Screw locking anomaly detection method, device, equipment and storage medium

Abstract

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

[0001] The present application relates to the field of equipment testing technology, and in particular to a method, device, equipment and storage medium for detecting abnormal screw locking. Background Art

[0002] Some consumer electronic products require screw locking during the finished product assembly process. Due to errors in key factors such as the torque of the electric screwdriver, hole coordinates, product hole position, and screw model matching, problems such as abnormal screw locking, screw floating height, screw locking deviation, screw missing, and screw stripping may occur. Usually, manual visual inspection, optical visual inspection, or electric screwdriver locking data analysis is used to detect abnormal screw locking.

[0003] Manual visual inspection has a high risk of missed detection, optical vision inspection has a high false detection rate due to environmental conditions, and has a small number of screw locking status categories to detect. The intelligent locking equipment used in electric screw locking data analysis has high equipment development difficulties, long cycles and high costs when detecting screw locking anomalies, which has become a pain point in the screw locking anomaly detection process in the manufacturing industry's assembly production.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of the present application is to provide a method, device, equipment and storage medium for detecting abnormal screw locking, aiming to solve the technical problem of limited detection capability of the screw locking abnormality detection scheme in the prior art.

[0006] To achieve the above-mentioned purpose, the present application proposes a method for detecting abnormal screw locking, which includes: collecting the original vibration signal generated by the electric screwdriver to be tested when the electric screwdriver is screwing 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 a spectrum feature curve; judging that the electric screwdriver to be tested is a qualified device if the amplitude of the spectrum feature curve in the preset detection frequency band is within the qualified judgment range. 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0008] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0009] Figure 1 This is a flow chart of the first embodiment of the screw locking abnormality detection method of the present application;

[0010] Figure 2 This is a structural schematic diagram of signal acquisition in the first embodiment of the screw locking abnormality detection method of the present application;

[0011] Figure 3 A flowchart diagram of the second embodiment of the screw locking abnormality detection method of the present application is provided;

[0012] Figure 4 A schematic diagram of FFT transformation provided for the second embodiment of the screw locking anomaly detection method of the present application;

[0013] Figure 5 A schematic diagram of a loudness analysis process provided in Example 2 of the screw locking anomaly detection method of the present application;

[0014] Figure 6 A schematic diagram of a sharpness analysis process provided in Example 2 of the screw locking anomaly detection method of the present application;

[0015] Figure 7 A flowchart diagram of the third embodiment of the screw locking abnormality detection method of the present application;

[0016] Figure 8 This is a schematic diagram of the module structure of the screw locking abnormality detection device according to an embodiment of the present application;

[0017] Fig. 9 Schematic diagram of the structure of a screw locking abnormality detection device in the hardware operating environment involved in the screw locking abnormality detection method in the embodiment of the present application.

[0018] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0020] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0021] The main solution of the embodiment of the present application is: collecting the original vibration signal generated by the electrical screwdriver equipment to be tested when screwing in a preset detection area; filtering the original vibration signal, filtering out interference signals in the original vibration signal that are not related to screw tightening according to the signal frequency, and obtaining a test sound signal after filtering; comparing the signal characteristics of the test sound signal with a pre-stored good product signal; performing time domain feature conversion on the test sound signal whose feature comparison result meets the preset similarity, and obtaining a frequency spectrum characteristic curve; determining that the electrical screwdriver equipment to be tested whose amplitude of the frequency spectrum characteristic curve in the preset detection frequency band is within the qualified judgment range is a qualified device.

[0022] In the prior art, when performing screw locking operations, errors in key factors such as the torque of the electric screwdriver equipment, hole coordinates, product hole positions, and screw model matching may cause problems such as abnormal screw locking, screw floating, screw locking deviation, screw missing, and screw stripping. Manual visual inspection, optical vision inspection, or electric screw locking data analysis methods have the problem of missed detection or limited detection capabilities, and the equipment cost is high and the applicable scenarios are limited.

[0023] This application provides a solution to determine the abnormal state of screw locking through acoustic testing, which can automatically determine whether the screw locking is abnormal without manual detection. Through the sensitive collection and judgment of vibration signals, the machine with abnormal locking can be found in time, which improves the yield and pass rate of the product. In addition, there is no requirement for the screw machine, no communication support is required, and the development cost of the equipment is reduced.

