CN119043699B - Screw locking abnormality detection method, device, equipment and storage medium - Google Patents
Screw locking abnormality detection method, device, equipment and storage medium Download PDFInfo
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Abstract
本申请公开了一种螺丝锁附异常检测方法、装置、设备及存储介质，涉及设备测试技术领域。该方法包括：采集待测试电批设备在预设检测区域进行螺丝锁附时产生的原始振动信号；对原始振动信号进行滤波处理，根据信号频率滤除原始振动信号中与螺丝拧动无关的干扰信号，获得滤波处理后的测试声音信号；将测试声音信号与预先存储的良品信号进行信号特征比对；对特征比对结果符合预设相似度的测试声音信号进行时域特征转换，得到频谱特征曲线；判定频谱特征曲线在预设检测频段的幅值处于合格判定范围内的待测试电批设备为合格设备。通过声学测试判断螺丝锁附异常状态，降低生产锁附设备开发难度和设备成本，降低维护使用难度。
The present application discloses a method, device, equipment and storage medium for detecting abnormal screw locking, and relates to the technical field of equipment testing. The method comprises: collecting the original vibration signal generated by the electric batch equipment to be tested when the electric batch equipment to be tested is screw locking in a preset detection area; filtering the original vibration signal, filtering out the interference signal irrelevant to the screw turning in the original vibration signal according to the signal frequency, and obtaining the test sound signal after filtering; comparing the signal characteristics of the test sound signal with the pre-stored good signal; performing time domain feature conversion on the test sound signal whose feature comparison result meets the preset similarity, and obtaining the spectrum feature curve; judging that the electric batch equipment to be tested whose amplitude of the spectrum feature curve in the preset detection frequency band is within the qualified judgment range is a qualified device. By judging the abnormal state of screw locking through acoustic testing, the difficulty of developing production locking equipment and the equipment cost are reduced, and the difficulty of maintenance and use is reduced.
Description
Technical Field
The present application relates to the field of device testing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for detecting abnormal screw locking.
Background
Some consumer electronic products need to carry out screw locking operation in the assembly process of finished products, and because of key factor errors such as torsion of electric batch equipment, hole position coordinates, product hole position and screw model matching, the problems of abnormal screw locking, screw floating height, screw locking deviation, screw locking omission, screw sliding and the like are caused, and the abnormal screw locking condition is usually detected by adopting a manual visual inspection, optical visual inspection or electric batch locking data analysis mode.
The manual visual inspection has higher detection omission risk, the optical visual inspection has high false detection rate due to environmental condition factors, the detection screw locking state category is less, the intelligent locking equipment adopted by the electric batch locking data analysis has the conditions of high equipment development difficulty, long period and higher cost when screw locking abnormal detection is carried out, and the intelligent locking equipment becomes a pain point of the screw locking abnormal detection technology for assembly production in manufacturing industry.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present application and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The application mainly aims to provide a method, a device, equipment and a storage medium for detecting screw locking abnormality, which aim to solve the technical problem that the detection capability of a screw locking abnormality detection scheme in the prior art is limited.
The application provides a screw locking abnormality detection method, which comprises the steps of collecting an original vibration signal generated when an electric batch device to be tested is locked in a preset detection area, filtering the original vibration signal, filtering interference signals irrelevant to screwing in the original vibration signal according to signal frequency to obtain a filtered test sound signal, comparing the signal characteristics of the test sound signal with those of a pre-stored good product signal, performing time domain feature conversion on the test sound signal with the characteristic comparison result conforming to the preset similarity to obtain a frequency spectrum feature curve, and judging that the amplitude of the frequency spectrum feature curve in a preset detection frequency range is in a qualified judgment range, wherein the electric batch device to be tested is qualified. The abnormal state of screw locking is judged through acoustic test, so that the development difficulty and the equipment cost of production locking equipment are reduced, and the maintenance and use difficulty is reduced.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a flowchart of a first embodiment of a method for detecting abnormal screw locking according to the present application;
FIG. 2 is a schematic diagram of a signal acquisition structure in a first embodiment of a method for detecting abnormal screw locking according to the present application;
FIG. 3 is a schematic flow chart of a second embodiment of a method for detecting abnormal screw locking according to the present application;
FIG. 4 is a schematic diagram of FFT transformation provided in a second embodiment of a method for detecting screw lock anomaly in accordance with the present application;
fig. 5 is a schematic diagram of a loudness analysis flow provided in a second embodiment of a method for detecting abnormal screw locking according to the present application;
FIG. 6 is a schematic diagram of a sharpness analysis process according to a second embodiment of the method for detecting abnormal screw locking of the present application;
FIG. 7 is a flowchart of a third embodiment of a method for detecting abnormal screw locking according to the present application;
FIG. 8 is a schematic block diagram of a screw lock abnormality detection device according to an embodiment of the present application;
Fig. 9 is a schematic structural diagram of a device for detecting abnormal screw locking in a hardware operating environment according to the method for detecting abnormal screw locking in the embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the technical solution of the present application and are not intended to limit the present application.
