of constructing the sampling signal sample in the aforementioned embodiment S110 will be described in detail.

[0110] The specific process for constructing sampling signal samples provided by this invention may include:

[0111] S200. Divide the initial signal sample into multiple initial signal sub-samples according to each rotation angle.

[0112] Based on the relevant content in step S110 above, the initial signal sample obtained after sampling the processed vibration signal at equal angular intervals includes multiple vibration signal amplitudes arranged in time sequence. More importantly, each vibration signal amplitude corresponds to a rotation angle of a rotary mechanism, and the vibration signal amplitude and rotation angle have a one-to-one correspondence. Furthermore, the vibration signal amplitudes and rotation angles with the corresponding relationship also correspond to the same timestamp.

[0113] Based on the above, the initial signal samples can be divided based on the rotation angle. Specifically, assuming the initial signal sample is labeled Xsam, arranging the rotation angles corresponding to each vibration signal amplitude in the same time sequence will yield a set of rotation angles, labeled Asam. It can be understood that the number of elements in the initial signal sample and the rotation angle set is the same.

[0114] First, determine the extreme point angles within the set of rotation angles, i.e., determine the extreme points in Asam. Further, using the times corresponding to any two adjacent extreme point angles as boundaries, divide the initial signal sample into multiple initial signal sub-samples. Through combinations of extreme point angles, Xsam can be divided into multiple periods, with the boundary of each period being the timestamp corresponding to the extreme point angle. Simultaneously, since there is a one-to-one correspondence between rotation angles and vibration signal amplitudes, the initial signal sample can also be divided into multiple initial signal sub-samples based on the timestamps corresponding to the obtained extreme point angles.

[0115] For example, if Asam = [0°, 10°, 20°, 30°, 20°, 10°], then the fourth point of Asam is an extreme value. Asam is divided into two periods, with the number of elements in each period being 4 and 2 respectively. Based on the extreme value angles obtained above, the initial signal sample can be divided into multiple initial signal sub-samples, and each initial signal sub-sample includes at least one vibration signal amplitude.

[0116] S210. Retain the target signal sub-samples that include impact vibration characteristics in each initial signal sub-sample.

[0117] As mentioned earlier, the operating characteristics of the rotary mechanism cause the vibration signal triggered by the fault point to be intermittent. If the period of impact vibration that has not occurred is not removed from the collected samples, the continuity will be disrupted, which is not conducive to spectrum analysis. Therefore, it is necessary to remove the sub-samples that do not include the impact vibration characteristics from each initial signal sub-sample and retain the target signal sub-samples that include the impact vibration characteristics.

[0118] Following the previous example, for each initial signal sub-sample obtained after division, the maximum vibration signal amplitude in each initial signal sub-sample is determined. If the maximum vibration signal amplitude in any initial signal sub-sample does not exceed the preset amplitude threshold, it is confirmed that the initial signal sub-sample does not include impact vibration characteristics, and the initial signal sub-sample is removed. Therefore, by analogy, only the initial signal sub-samples in each initial signal sub-sample whose maximum vibration signal amplitude is greater than the preset amplitude threshold are retained, thus obtaining the target signal sub-sample.

[0119] S220. Construct sampling signal samples based on each target signal sub-sample.

[0120] Based on the actual operation of the slewing mechanism, it is known that the slewing mechanism can rotate in both forward and reverse directions. Therefore, it is necessary to reverse the vibration signal collected when the slewing mechanism is rotating in reverse to ensure that the impact vibration caused by the fault is at the same angle, and at the same time to ensure that the number of data points in each cycle is consistent, thereby improving the reliability of the spectrum analysis results.

[0121] To complete the construction of the sampled signal samples, the embodiments of the present invention propose the following preset processing rules, specifically including: reversing the order of the target signal sub-samples corresponding to the reversal process; and using zero values ​​to supplement the number of vibration signal amplitudes in the target signal sub-samples to the preset number for target signal sub-samples that do not include a preset number of vibration signal amplitudes.