[0024] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a screw locking abnormality detection device that can realize the above functions. The following takes the screw locking abnormality detection device as an example to illustrate this embodiment and the following embodiments.

[0025] Based on this, the embodiment of the present application provides a method for detecting abnormal screw locking. Figure 1 , Figure 1 This is a flow chart of the first embodiment of the screw fastening abnormality detection method of the present application.

[0026] In this embodiment, the screw locking abnormality detection method includes steps S10 to S50:

[0027] Step S10: collecting the original vibration signal generated by the electric screwdriver to be tested when screwing in a preset detection area.

[0028] It should be noted that the electric screwdriver device may be an electric tool used to tighten and loosen screws, and the electric screwdriver device to be tested may be an electric screwdriver device that needs to perform screw locking abnormality detection. The preset detection area may be the area where the electric screwdriver device is working to lock screws, and may also be the area where the screw locking abnormality detection device in this embodiment collects the original vibration signal emitted by the electric screwdriver device to be tested, refer to Figure 2 , Figure 2 This is a schematic diagram of the structure of signal acquisition in the first embodiment of the screw locking abnormality detection method of the present application. The screw locking abnormality detection device may be provided with a vibration sensor and a sound card acquisition device for collecting and transmitting the original vibration signal.

[0029] Step S20: filtering the original vibration signal, filtering out interference signals irrelevant to screw tightening in the original vibration signal according to the signal frequency, and obtaining a filtered test sound signal.

[0030] It should be noted that the normal sound (i.e. the sound of screwing by qualified equipment) and the abnormal sound (i.e. the sound of screwing by unqualified equipment) in the electric screwdriver are close to the medium and high frequency band (e.g. 500-8000Hz), while the non-screwing sound (e.g. the frequency of electrical noise or environmental noise) in the electric screwdriver is mostly outside this frequency band. Therefore, the interference signal in the original vibration signal can be filtered out by bandpass filtering to obtain the filtered test sound signal, making the subsequent analysis more effective and accurate. The specific frequency band of the bandpass filter can be set according to the actual sampling conditions (e.g. set to 200-10000Hz), and is not specifically limited here.

[0031] It should be understood that bandpass filtering can be achieved by constructing a filtering circuit or by presetting a software program.

[0032] Step S30: performing signal feature comparison between the test sound signal and a pre-stored good product signal.

[0033] It should be noted that the good signal can be a pre-stored test sound signal of a qualified electric screwdriver screwing in the process of screwing, which has been filtered. The feature comparison step can be performed by customizing feature extraction and calibration, using autocorrelation and cross-correlation comparison algorithms, and signal similarity methods to compare the acquired test sound signal with the good signal. If the similarity between the test sound signal and the good signal is within the preset similarity range, the corresponding electric screwdriver may be judged as a qualified device through the time domain signal; if the similarity between the test sound signal and the good signal exceeds the preset similarity range, the corresponding electric screwdriver may be judged as an unqualified device through the time domain signal.

[0034] It should be understood that by obtaining the test sound signals of the electric batch equipment in various conditions (including normal conditions, floating conditions, and slipped tooth conditions, etc.) in advance, the feature sample library can be trained to obtain the characteristic signal waveforms of various conditions, and the characteristic signals of good product signals can be saved. At the same time, the similarity between the characteristic signals of various bad conditions and the good product signals can be obtained to improve the accuracy of model recognition.

[0035] Step S40: performing time domain feature conversion on the test sound signal whose feature comparison result meets the preset similarity to obtain a frequency spectrum feature curve.

[0036] It should be noted that the test sound signal whose feature comparison result meets the preset similarity can be preliminarily judged to be a qualified electric batch device, and the test sound signal is converted into a frequency spectrum characteristic curve by time domain feature conversion.

[0037] It should be understood that the frequency spectrum characteristic curve may include a Fast Fourier Transform (FFT) spectrum, which changes the test sound signal from the time domain to the frequency domain for feature analysis. The frequency spectrum characteristic curve may also include a Bark intensity spectrum, which may be a relatively obvious difference between the sound of normal and abnormal screwing during the process of screwing with an electric screwdriver, and thus the difference can be artificially distinguished from the human ear, and thus the spectrum conversion can be performed by simulating the human ear structure's perception of different frequency bands.