For a better understanding of the technical solution of the present application, the following detailed description will be given with reference to the drawings and the specific embodiments.
The method comprises the main steps of collecting original vibration signals generated when screw locking is carried out on to-be-tested electric batch equipment in a preset detection area, carrying out filtering processing on the original vibration signals, filtering interference signals irrelevant to screwing in the original vibration signals according to signal frequency to obtain test sound signals after filtering processing, carrying out signal characteristic comparison on the test sound signals and pre-stored good product signals, carrying out time domain feature conversion on the test sound signals with feature comparison results meeting preset similarity to obtain a frequency spectrum feature curve, and judging that the to-be-tested electric batch equipment with the amplitude of the frequency spectrum feature curve in a preset detection frequency range is qualified equipment.
Because key factor errors such as torsion, hole position coordinates, product hole position and screw model matching of electric batch equipment are caused when screw locking operation is carried out in the prior art, the problems of abnormal screw locking, screw floating height, screw locking deflection, screw locking omission, screw sliding and the like are caused, the current situation that manual visual inspection, optical visual inspection or electric batch locking data analysis modes have limited detection omission or detection capability and high equipment cost is applicable to scene limitation.
According to the application, the abnormal state of the screw locking is judged through the acoustic test, the automatic judgment of whether the screw locking is abnormal or not can be carried out without manual detection, and the abnormal locking machine can be timely found through the sharp acquisition judgment of the vibration signal, so that the yield and the passing rate of products are improved. And the screw machine has no requirement, communication support is not needed, and the development cost of equipment is reduced.
It should be noted that, the execution body of the embodiment may be a computing service device with functions of data processing, network communication and program running, for example, a tablet computer, a personal computer, a mobile phone, or a screw locking abnormality detection device capable of implementing the above functions. The present embodiment and the following embodiments will be described below with reference to a screw lock abnormality detection device.
Based on this, an embodiment of the present application provides a method for detecting abnormal screw locking, referring to fig. 1, fig. 1 is a schematic flow chart of a first embodiment of the method for detecting abnormal screw locking according to the present application.
In this embodiment, the method for detecting abnormal screw locking includes steps S10 to S50:
step S10, collecting an original vibration signal generated when screw locking is performed on the to-be-tested electric batch equipment in a preset detection area.
It should be noted that the electric screwdriver may be an electric tool used when screwing and unscrewing a screw, and the electric screwdriver to be detected may be an electric screwdriver that needs to perform a function of detecting abnormal locking of the screw. The preset detection area may be an area where the electric batch device works and performs screw locking, and may also be an area where the screw locking abnormality detection device in this embodiment collects an original vibration signal sent by the electric batch device to be detected, and referring to fig. 2, fig. 2 is a schematic structural diagram of signal collection in the first embodiment of the screw locking abnormality detection method of the present application. The screw locking abnormal detection device can be provided with a vibration sensor and an acoustic card acquisition device which are used for acquiring and transmitting the original vibration signals.
And S20, filtering the original vibration signal, and filtering interference signals irrelevant to screwing in the original vibration signal according to the signal frequency to obtain a test sound signal after the filtering process.
It should be noted that, in the electric screwdriver screwing sound, the normal sound (i.e. the sound of screwing the qualified device) is close to the middle-high frequency band (e.g. 500-8000 Hz) with the abnormal sound (i.e. the sound of screwing the unqualified device), while the non-screwing sound (e.g. the frequency of electric noise or environmental noise) in the electric screwdriver work is mostly outside the frequency band, so that the interference signal in the original vibration signal can be filtered by the band-pass filtering method to obtain the filtered test sound signal, so that the subsequent analysis is more effective and accurate. The specific frequency band of the band-pass filtering may be specifically set (for example, set to 200-10000 Hz) according to the actual sampling condition, which is not specifically limited herein.