[0122] Following the previous example, let the minimum angle of rotation of the rotary mechanism be Amin and the maximum angle be Amax. The aforementioned target signal subsample can be processed as follows:

[0123] First, the set of rotation angles Asam is differentially processed to obtain the angle intervals corresponding to each rotation angle. If all angle intervals corresponding to the target signal subsample are negative, then the target signal subsample corresponds to a reversal process, and the target sampling subsample needs to be reversed. For example, if the target sampling subsample is [0.01,1,2,3,1,0.01,0.01], after reversing the order, it becomes [0.01,0.01,1,3,2,1,0.01].

[0124] Furthermore, if the minimum angle included in any target sampling signal subsample is not Amin, and / or the maximum angle in any target sampling subsample is not Amax, then the target sampling signal subsample needs to be padded. For example, if the target sampling signal subsample is [0.01,1,2,3,1,0.01,0.01], the corresponding rotation angle set is [10°,20°,30°,40°,50°,60°,70°], and Amin = 0°, Amax = 100°, and the corresponding angle interval is 10°, then 4 data points need to be padded, that is, 4 zero values ​​need to be added. The result after padding is [0,0.01,1,2,3,1,0.01,0.01,0,0,0].

[0125] It is understandable that the preset processing rules mentioned above can be used individually or simultaneously. If any target signal subsample needs to be processed in reverse order and the number of vibration signal amplitudes needs to be supplemented, then the reverse processing can be performed first, and then the number of vibration signal amplitudes in the target signal subsample can be supplemented to the preset number. Of course, as can be seen from the foregoing, the preset number mentioned here is the number of vibration signal amplitudes corresponding to the sampling signal subsample including all vibration signal amplitudes.

[0126] After processing each target sampling signal subsample according to the above content, the target signal subsamples can be further arranged in time sequence. If the number of vibration signal amplitudes included in the arranged vibration signal amplitude set is greater than the preset sample length, the target signal subsamples arranged in time sequence can be truncated according to the preset sample length to obtain the sampling signal sample. Thus, the construction process of the sampling signal sample is completed.

[0127] Combination Figures 8a to 8d As shown, suppose the processed vibration signal is as follows: Figure 8a As shown, the signal includes 5 cycles (bounded by dashed lines). Each cycle has 200 complete sampling points. Due to the characteristics of the rotary mechanism, some cycles have fewer than 200 points, and some cycles are reversed. After processing the vibration signal and performing a Fourier transform, the spectrum becomes cluttered, as shown in the image. Figure 8b As shown, this is not conducive to fault identification. After obtaining the sampled signal samples as described above, each target sampled signal subsample includes 200 vibration signal amplitudes, and the inverted signals are reversed in order, which can achieve the following effect: Figure 8c As shown, the spectral lines obtained by performing a Fourier transform on the most sampled signal samples can be as follows: Figure 8d As shown, with Figure 8b Compare, Figure 8d The resulting spectral lines are clear and orderly, which is helpful for fault identification.

[0128] The following describes the rotary mechanism operation status detection device provided by the present invention. This device belongs to the same concept as the rotary mechanism operation status detection method provided in the embodiments of this application, and can execute the rotary mechanism operation status detection method provided in any embodiment of this application. It possesses the corresponding functional modules and beneficial effects for executing the rotary mechanism operation status detection method. Technical details not described in detail in this embodiment can be found in the rotary mechanism operation status detection method provided in the embodiments of this application, and will not be repeated here.

[0129] See Figure 9 , Figure 9 This is a structural block diagram of a rotary mechanism operation status detection device provided in an embodiment of the present invention. The detection device provided in this embodiment includes:

[0130] Acquisition unit 10 is used to acquire operating information related to the rotational motion of the slewing mechanism;

[0131] Construction unit 20 is used to construct sampling signal samples based on runtime information;

[0132] The transformation unit 30 is used to perform Fourier transform on the sampled signal samples to obtain spectrum samples;

[0133] Extraction unit 40 is used to extract the amplitude of multiple spectral lines from the spectrum sample based on the fault characteristic spectrum of the target component to obtain the constructed spectrum, wherein the target component includes gears or bearings in the rotary mechanism;

[0134] The inverse transform unit 50 is used to perform an inverse Fourier transform on the constructed spectrum to obtain inverse transform signal samples;

[0135] The determination unit 60 is used to determine the operating status of the target component based on the inverse transform signal samples.