[0038] Step S50: Determine that the electrical batch device to be tested whose amplitude of the frequency spectrum characteristic curve in the preset detection frequency band is within the qualified determination range is a qualified device.

[0039] It should be noted that the spectrum characteristic curves of unqualified devices and qualified devices differ greatly in some frequency bands, and the preset detection frequency band may be a frequency band with the largest spectrum characteristic curve difference set during the debugging process.

[0040] It should be understood that the electrical equipment to be tested corresponding to the frequency spectrum characteristic curve whose amplitude in the preset detection frequency band is always within the qualified judgment range (for example, it can be set to ±10%, ±20%, etc.) is judged as a qualified equipment.

[0041] In this embodiment, the original vibration signal generated by the electric batch equipment to be tested when the screw is locked in the preset detection area is collected; the original vibration signal is filtered, and the interference signal irrelevant to the screwing in the original vibration signal is filtered out according to the signal frequency to obtain the test sound signal after filtering; the signal feature of the test sound signal is compared with the pre-stored good signal; the test sound signal whose feature comparison result meets the preset similarity is converted into the time domain feature to obtain the spectrum feature curve; 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 determined to be a qualified device. The abnormal state of screw locking can be judged by acoustic testing, and it can automatically judge whether the screw locking is abnormal without manual detection. Through the keen collection and judgment of vibration signals, the machine with abnormal locking can be found in time, which improves the yield and pass rate of the product. In addition, there is no requirement for the screw machine, no communication support is required, and the development cost of the equipment is reduced.

[0042] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 , Figure 3 The flowchart of the second embodiment of the screw locking abnormality detection method of the present application is provided. In step S40, the screw locking abnormality detection method includes:

[0043] Step S301: segmenting the test sound signal whose feature comparison result meets the preset similarity.

[0044] Among them, the step of segmenting the test sound signal includes: uniformly segmenting the test sound signal according to a preset time window length, the time window length is a fixed preset duration, specifically including a time period of 0.1 seconds to 1 second; or, adaptively segmenting the test sound signal according to the frequency change of the test sound signal, detecting the frequency mutation point or energy-intensive area, and segmenting the signal at the frequency mutation point; or, adaptively segmenting the test sound signal based on the amplitude change of the test sound signal, detecting the significant change of the amplitude or the signal peak point, and segmenting the signal at the point of significant amplitude change; the segmentation processing can also be comprehensively segmented according to the combined criteria of the time window length, frequency change or amplitude change to ensure that the key features of the signal are fully captured.

[0045] Furthermore, the step of performing adaptive segmentation processing based on frequency changes includes: performing Fourier transform (FFT) on the test sound signal to obtain its spectrum information; detecting the mutation point of the frequency amplitude in the spectrum, the mutation point of the frequency amplitude is determined by a set amplitude change threshold, specifically including the frequency amplitude changing by more than a preset percentage or numerical range in adjacent time windows; segmenting at the mutation point of the frequency amplitude to ensure that each signal segment is within a relatively stable frequency change interval; performing energy analysis on each segmented signal, and if the signal energy of a certain frequency band exceeds the preset energy threshold, further subdividing the signal in the frequency band.

[0046] The steps of adaptive segmentation based on amplitude change include: performing amplitude analysis on the test sound signal to obtain a curve of amplitude change over time; setting a threshold for amplitude change, which is the rate or amplitude of amplitude change between adjacent sampling points, and the specific threshold can be dynamically adjusted according to the signal strength; when the amplitude change exceeds the preset threshold, it is marked as an amplitude mutation point, and the signal is segmented at the amplitude mutation point; the segmented signal is further processed to ensure that each segment of the signal contains uniform amplitude feature changes to avoid ignoring the maximum or minimum amplitude points. If the amplitude fluctuation of a certain segment of the signal is large, the segmentation is further refined to capture more detailed information.