It should be appreciated that the bandpass filtering may be implemented by constructing a filter circuit, or by a preset software program.
And S30, comparing the signal characteristics of the test sound signal with those of the pre-stored good signals.
It should be noted that, the good product signal may be a test sound signal obtained by screwing a screw by a pre-stored qualified electric batch device and performing the filtering processing. The characteristic comparison step can compare the acquired test sound signal with the good product signal by using an auto-correlation and cross-correlation comparison algorithm and a signal similarity mode through a self-defined characteristic extraction and calibration mode, if the similarity of the test sound signal and the good product signal is within a preset similarity range, the corresponding electric batch equipment is judged to be qualified through the time domain signal, and if the similarity of the test sound signal and the good product signal is beyond the preset similarity range, the corresponding electric batch equipment is judged to be unqualified through the time domain signal.
It should be understood that the test sound signals of the electric batch equipment with various conditions (including normal conditions, floating conditions, tooth sliding conditions and the like) can be obtained in the early stage, training of the characteristic sample library is performed, characteristic signal waveforms of various conditions are obtained, characteristic signals of good product signals are stored, and meanwhile similarity of the characteristic signals of various bad conditions and the good product signals can be obtained, so that accuracy of model identification is improved.
And S40, performing time domain feature conversion on the test sound signals with the feature comparison result conforming to the preset similarity to obtain a frequency spectrum feature curve.
It should be noted that, the test sound signal with the feature comparison result conforming to the preset similarity may be an electric batch device that is primarily determined to be likely to be qualified, and the test sound signal is subjected to time domain feature conversion to obtain a frequency spectrum feature curve.
It should be appreciated that the spectral signature may include a fast fourier transform (Fast Fourier Transform, FFT) spectrum, which characterizes the test sound signal from the time domain to the frequency domain. The frequency spectrum characteristic curve can also comprise a Bark intensity spectrum, the Bark intensity spectrum can be that in the process of screwing the screw in an electric batch, the normal and abnormal running sounds of the screw can be heard to be obvious differences through human ears, so that the differences can be distinguished from the human ears by judging the hearing feeling, and the frequency spectrum conversion can be carried out on the hearing feeling of different frequency bands through simulating the human ear structure.
And S50, judging that the to-be-tested electric batch equipment with the amplitude of the frequency spectrum characteristic curve in a preset detection frequency range is qualified equipment.
It should be noted that, the difference of the frequency spectrum characteristic curves of the unqualified equipment and the qualified equipment in the partial frequency bands is larger, and the preset detection frequency band may be the frequency band with the largest difference of the frequency spectrum characteristic curves set in the debugging process.
It should be understood that the to-be-tested electric batch equipment corresponding to the frequency spectrum characteristic curve with the amplitude within the preset detection frequency range being within the qualification judging range (for example, the frequency spectrum characteristic curve can be set to be +/-10%, +/-20%, etc.) is judged to be qualified equipment.
In the embodiment, an original vibration signal generated when screw locking is carried out on electric batch equipment to be tested in a preset detection area is acquired, filtering processing is carried out on the original vibration signal, interference signals irrelevant to screw screwing in the original vibration signal are filtered according to signal frequency, a test sound signal after filtering processing is obtained, signal characteristic comparison is carried out on the test sound signal and a pre-stored good product signal, time domain characteristic conversion is carried out on the test sound signal with characteristic comparison results conforming to preset similarity, a frequency spectrum characteristic curve is obtained, and the electric batch equipment to be tested, of which the amplitude of the frequency spectrum characteristic curve in a preset detection frequency range, is judged to be qualified equipment. The abnormal state of the screw locking is judged through acoustic test, so that the automatic judgment of whether the screw locking is abnormal or not can be carried out without manual detection, and the abnormal locking machine can be timely found through the sharp acquisition and judgment of the vibration signals, so that the yield and the passing rate of products are improved. And the screw machine has no requirement, communication support is not needed, and the development cost of equipment is reduced.
In the second embodiment of the present application, the same or similar content as in the first embodiment of the present application may be referred to the above description, and will not be repeated. On this basis, referring to fig. 3, fig. 3 is a schematic flow chart of a second embodiment of the method for detecting abnormal screw locking according to the present application. In step S40, the screw locking abnormality detection method includes:
step S301, the test sound signals with the characteristic comparison result meeting the preset similarity are segmented.