[0136] Optionally, the operational information includes: vibration signals and rotation signals corresponding to the same timestamp;

[0137] Construction unit 20, used to construct sampled signal samples based on runtime information, includes:

[0138] Extract the vibration signal corresponding to the target frequency from the vibration signal, and perform envelope processing on the vibration signal corresponding to the target frequency to obtain the processed vibration signal;

[0139] Based on the rotation signal and the preset sampling frequency, the processed vibration signal is sampled at equal angular intervals to obtain the initial signal sample;

[0140] The initial signal sample includes multiple vibration signal amplitudes arranged in time sequence, with each vibration signal amplitude corresponding to the rotation angle of a rotary mechanism.

[0141] The initial signal samples are processed according to the preset sample processing rules to obtain the sampled signal samples.

[0142] Optionally, the construction unit 20 is used to process the initial signal sample according to a preset sample processing rule to obtain the sampled signal sample, including:

[0143] The initial signal sample is divided into multiple initial signal sub-samples according to each rotation angle, and each initial signal sub-sample includes at least one vibration signal amplitude.

[0144] Retain the target signal sub-samples that include impact vibration characteristics from each initial signal sub-sample;

[0145] Sampling signal samples are constructed based on each target signal sub-sample.

[0146] Optionally, the building unit 20 is used to divide the initial signal sample into multiple initial signal sub-samples according to each rotation angle, including:

[0147] Determine the extreme point angles among the various rotation angles;

[0148] Using the time corresponding to the angles of any two adjacent extreme points as boundaries, the initial signal sample is divided into multiple initial signal sub-samples.

[0149] Optionally, the construction unit 20 is used to construct sampling signal samples based on each target signal sub-sample, including:

[0150] Each target signal sub-sample is processed according to the preset processing rules, and the processed target signal sub-samples are arranged in time sequence to obtain the sampled signal samples;

[0151] The preset processing rules include:

[0152] The target signal subsamples corresponding to the inversion process are reversed;

[0153] For target signal subsamples that do not include a preset number of vibration signal amplitudes, zero values ​​are used to supplement the number of vibration signal amplitudes in the target signal subsamples to the preset number.

[0154] Optionally, the construction unit 20 is used to arrange the processed target signal sub-samples in time sequence to obtain sampled signal samples, including:

[0155] If the number of vibration signal amplitudes included in the target signal sub-samples arranged in time sequence is greater than the preset sample length, the target signal sub-samples arranged in time sequence are truncated according to the preset sample length to obtain the sampled signal sample.

[0156] Extraction unit 40 is used to extract the amplitude of multiple spectral lines from the spectrum sample based on the fault characteristic spectral signature of the target component to obtain a constructed spectrum, including:

[0157] In the spectrum sample, the fault feature spectrum of the target component and the amplitude of the spectral line corresponding to the spectrum that is an integer multiple of the fault feature spectrum are extracted to obtain the constructed spectrum.

[0158] Optionally, the determining unit 60 is used to determine the operating state of the target component based on the inverse transform signal samples, including:

[0159] The inverse transform signal sample is divided into multiple inverse transform signal sub-samples;

[0160] Calculate the average value of the largest vibration signal amplitude in each inverse transform signal sub-sample to obtain the mean vibration signal.

[0161] Calculate the target decibel value corresponding to the mean value of the vibration signal;

[0162] The operating status of the target component corresponding to the target decibel value is determined according to the preset mapping relationship;

[0163] The preset mapping relationship records the correspondence between different decibel values ​​and different motion states of the target component.