[0047] The steps of performing comprehensive segmentation according to the combined standard of time window length, frequency change or amplitude change include: performing preliminary segmentation of the signal based on a preset time window length, the time window length can be set according to the time characteristics of the locking process, and is usually between 0.1 seconds and 1 second; on the basis of the preliminary segmentation, performing frequency and amplitude analysis on each signal segment respectively to obtain the frequency change amplitude and amplitude change amplitude of each signal segment; if the frequency change or amplitude change of a certain signal segment exceeds a preset change threshold, performing secondary segmentation on the signal segment, specifically including further segmentation according to the frequency mutation point or the amplitude mutation point; performing energy evaluation on the segmented signal to ensure that the energy concentration of the signal in the key frequency segment or the amplitude segment is high; based on the segmentation result of the signal, further refining the key frequency band or the segment with significant amplitude change to ensure detection accuracy.

[0048] Step S302: Calculate the energy status of each segment of the signal at different frequencies using Fourier transform.

[0049] The energy status is used to characterize the energy distribution of the test sound signal at different frequencies or time periods, and mainly reflects the strength of the signal at each frequency component.

[0050] It should be noted that the spectrum characteristic curve includes: FFT spectrum curve. FFT or short-time Fourier transform (STFT) can be performed on the test sound signal to display the energy distribution of different frequency components in the noise to identify specific noise sources or abnormal frequency components. The test sound signal can be processed in segments. To reduce spectrum leakage, each signal segment needs to be windowed. This system adopts the Hamming window method and uses the short-time Fourier calculation method to square its real part and calculate the energy of different frequencies of each signal segment. The sampling frequency of the test sound signal must be greater than twice the signal frequency.

[0051] It should be understood that for N sampling points, after FFT, the FFT results of N frequency points can be obtained. In order to facilitate FFT operations, N is usually taken as an integer power of 2. Assume that the sampling frequency is Fi, the signal frequency is Fs, and the number of sampling points is N. Then the result of the test sound signal after FFT is a complex number of N points. Each of the N points corresponds to a frequency value. The modulus value of 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, then the modulus value of each point of the FFT result (except the DC component of the first point) is N / 2 times A. The first point is the DC component, and its modulus value is N times the DC component. After FFT, a point k is represented by the complex number m+ni, so the modulus of this complex number is , the phase satisfies Pi=mtan2(n,m).

[0052] Step S303: acquiring the FFT spectrum curve in the spectrum characteristic curve according to the energy status of each signal segment.

[0053] Specifically, each signal segment is preprocessed, which includes normalization and denoising; Fast Fourier Transform (FFT) is applied to each preprocessed signal segment to convert the time domain signal into a frequency domain signal, and the amplitude and phase of the signal at each frequency component are obtained; the power spectral density (PSD) of each signal segment is calculated; based on the power spectral density analysis results, the main frequency components of the energy concentration area are determined and marked in the spectrum curve; the spectrum curves of all segmented signals are spliced ​​or weighted to generate a complete spectrum characteristic curve, which reflects the overall spectrum characteristics of the test sound signal by combining the frequency distribution and energy concentration of each segmented signal.

[0054] Among them, the step of splicing or weighting the spectral curves of all segmented signals may specifically include: analyzing the spectral curve of each segment signal, extracting the main frequency component and the corresponding energy value of each segment signal within a specific frequency range, wherein the main frequency component is the frequency component with the largest energy in the spectral curve; performing frequency alignment processing between adjacent segment signals to ensure the continuity of the spectral curves of adjacent segment signals on the frequency axis and avoid frequency inconsistency caused by segmentation; if there is a frequency overlapping part, weighting processing is performed on the energy value of the overlapping frequency segment, and the weighted processing is weighted averaged according to the duration, energy size or other preset weights of each segment signal; splicing the spectral curves of all segment signals segment by segment, and splicing the energy distribution of each segment signal in the spectrum into a complete spectral characteristic curve in chronological order; if there are segmented signals with large energy differences, weighted smoothing processing is performed on the frequency bands with significant energy changes to ensure the smooth transition and energy consistency of the spliced ​​spectral characteristic curve as a whole, and finally forming a complete spectral characteristic curve for characterizing the entire test signal.

[0055] It should be noted that, according to the calculated energy status of each signal at different frequencies, the FFT spectrum curve is obtained in the spectrum characteristic diagram, referring to Figure 4 , Figure 4 This is a schematic diagram of FFT transformation provided in Example 2 of the screw locking anomaly detection method of the present application.