The method comprises the steps of carrying out uniform segmentation processing on a test sound signal according to a preset time window length, wherein the time window length is a fixed preset time length and specifically comprises a time period of 0.1 second to 1 second, or carrying out self-adaptive segmentation processing according to frequency change of the test sound signal, detecting a frequency mutation point or an energy intensive area, segmenting the signal at the frequency mutation point, or carrying out self-adaptive segmentation processing according to amplitude change of the test sound signal, detecting a significant change of the amplitude or a peak value of the signal, segmenting the signal at the significant change point of the amplitude, and comprehensively segmenting the signal according to a combination standard of the time window length, the frequency change or the amplitude change so as to ensure that key features of the signal are fully captured.
The method comprises the steps of detecting frequency amplitude mutation points in a frequency spectrum, determining the frequency amplitude mutation points through a set amplitude change threshold, wherein the frequency amplitude mutation points specifically comprise frequency amplitude changes exceeding a preset percentage or numerical range in an adjacent time window, segmenting the frequency amplitude mutation points to ensure that each segment of signal is in a section with stable frequency change, carrying out energy analysis on each segment of segmented signal, and further subdividing the signal in a certain frequency band if the energy of the segmented signal exceeds the preset energy threshold.
The method comprises the steps of carrying out amplitude analysis on a test sound signal to obtain a curve of amplitude change along with time, setting an amplitude change threshold, wherein the threshold is the speed or amplitude of the amplitude change between adjacent sampling points, the specific threshold can be dynamically adjusted according to the signal intensity, marking as an amplitude abrupt change point when the amplitude change exceeds a preset threshold, carrying out signal segmentation at the amplitude abrupt change point, and carrying out further processing on the segmented signals to ensure that each segment of signal contains uniform amplitude characteristic change and avoid the point of maximum or minimum amplitude from being ignored. If the amplitude fluctuation of a certain segment of signal is large, the segmentation is further refined to capture more detailed information.
The method comprises the steps of carrying out comprehensive segmentation according to a combination standard of time window length, frequency change or amplitude change, carrying out primary segmentation on signals on the basis of a preset time window length, wherein the time window length can be set according to time characteristics of a locking process and is usually between 0.1 second and 1 second, carrying out frequency and amplitude analysis on each segment of signals on the basis of the primary segmentation to obtain frequency change amplitude and amplitude change amplitude of each segment of signals, carrying out secondary segmentation on a segment of signals if the frequency change or the amplitude change of the segment of signals exceeds a preset change threshold value, specifically comprising further segmentation according to frequency mutation points or amplitude mutation points, carrying out energy evaluation on the segmented signals to ensure that the energy concentration of the signals in a key frequency section or an amplitude section is higher, and further carrying out refinement treatment on the section with the key frequency section or the amplitude significantly changed section according to the segmentation result of the signals to ensure detection accuracy.
Step S302, energy conditions of each segment of signals at different frequencies after segmentation are calculated by utilizing Fourier transformation.
The energy condition is used for representing the energy distribution condition of the test sound signal at different frequencies or time periods, and mainly reflects the intensity degree of the signal at each frequency component.
The spectrum characteristic curve comprises an FFT spectrum curve. An FFT or Short-time fourier transform (STFT) may be performed on the test sound signal to show that the energy distribution of the different frequency components in the noise identifies a particular noise source or abnormal frequency component. The test sound signals can be processed in a segmented mode, each segment of signals needs to be windowed for reducing spectrum leakage, the Hamming window mode is adopted, the short-time Fourier computing mode is utilized, the square is taken for the real part of the test sound signals, and the energy of different frequencies of each segment of signals is calculated. The sampling frequency of the test sound signal is greater than twice the signal frequency.
It should be understood that, for N sampling points, after FFT, FFT results of N frequency points can be obtained, where N is typically an integer power of 2 for convenience in performing FFT operation. Let Fi be the sampling frequency, fs be the signal frequency, and N be the number of sampling points. The test sound signal after FFT results in a complex number of N points. Each N points corresponds to a frequency value. The modulus value at this point is the amplitude characteristic at this frequency value. If the peak value of the test sound signal is a and the phase is P, the modulus value of each point (except the first point dc component) of the result of the FFT is N/2 times a. And the first point is the dc component, whose modulus is N times that of the dc component. Some point k after the FFT is represented by complex number m+ni, then the modulus of this complex number isThe phase satisfies pi= mtan (n, m).