[0164] Optionally, the determining unit 60 is used to divide the inverse transform signal sample into multiple inverse transform signal sub-samples, including:

[0165] The subsample length is determined based on the sample length of the inverse transform signal sample and the fault characteristic spectrum of the target component.

[0166] The inverse transform signal sample is divided into multiple inverse transform signal sub-samples according to the sub-sample length.

[0167] Optionally, embodiments of the present invention also provide a controller, including: a memory and a processor; the memory stores a program suitable for execution by the processor to implement the rotary mechanism operation status detection method provided in any of the above embodiments.

[0168] Optionally, embodiments of the present invention also provide a working machine, including: a slewing mechanism and a controller provided in the foregoing embodiments, wherein,

[0169] The controller is connected to the rotary structure.

[0170] In some embodiments, this embodiment also provides a computer-readable storage medium, such as a floppy disk, optical disk, hard disk, flash memory, USB flash drive, SD (Secure Digital Memory Card), MMC (Multimedia Card), etc., in which one or more instructions implementing the above steps are stored. When these one or more instructions are executed by one or more processors, the processors execute the rotary mechanism operation status detection method described above. For specific implementation details, please refer to the foregoing description; further elaboration is not provided here.

[0171] In addition to the methods and devices described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the rotary mechanism operation status detection method according to various embodiments of this application as described above.

[0172] Computer program products can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0173] Those skilled in the art will understand that the contents disclosed herein can be varied and modified in many ways. For example, the various devices or components described above can be implemented in hardware, or in software, firmware, or a combination of some or all of the three.

[0174] Furthermore, while this disclosure makes various references to certain elements of systems according to embodiments of this disclosure, any number of different elements may be used and operated on clients and / or servers. Elements are merely illustrative, and different aspects of the system and method may use different elements.

[0175] This disclosure uses flowcharts to illustrate the steps of a method according to embodiments of this disclosure. It should be understood that the preceding or following steps are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes.

[0176] Those skilled in the art will understand that all or part of the steps in the above methods can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium, such as a read-only memory. Optionally, all or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Accordingly, each module / unit in the above embodiments can be implemented in hardware or as a software functional module. This disclosure is not limited to any particular combination of hardware and software.

[0177] Unless otherwise defined, all terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. It should also be understood that terms such as those defined in a common dictionary should be interpreted as having a meaning consistent with their meaning in the context of the relevant art, and not as having an idealized or highly formalized meaning, unless expressly defined herein.

[0178] The foregoing description is intended to illustrate the present disclosure and should not be construed as limiting it. While several exemplary embodiments of the present disclosure have been described, those skilled in the art will readily understand that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present disclosure. Therefore, all such modifications are intended to be included within the scope of the present disclosure as defined by the claims. It should be understood that the foregoing description is intended to illustrate the present disclosure and should not be construed as limiting it to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present disclosure is defined by the claims and their equivalents.

Claims

1. A method for detecting the operating status of a rotary mechanism, characterized in that, include: Obtain operational information related to the rotational motion of the slewing mechanism; The operational information includes: vibration signals and rotation signals corresponding to the same timestamp; Based on the aforementioned operational information, a sampled signal sample is constructed, including: Extract the vibration signal corresponding to the target frequency from the vibration signal, and perform envelope processing on the vibration signal corresponding to the target frequency to obtain the processed vibration signal; Based on the rotation signal and the preset sampling frequency, the processed vibration signal is sampled at equal angular intervals to obtain an initial signal sample. The initial signal sample includes multiple vibration signal amplitudes arranged in time sequence, and each vibration signal amplitude corresponds to a rotation angle of the rotary mechanism. The initial signal sample is processed according to a preset sample processing rule to obtain a sampled signal sample; Perform a Fourier transform on the sampled signal to obtain a spectrum sample; In the spectrum sample, the amplitude of multiple spectral lines is extracted based on the fault characteristic spectrum of the target component to obtain the constructed spectrum, wherein the target component includes gears or bearings in the rotary mechanism; Perform an inverse Fourier transform on the constructed spectrum to obtain inverse transform signal samples; The operating status of the target component is determined based on the inverse transform signal samples; The step of processing the initial signal sample according to a preset sample processing rule to obtain the sampled signal sample includes: The initial signal sample is divided into multiple initial signal sub-samples according to each of the rotation angles, and each initial signal sub-sample includes at least one vibration signal amplitude. Retain the target signal sub-samples that include impact vibration characteristics from each of the initial signal sub-samples; Constructing sampling signal samples based on each of the target signal sub-samples; including: Each target signal sub-sample is processed according to a preset processing rule, and the processed target signal sub-samples are arranged in time sequence to obtain the sampled signal samples; The preset processing rules include: The target signal subsamples corresponding to the inversion process are reversed; For target signal subsamples that do not include a preset number of vibration signal amplitudes, zero values ​​are used to supplement the number of vibration signal amplitudes in the target signal subsamples to the preset number.