[0056] Further, refer to Figure 5 , Figure 5 A schematic diagram of the loudness analysis process provided in Example 2 of the screw locking abnormality detection method of the present application. The step S40 also includes:

[0057] Step S501: obtaining the actual sound pressure level corresponding to the center frequency of the test sound signal according to the FFT spectrum curve.

[0058] It should be noted that the frequency spectrum characteristic curve also includes: a loudness analysis curve. During the process of screwing with an electric screwdriver, the sounds of normal and abnormal screwing are divided into 1~FB preset critical frequency bands by simulating the structure of the human ear. Based on this method, loudness or sharpness can be used for analysis. Loudness is the subjective perception of the human ear to the strength of sound. It is a concept in psychoacoustics, usually measured in phon or sone. Loudness usually increases with the increase of sound pressure level, but there is no linear relationship between loudness and sound pressure level. The human ear's perception of loudness gradually weakens as the sound pressure level increases. The human ear has different perceptions of the loudness of sounds of different frequencies at the same sound pressure. Usually the human ear is more sensitive to mid- and high-frequency sounds, and less sensitive to low-frequency sounds.

[0059] It should be understood that the actual sound pressure level Ea corresponding to the center frequency of the test sound signal can be obtained according to the energy conditions of different frequencies in the above FFT spectrum curve.

[0060] Step S502: obtaining the hearing threshold sound pressure level corresponding to the center frequency of each preset critical frequency band on the equal loudness curve.

[0061] Step S503: Calculate and obtain 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 number of center frequencies of the preset critical frequency bands.

[0062] It should be noted that the hearing threshold sound pressure level Eq corresponding to the center frequency of the human ear hearing threshold of the preset critical frequency band in a quiet state is obtained by pre-designing an experiment. The band loudness Lp of the preset critical frequency band can be calculated according to the difference between the hearing threshold sound pressure level of each preset critical frequency band and the actual sound pressure level; therefore, the loudness formula of each preset critical frequency band can be written as:

[0063] Lp=f1(Eq)*f2(Ea-Eq)

[0064] Wherein, f1(Eq) is the excitation function of the hearing threshold sound pressure level, and f2(Ea-Eq) is the excitation function of the test sound signal. Then, the loudness analysis curve Ls in the spectrum characteristic curve can be obtained by integrating the calculated frequency band loudness of all the preset critical frequency bands and the number of center frequencies of the preset critical frequency bands:

[0065] Ls= dz

[0066] Further, refer to Figure 6 , Figure 6 A schematic diagram of the sharpness analysis process provided in Example 2 of the screw locking abnormality detection method of the present application. The step S40 also includes:

[0067] Step S601: Calculate the loudness weighting function of each preset critical band.

[0068] It should be noted that the spectrum characteristic curve also includes a sharpness analysis curve. Sharpness corresponds to the perceived value of the high-frequency components in the noise, and is directly related to the structure of the noise spectrum. The unit of sharpness is acum.

[0069] 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.

[0070] It should be noted that the loudness weighting function h1(FB) corresponding to each preset critical frequency band can be set according to the following formula: when FB is less than or equal to the function inflection point FT, h1(FB)=low-frequency constant C; when FB is greater than the function inflection point FT, h1(FB)=f3(FB), where f3(FB) is an exponential variation function of the preset critical frequency band to increase the weight of the mid-high frequency.

[0071] In this embodiment, the test sound signal whose feature comparison result meets the preset similarity is processed in segments; the energy status of each segment of the signal at different frequencies after segmentation is calculated by Fourier transform; and the FFT spectrum curve in the spectrum characteristic curve is obtained according to the energy status of each segment of the signal. The band loudness of the preset critical band is calculated according to the difference between the hearing threshold sound pressure level of each preset critical band and the actual sound pressure level; the calculated band loudness of all the preset critical bands is integrated to obtain the loudness analysis curve in the spectrum characteristic curve. The loudness weighting function of each preset critical band is calculated; the sharpness analysis curve in the spectrum characteristic curve is obtained according to the loudness weighting function of each preset critical band and the band loudness calculation. By means of FFT spectrum analysis, loudness and sharpness analysis, the time domain waveform is converted into frequency domain and Bark domain for analysis, comparison and judgment. The electrical batch device to be tested whose amplitude of the spectrum characteristic curve in the preset detection frequency band is within the qualified judgment range is determined to be a qualified device, and the electrical batch device to be tested that is not within the qualified judgment range is determined to be an unqualified device. By collecting and judging vibration signals, machines with abnormal locking can be discovered in time, thus improving the reliability of analysis.