Step S303, obtaining FFT spectrum curves in the spectrum characteristic curves according to the energy condition of each section of signal.
The method comprises the steps of preprocessing each segment of signals, preprocessing the signals, including normalization and denoising, applying Fast Fourier Transform (FFT) to each segment of preprocessed signals, converting time domain signals into frequency domain signals, obtaining amplitude and phase of the signals under each frequency component, calculating Power Spectral Density (PSD) of each segment of signals, determining main frequency components of an energy concentration area according to a power spectral density analysis result, marking the main frequency components in a frequency spectrum curve, and performing splicing or weighting on the frequency spectrum curves of all segments of signals to generate a complete frequency spectrum characteristic curve, wherein the frequency spectrum characteristic curve reflects the overall frequency spectrum characteristic of a test sound signal by combining the frequency distribution and the energy concentration condition of each segment of signals.
The method comprises the steps of performing splicing or weighting processing on spectrum curves of all segmented signals, specifically, analyzing the spectrum curves of each segmented signal, extracting main frequency components and corresponding energy values of each segmented signal in a specific frequency range, wherein the main frequency components are frequency components with maximum energy in the spectrum curves, performing frequency alignment processing between adjacent segmented signals, guaranteeing continuity of the spectrum curves of the adjacent segmented signals on a frequency axis, avoiding frequency inconsistency caused by segmentation, performing weighting processing on energy values of overlapping frequency segments if a frequency overlapping part exists, performing weighted average on the weighted average according to duration, energy size or other preset weights of each segmented signal, performing segment-by-segment splicing on the spectrum curves of each segmented signal, splicing the energy distribution of each segmented signal in a frequency spectrum into a complete spectrum characteristic curve according to time sequence, performing weighted smoothing processing on the frequency bands with significant energy variation if the segmented signals exist, guaranteeing the overall smooth transition and the energy consistency of the spliced spectrum characteristic curve, and finally forming the complete spectrum characteristic curve for representing the whole test signal.
It should be noted that, according to the calculated energy status of different frequencies of each signal segment, the FFT spectral curve is obtained in the spectral feature diagram, referring to fig. 4, and fig. 4 is a schematic FFT transformation provided in the second embodiment of the screw lock anomaly detection method of the present application.
Further, referring to fig. 5, fig. 5 is a schematic flow chart of loudness analysis provided in a second embodiment of the method for detecting abnormal screw locking according to the present application. The step S40 further includes:
And step S501, acquiring an actual sound pressure level corresponding to the center frequency of the test sound signal according to the FFT spectrum curve.
The spectral characteristic curve also comprises a loudness analysis curve. In the process of screwing the screw by electric screwdriver, the normal and abnormal running sounds are divided into 1-FB preset critical frequency bands by simulating the human ear structure, and based on the mode, the mode of loudness or sharpness can be adopted for analysis. Loudness is the subjective perception of sound intensity by the human ear, which is a concept of psychoacoustics, usually in terms of square (phon) or loudness units (stone). The loudness generally increases with increasing sound pressure level, but the loudness is not a linear relationship with sound pressure level. The perception of loudness by the human ear gradually decreases as the sound pressure level increases. Sounds of different frequencies have different perception of their loudness by the human ear at the same sound pressure. Typically the human ear is more sensitive to medium and high frequency sounds and less sensitive to low frequency sounds.
It should be understood that the actual sound pressure level Ea corresponding to the center frequency of the test sound signal may be obtained according to the energy conditions of different frequencies in the above-described FFT spectral curves.
Step S502, acquiring the hearing threshold sound pressure level corresponding to the central frequency of each preset critical frequency band on the equal-response curve.
And step S503, calculating and obtaining a loudness analysis curve in the frequency spectrum characteristic curve according to the actual sound pressure level, the hearing threshold sound pressure level and the quantity of the central frequencies of the preset critical frequency bands.