2. The method according to claim 1, characterized in that, The step of dividing the initial signal sample into multiple initial signal sub-samples according to each of the rotation angles includes: Determine the extreme point angle among the various rotation angles; Using the time corresponding to the angles of any two adjacent extreme points as boundaries, the initial signal sample is divided into multiple initial signal sub-samples.

3. The method according to claim 1, characterized in that, The step of arranging the processed target signal sub-samples in time sequence to obtain sampled signal samples includes: If the number of vibration signal amplitudes included in the target signal sub-samples arranged in time sequence is greater than the preset sample length, the target signal sub-samples arranged in time sequence are truncated according to the preset sample length to obtain the sampled signal sample.

4. The method according to claim 1, characterized in that, In the spectral sample, the amplitudes of multiple spectral lines are extracted based on the fault characteristic spectral signature of the target component to obtain a constructed spectrum, including: In the spectrum sample, the fault feature spectrum of the target component and the amplitude of the spectral line corresponding to the spectrum that is an integer multiple of the fault feature spectrum are extracted to obtain the constructed spectrum.

5. The method according to claim 1, characterized in that, Determining the operating state of the target component based on the inverse transform signal samples includes: The inverse transform signal sample is divided into multiple inverse transform signal sub-samples; Calculate the average value of the largest vibration signal amplitude in each of the inverse transform signal sub-samples to obtain the mean vibration signal value; Calculate the target decibel value corresponding to the mean value of the vibration signal; The operating state of the target component corresponding to the target decibel value is determined according to a preset mapping relationship; The preset mapping relationship records the correspondence between different decibel values ​​and different motion states of the target component.

6. The method according to claim 5, characterized in that, The inverse transform signal sample is divided into multiple inverse transform signal sub-samples, including: The subsample length is determined based on the sample length of the inverse transform signal sample and the fault characteristic spectrum of the target component; The inverse transform signal sample is divided into multiple inverse transform signal sub-samples according to the sub-sample length.

7. A rotating structure operation status detection device, used to execute the method according to any one of claims 1-6, characterized in that, include: The acquisition unit is used to acquire operating information related to the rotational motion of the slewing mechanism; A construction unit is used to construct sampling signal samples based on the operational information; The transformation unit is used to perform Fourier transform on the sampled signal samples to obtain spectrum samples; An extraction unit is used to extract the amplitude of multiple spectral lines from the spectrum sample based on the fault characteristic spectral symbols of the target component to obtain a constructed spectrum, wherein the target component includes gears or bearings in the rotary mechanism; The inverse transform unit is used to perform an inverse Fourier transform on the constructed spectrum to obtain inverse transform signal samples; The determining unit is used to determine the operating state of the target component based on the inverse transform signal samples.

8. A controller, characterized in that, include: Memory and processor; The memory stores a program suitable for execution by the processor to implement the rotary mechanism operation status detection method according to any one of claims 1 to 6.

9. A type of operating machinery, characterized in that, include: The rotary mechanism and the controller of claim 8, wherein, The controller is connected to the rotary mechanism.

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