[0072] Based on the first embodiment and / or the second embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those of the above-mentioned first and second embodiments can refer to the above introduction, and will not be repeated later. Figure 7 , Figure 7 This is a flow chart of the third embodiment of the screw locking abnormality detection method of the present application.

[0073] In step S10, the screw locking abnormality detection method includes:

[0074] Step S701: Determine whether a trigger start signal generated when the electrical batch device to be tested moves to the detection sensing position of the preset detection area is received.

[0075] It should be noted that the start signal can be triggered when the accelerometer detects that the screwdriver of the electric screwdriver 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.

[0076] Step S702: When the trigger start signal is received, the vibration signal generated by the electrical batch device to be tested is picked up, and it is determined whether a cut-off signal indicating that the electrical batch device to be tested has ended is received.

[0077] It should be noted that when the trigger start signal is received, the vibration sensor can be used to pick up the vibration signal generated during the operation of the electrical batch equipment to be tested. At the same time, the accelerometer continuously collects whether the electrical batch equipment to be tested has moved out of the preset detection area, and triggers the cut-off signal when it moves out.

[0078] Step S703: When the cutoff signal is received, signal picking is stopped, and the vibration signal picked up during the process is used to construct the original vibration signal.

[0079] It should be noted that after the electric batch equipment to be tested is completed, it returns to the origin, the accelerometer generates a cutoff signal, the vibration sensor stops picking up signals, and the vibration signal picked up by the vibration sensor during the use of the sound card acquisition device constructs the original vibration signal.

[0080] In this embodiment, it is determined whether a trigger start signal is received when the electrical batch device to be tested moves to the detection sensing position of the preset detection area; when the trigger start signal is received, the vibration signal generated by the electrical batch device to be tested is picked up, and it is determined whether a cut-off signal indicating the end of the operation of the electrical batch device to be tested is received; when the cut-off signal is received, signal picking is stopped, and the vibration signal picked up during the process is used to construct the original vibration signal. During production line production testing, no external shielding box is required to automatically determine whether the screw locking is abnormal without manual inspection, thereby reducing equipment testing costs.

[0081] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the screw locking abnormality detection method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0082] This application also provides a screw locking abnormality detection device, please refer to Figure 8 , the screw locking abnormality detection device comprises:

[0083] The signal acquisition module 10 is used to collect the original vibration signal generated when the electric batch equipment to be tested is running in a preset detection area;

[0084] A filtering processing module 20 is used to filter the original vibration signal, filter out interference signals irrelevant to screw tightening in the original vibration signal according to the signal frequency, and obtain a test sound signal after filtering;

[0085] The detection and judgment module 30 is used to compare the signal characteristics of the test sound signal with the pre-stored good product signal;

[0086] The detection and judgment module 30 is further used to perform time domain feature conversion on the test sound signal whose feature comparison result meets the preset similarity to obtain a frequency spectrum feature curve;

[0087] The detection and judgment module 30 is further used to determine that the electrical batch device to be tested is a qualified device if the amplitude of the frequency spectrum characteristic curve in the preset detection frequency band is within the qualified judgment range.

[0088] Optionally, the detection and judgment module 30 is also used to segment the test sound signal whose feature comparison results meet the preset similarity; use Fourier transform to calculate the energy status of each signal segment at different frequencies after segmentation; and obtain the FFT spectrum curve in the spectrum characteristic curve based on the energy status of each signal segment.

[0089] Optionally, the detection and judgment module 30 is further used to obtain the actual sound pressure level corresponding to the center frequency of the test sound signal according to the FFT spectrum curve; obtain the hearing threshold sound pressure level corresponding to the center frequency of each preset critical frequency band on the equal loudness curve; and calculate and obtain the loudness analysis curve in the spectrum characteristic curve according to the actual sound pressure level, the hearing threshold sound pressure level and the number of center frequencies of the preset critical frequency bands.