The threshold sound pressure level Eq corresponding to the center frequency of the human ear below the threshold of the preset critical frequency band in the quiet state is obtained by a pre-designed experiment. The frequency band loudness Lp of each preset critical frequency band can be calculated according to the difference value between the hearing threshold sound pressure level and the actual sound pressure level of the preset critical frequency band, so that the loudness formula of each preset critical frequency band can be expressed as:
Lp=f1(Eq)*f2(Ea-Eq)
where f 1 (Eq) is the excitation function of the number of threshold sound pressure levels and f 2 (Ea-Eq) is the excitation function of the test sound signal. And then, integrating the calculated quantity of the central frequencies of the preset critical frequency bands of the frequency band loudness of all the preset critical frequency bands to obtain a loudness analysis curve Ls in the frequency spectrum characteristic curve:
Ls=dz
Further, referring to fig. 6, fig. 6 is a schematic diagram of a sharpness analysis process according to a second embodiment of the method for detecting abnormal screw locking of the present application. The step S40 further includes:
Step S601, calculating a loudness weighting function of each preset critical frequency band.
The spectrum characteristic curve also comprises a sharpness analysis curve. The sharpness corresponds to the perception value of the high frequency component in the noise and has a direct relation with the structure of the noise spectrum, and the unit of the sharpness is acum.
Step S602, obtaining a sharpness analysis curve in the frequency spectrum characteristic curve according to the loudness weighting function of each preset critical frequency band and the frequency band loudness calculation.
It should be noted that, the loudness weighting function h 1 (FB) corresponding to each preset critical frequency band may be set with reference to the following formula, where h 1 (FB) =low-frequency constant C when FB is less than or equal to the function inflection point FT, and h 1(FB)=f3 (FB) when FB is greater than the function inflection point FT, where f 3 (FB) is an exponential variation function of the preset critical frequency band, so as to increase the weight of the medium-high frequency.
In the embodiment, the test sound signals with the characteristic comparison result conforming to the preset similarity are subjected to segmentation processing, the energy conditions of each segment of signals at different frequencies after segmentation are calculated by utilizing Fourier transformation, and FFT (fast Fourier transform) spectrum curves in the spectrum characteristic curves are obtained according to the energy conditions of each segment of signals. Calculating the frequency band loudness of each preset critical frequency band according to the difference value between the hearing threshold sound pressure level and the actual sound pressure level of the preset critical frequency band; and integrating the calculated frequency band loudness of all the preset critical frequency bands to obtain a loudness analysis curve in the frequency spectrum characteristic curve. And calculating the loudness weighting function of each preset critical frequency band, and obtaining the sharpness analysis curve in the frequency spectrum characteristic curve according to the loudness weighting function of each preset critical frequency band and the frequency band loudness calculation. And converting the time domain waveform into a frequency domain and a Bark domain by means of FFT spectrum analysis, loudness and sharpness analysis, and analyzing, comparing and judging. And judging that the to-be-tested electric batch equipment with the amplitude of the frequency spectrum characteristic curve in the preset detection frequency range within the qualification judgment range is qualified equipment, and the to-be-tested electric batch equipment not within the qualification judgment range is unqualified equipment. By collecting and judging the vibration signals, the machine which timely finds out the abnormal locking attachment improves the reliability of analysis.
In the third embodiment of the present application, the same or similar contents as those of the first and second embodiments of the present application can be referred to the description above, and the description thereof will not be repeated. On this basis, referring to fig. 7, fig. 7 is a schematic flow chart of a screw locking abnormality detection method according to a third embodiment of the present application.
In step S10, the method for detecting abnormal screw locking includes:
and step 701, judging whether a trigger starting signal generated when the electric batch equipment to be tested moves to the detection sensing position of the preset detection area is received or not.
It should be noted that, the start signal may be triggered when the accelerometer detects that the screwdriver of the electric batch device to be tested moves to the preset detection area. The preset detection area can be set and adjusted according to the position of the accelerometer.
And step S702, when the trigger starting signal is received, picking up a vibration signal generated by the electric batch equipment to be tested, and judging whether a cut-off signal for ending the operation of the electric batch equipment to be tested is received.
When receiving the trigger start signal, the vibration sensor can be used for picking up a vibration signal generated in the working process of the electric batch equipment to be tested, and meanwhile, the accelerometer continuously collects whether the electric batch equipment to be tested moves out of a preset detection area or not, and the stop signal is triggered when the electric batch equipment to be tested moves out.
And step 703, stopping signal pickup when the cut-off signal is received, and constructing the original vibration signal by using the vibration signal picked up in the process.
It should be noted that, the to-be-tested electric batch equipment returns to the original point after operation, the accelerometer generates a cut-off signal, the vibration sensor stops picking up signals, and the vibration signal picked up by the vibration sensor in the using process of the sound card collecting equipment constructs the original vibration signal.
In the embodiment, whether the trigger starting signal generated when the to-be-tested electric batch equipment moves to the detection sensing position of the preset detection area is received or not is judged, when the trigger starting signal is received, the vibration signal generated by the to-be-tested electric batch equipment is picked up, whether the cut-off signal for ending the operation of the to-be-tested electric batch equipment is received or not is judged, when the cut-off signal is received, signal pickup is stopped, and the original vibration signal is constructed by the vibration signal picked up in the using process. When the production line is used for production test, the shielding box is not required to be additionally arranged, so that the manual detection can be eliminated, whether the screw locking is abnormal or not can be automatically judged, and the equipment test cost is reduced.
It should be noted that the foregoing examples are only for understanding the present application, and do not limit the method for detecting screw locking abnormality of the present application, and it is within the scope of the present application to make more simple changes based on the technical idea.
The present application also provides a device for detecting abnormal screw locking, referring to fig. 8, the device for detecting abnormal screw locking comprises:
the signal acquisition module 10 is used for acquiring an original vibration signal generated when the electric batch equipment to be tested runs in a preset detection area;
The filtering processing module 20 is configured to perform filtering processing on the original vibration signal, and filter an interference signal irrelevant to screwing in the original vibration signal according to a signal frequency to obtain a test sound signal after filtering processing;
the detection and judgment module 30 is used for comparing the signal characteristics of the test sound signal with those of the pre-stored good signals;
The detection and judgment module 30 is further configured to perform time domain feature conversion on the test sound signal with the feature comparison result conforming to the preset similarity, so as to obtain a spectrum feature curve;
The detection and judgment module 30 is further configured to judge that the to-be-tested electric batch device whose amplitude of the spectrum characteristic curve in a preset detection frequency band is within a qualification judgment range is a qualification device.
Optionally, the detection and judgment module 30 is further configured to segment the test sound signal with the feature comparison result meeting the preset similarity, calculate the energy condition of each segment of signal at different frequencies after segmentation by using fourier transform, and obtain the FFT spectrum curve in the spectrum feature curve according to the energy condition of each segment of signal.
Optionally, the detection and judgment module 30 is further configured to obtain an actual sound pressure level corresponding to the center frequency of the test sound signal according to the FFT spectrum curve, obtain a threshold sound pressure level corresponding to the center frequency of each preset critical frequency band on the equal-loudness curve, and calculate and obtain a loudness analysis curve in the spectrum characteristic curve according to the actual sound pressure level, the threshold sound pressure level, and the number of the center frequencies of the preset critical frequency bands.
Optionally, the detection and judgment module 30 is further configured to calculate the frequency band loudness of the preset critical frequency band according to the difference between the hearing threshold sound pressure level and the actual sound pressure level of each preset critical frequency band, and integrate the calculated frequency band loudness of all preset critical frequency bands according to the number of the central frequencies of the preset critical frequency bands to obtain a loudness analysis curve in the frequency spectrum characteristic curve.
Optionally, the detection and judgment module 30 is further configured to calculate a loudness weighting function of each preset critical frequency band, and obtain a sharpness analysis curve in the spectrum characteristic curve according to the loudness weighting function of each preset critical frequency band and the frequency band loudness calculation.
Optionally, the signal acquisition module 10 is further configured to determine whether a trigger start signal generated when the to-be-tested electric batch device moves to a detection sensing position of the preset detection area is received, pick up a vibration signal generated by the to-be-tested electric batch device when the trigger start signal is received, determine whether a stop signal for ending operation of the to-be-tested electric batch device is received, stop signal pick-up when the stop signal is received, and construct the original vibration signal by using the vibration signal picked up in the process.
Optionally, the detection and judgment module 30 is further configured to obtain the test sound signals of the electrical batch devices under various conditions, store the test sound signals of the qualified devices as the good signals, and compare the good signals of the multiple groups of qualified devices with the test sound signals of the unqualified devices to obtain the preset similarity of the preliminary qualified devices.
The screw locking abnormality detection device provided by the application can solve the technical problem that the detection capability of the screw locking abnormality detection scheme in the prior art is limited by adopting the screw locking abnormality detection method in the embodiment. Compared with the prior art, the screw locking abnormal detection device has the advantages that the screw locking abnormal detection device has the same advantages as the screw locking abnormal detection method provided by the embodiment, and other technical characteristics in the screw locking abnormal detection device are the same as the characteristics disclosed by the method of the embodiment, and are not repeated herein.