[0090] Optionally, the detection and judgment module 30 is further used to calculate the band loudness of each 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 band loudness of all the preset critical frequency bands according to the number of center frequencies of the preset critical frequency bands to obtain a loudness analysis curve in the frequency spectrum characteristic curve.

[0091] Optionally, the detection and judgment module 30 is further used to calculate the loudness weighting function of each preset critical frequency band; and obtain 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.

[0092] Optionally, the signal acquisition module 10 is also used to determine whether a trigger start signal generated when the electrical batch device to be tested moves to the detection sensing position of the preset detection area is received; when the trigger start signal is received, the vibration signal generated by the electrical batch device to be tested is picked up, and it is determined whether a cutoff signal indicating the end of the operation of the electrical batch device to be tested is received; when the cutoff signal is received, signal picking is stopped, and the vibration signal picked up during the process is used to construct the original vibration signal.

[0093] Optionally, the detection and judgment module 30 is also used to obtain the test sound signals of the electric batch equipment in various conditions; store the test sound signals of qualified equipment as the good product signals; compare the good product signals of multiple groups of qualified equipment with the test sound signals of unqualified equipment to obtain the preset similarity for preliminary judgment of qualified equipment.

[0094] The screw locking anomaly detection device provided by the present application adopts the screw locking anomaly detection method in the above embodiment, which can solve the technical problem of limited detection capability of the screw locking anomaly detection scheme in the prior art. Compared with the prior art, the beneficial effects of the screw locking anomaly detection device provided by the present application are the same as the beneficial effects of the screw locking anomaly detection method provided by the above embodiment, and the other technical features of the screw locking anomaly detection device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0095] The present application provides a screw locking abnormality detection device, which includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed 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 above-mentioned embodiment 1.

[0096] Reference below Fig. 9 , which shows a schematic diagram of the structure of a screw locking abnormality detection device suitable for implementing the embodiment of the present application. The screw locking abnormality detection device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig. 9 The screw fastening abnormality detection device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0097] like Fig. 9As shown, the screw locking abnormality detection device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the screw locking abnormality detection device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the screw locking anomaly detection device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows a screw locking anomaly detection device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0098] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0099] The screw locking anomaly detection device provided by the present application adopts the screw locking anomaly detection method in the above embodiment, which can solve the technical problem of limited detection capability of the screw locking anomaly detection scheme in the prior art. Compared with the prior art, the beneficial effects of the screw locking anomaly detection device provided by the present application are the same as the beneficial effects of the screw locking anomaly detection method provided by the above embodiment, and the other technical features of the screw locking anomaly detection device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0100] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0101] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0102] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the screw fastening abnormality detection method in the above-mentioned embodiment.

[0103] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0104] The computer-readable storage medium may be included in the screw-fastening abnormality detection device; or may exist independently without being assembled into the screw-fastening abnormality detection device.

[0105] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0106] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0107] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0108] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned screw locking abnormality detection method, and can solve the technical problem that the screw locking abnormality detection scheme in the prior art has limited detection capability. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the screw locking abnormality detection method provided in the above-mentioned embodiment, and will not be elaborated here.

[0109] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for detecting abnormal screw fastening, characterized in that: The screw locking abnormality detection method comprises: Collect the original vibration signal generated by the electric screwdriver to be tested when screwing in the preset detection area; Performing filtering processing on the original vibration signal, filtering out interference signals irrelevant to screw tightening in the original vibration signal according to the signal frequency, and obtaining a test sound signal after filtering processing; Comparing the signal characteristics of the test sound signal with the pre-stored good product signal; Performing time domain feature conversion on the test sound signal whose feature comparison result meets the preset similarity to obtain a frequency spectrum feature curve; Determine that the electrical batch device to be tested whose amplitude of the frequency spectrum characteristic curve in a preset detection frequency band is within a qualified determination range is a qualified device; The frequency spectrum characteristic curve includes: an FFT frequency spectrum curve; the step of performing time domain feature conversion on the test sound signal whose feature comparison result meets the preset similarity to obtain the frequency spectrum characteristic curve includes: Segmentally processing the test sound signal whose feature comparison result meets the preset similarity; Use Fourier transform to calculate the energy status of each segment of the signal at different frequencies after segmentation; The FFT spectrum curve in the spectrum characteristic curve is obtained according to the energy status of each signal segment.