The application provides screw locking abnormality detection equipment, which comprises at least one processor and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the screw locking abnormality detection method in the first embodiment.
Referring now to fig. 9, a schematic diagram of a screw lock abnormality detection apparatus suitable for use in implementing an embodiment of the present application is shown. The screw locking abnormality detection device in the embodiment of the present application may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal DIGITAL ASSISTANT: personal digital assistant), a PAD (Portable Application Description: tablet computer), a PMP (Portable MEDIA PLAYER: portable multimedia player), a vehicle-mounted terminal (e.g., a vehicle-mounted navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. The screw locking abnormality detection device shown in fig. 9 is only one example, and should not bring any limitation to the function and the range of use of the embodiment of the present application.
As shown in fig. 9, the screw lock abnormality detection apparatus may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access Memory (RAM: random Access Memory) 1004. In the RAM1004, various programs and data necessary for the operation of the screw lock abnormality detection apparatus are also stored. The processing device 1001, the ROM1002, and the RAM1004 are connected to each other by a bus 1005. An input/output (I/O) interface 1006 is also connected to the bus. In general, a system including an input device 1007 such as a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc., an output device 1008 including a Liquid crystal display (LCD: liquid CRYSTAL DISPLAY), a speaker, a vibrator, etc., a storage device 1003 including a magnetic tape, a hard disk, etc., and a communication device 1009 may be connected to the I/O interface 1006. The communication means 1009 may allow the screw locking abnormality detection device to communicate with other devices wirelessly or by wire to exchange data. While a screw lock anomaly detection device having various systems is shown in the figures, it should be understood that not all of the illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. The above-described functions defined in the method of the disclosed embodiment of the application are performed when the computer program is executed by the processing device 1001.
The screw locking abnormality detection equipment provided by the application adopts the screw locking abnormality detection method in the embodiment, and can solve the technical problem that the detection capability of a screw locking abnormality detection scheme in the prior art is limited. Compared with the prior art, the screw locking abnormal detection device has the advantages that the screw locking abnormal detection device has the same advantages as the screw locking abnormal detection method provided by the embodiment, and other technical features of the screw locking abnormal detection device are the same as the features disclosed by the method of the previous embodiment, and are not repeated herein.
It is to be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon for executing the screw lock abnormality detection method in the above-described embodiment.
The computer readable storage medium provided by the present application may be, for example, a U disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of a computer-readable storage medium may include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access Memory (RAM: random Access Memory), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (EPROM: erasable Programmable Read Only Memory or flash Memory), an optical fiber, a portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this embodiment, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to electrical wiring, fiber optic cable, RF (Radio Frequency) and the like, or any suitable combination of the foregoing.
The computer-readable storage medium may be included in the screw locking abnormality detection apparatus or may exist alone without being incorporated in the screw locking abnormality detection apparatus.
Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN: local Area Network) or a wide area network (WAN: wide Area Network), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present application may be implemented in software or in hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.
The readable storage medium provided by the application is a computer readable storage medium, and the computer readable storage medium stores computer readable program instructions (namely computer program) for executing the screw locking abnormality detection method, so that the technical problem that the screw locking abnormality detection scheme in the prior art has limited detection capability can be solved. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the application are the same as those of the screw locking abnormality detection method provided by the embodiment, and are not described in detail herein.
The foregoing description is only a partial embodiment of the present application, and is not intended to limit the scope of the present application, and all the equivalent structural changes made by the description and the accompanying drawings under the technical concept of the present application, or the direct/indirect application in other related technical fields are included in the scope of the present application.
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| CN109813925A (en) * | 2019-02-19 | 2019-05-28 | 厦门盈趣科技股份有限公司 | The method and its hand of a kind of electric screwdriver lock screw quality inspection wear equipment |
| CN110869721A (en) * | 2017-06-13 | 2020-03-06 | 罗伯特·博世有限公司 | Closure detection system |
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| DE102011101599B4 (en) * | 2011-05-13 | 2021-08-05 | Sew-Eurodrive Gmbh & Co Kg | System for the determination of structure-borne noise in a test object |
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| CN110869721A (en) * | 2017-06-13 | 2020-03-06 | 罗伯特·博世有限公司 | Closure detection system |
| CN109813925A (en) * | 2019-02-19 | 2019-05-28 | 厦门盈趣科技股份有限公司 | The method and its hand of a kind of electric screwdriver lock screw quality inspection wear equipment |
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