2. The method for detecting abnormal screw fastening according to claim 1, wherein: The frequency spectrum characteristic curve also includes: a loudness analysis curve; the step of performing time domain feature conversion on the test sound signal whose feature comparison result meets the preset similarity to obtain the frequency spectrum characteristic curve includes: Acquire the actual sound pressure level corresponding to the center frequency of the test sound signal according to the FFT spectrum curve; Obtain the hearing threshold sound pressure level corresponding to the center frequency of each preset critical frequency band on the equal loudness curve; A loudness analysis curve in the frequency spectrum characteristic curve is obtained by calculation according to the actual sound pressure level, the hearing threshold sound pressure level and the number of center frequencies of the preset critical frequency bands.

3. The method for detecting abnormal screw fastening according to claim 2, characterized in that: The step of calculating and acquiring the loudness analysis curve in the frequency spectrum characteristic curve according to the actual sound pressure level, the hearing threshold sound pressure level and the number of center frequencies of the preset critical frequency bands comprises: Calculating the frequency band loudness of each preset critical frequency band according to the difference between the hearing threshold sound pressure level and the actual sound pressure level of the preset critical frequency band; The loudness analysis curve in the frequency spectrum characteristic curve is obtained by integrating the calculated frequency band loudnesses of all the preset critical frequency bands according to the number of center frequencies of the preset critical frequency bands.

4. The method for detecting abnormal screw fastening according to claim 3, characterized in that: The frequency spectrum characteristic curve also includes: a sharpness analysis curve; the step of performing time domain feature conversion on the test sound signal whose feature comparison result meets the preset similarity to obtain the frequency spectrum characteristic curve includes: Calculating a loudness weighting function for each preset critical frequency band; The sharpness analysis curve in the frequency spectrum characteristic curve is obtained according to the loudness weighting function of each preset critical frequency band and the frequency band loudness calculation.

5. The method for detecting abnormal screw fastening according to claim 1, wherein: The step of collecting the original vibration signal generated when the electric screwdriver to be tested is screwed in the preset detection area includes: Determine whether a trigger start signal generated when the electrical batch device to be tested moves to the detection sensing position of the preset detection area is received; When the trigger start signal is received, the vibration signal generated by the electrical batch device to be tested is picked up, and it is determined whether a cut-off signal indicating that the electrical batch device to be tested has ended is received; When the cut-off signal is received, signal picking is stopped, and the vibration signal picked up during the process is used to construct the original vibration signal.

6. The method for detecting abnormal screw fastening according to claim 1, wherein: Before the step of comparing the signal characteristics of the test sound signal with the pre-stored good product signal, the method further includes: Acquire the test sound signals of the electric batch equipment in various conditions; The electrical batch equipment in various conditions include: qualified equipment and unqualified equipment; storing the test sound signal of a qualified device as the good product signal; The good product signals of multiple groups of qualified devices are compared with the test sound signals of unqualified devices to obtain the preset similarity for preliminary judgment of qualified devices.

7. A screw locking abnormality detection device, characterized in that: The device comprises: The signal acquisition module is used to collect the original vibration signal generated when the electric batch equipment to be tested is running in the preset detection area; A filtering processing module, used to filter the original vibration signal, filter out interference signals irrelevant to screw tightening in the original vibration signal according to the signal frequency, and obtain a test sound signal after filtering; A detection and judgment module, used for comparing the signal characteristics of the test sound signal with the pre-stored good product signal; The detection and judgment module is further used to perform time domain feature conversion on the test sound signal whose feature comparison result meets the preset similarity to obtain a frequency spectrum feature curve; The detection and judgment module is further used to determine that the electrical batch device to be tested is a qualified device if the amplitude of the frequency spectrum characteristic curve in the preset detection frequency band is within the qualified judgment range; The detection and judgment module is also used to segment the test sound signal whose feature comparison results meet the preset similarity; use Fourier transform to calculate the energy status of each segment of the signal at different frequencies after segmentation; and obtain the FFT spectrum curve in the spectrum feature curve according to the energy status of each segment of the signal.

8. A screw locking abnormality detection device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the screw fastening abnormality detection method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the screw fastening abnormality detection method according to any one of claims 1 to 6 are implemented.

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