Production line anomaly detection method and system based on SPC

Abstract

The invention discloses an SPC-based production line anomaly detection method and system, and the method comprises the following steps: S1, collecting magnetic core data which comprises an inductance value, a magnetic flux density, hardness and a size; s2, detecting abnormal conditions of the magnetic core and the production line based on the magnetic core data; and S3, if the production line is abnormal, performing abnormal early warning. The method has the beneficial effects that the qualification conditions of the inductance value, the magnetic flux density, the hardness and the size of the magnetic core can be comprehensively and accurately detected, the abnormal condition of a production line can be judged, early warning can be timely performed, the production efficiency and the product quality are effectively improved, and the production cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of magnetic core production line detection, and in particular to a production line anomaly detection method and system based on SPC. Background Art

[0002] The magnetic core is a magnetic material with high magnetic permeability, usually used in electronic devices, mainly to gather and guide the magnetic field, and is widely used in electromagnetic components such as transformers, inductors, and magnetic heads. As a key component in electronic devices, the quality of the magnetic core directly affects the performance of the electronic equipment.

[0003] In the existing technology, the magnetic core detection process often lacks systematicity and accuracy, and the processing and analysis of the detection data are not comprehensive and in-depth enough, making it difficult to accurately determine whether the magnetic core is qualified and to determine the abnormal situation of the magnetic core production line in real time, resulting in low production efficiency and unstable product quality. Summary of the invention

[0004] In response to the shortcomings of the prior art, this application proposes a production line abnormality detection method and system based on SPC, which can comprehensively and accurately detect the qualified status of the inductance, magnetic flux density, hardness and size of the magnetic core, and can judge the abnormal situation of the production line and issue timely warnings, thereby effectively improving production efficiency and product quality and reducing production costs.

[0005] The following is a technical solution of the present invention, a production line anomaly detection method based on SPC, comprising the following steps:

[0006] S1. Collect core data, including inductance, magnetic flux density, hardness and size;

[0007] S2, detect abnormal conditions of the core and production line based on the core data;

[0008] S3. If the production line is abnormal, an abnormal warning will be issued.

[0009] As a preferred solution of the present invention, S2 comprises the following steps:

[0010] S21, processing missing values ​​of magnetic core data;

[0011] S22, performing abnormal value detection on the magnetic core data, and judging whether the magnetic core is qualified based on the abnormal value;

[0012] S23, judging whether the core data is abnormal based on the specification range, and judging whether the core is qualified based on the number of abnormal data;

[0013] S24, respectively calculating the first production line index and the second production line index of the magnetic core data;

[0014] S25. Determine whether the production line is abnormal based on the first production line index and the second production line index. If the production line is abnormal, issue an abnormal warning.

[0015] As a preferred solution of the present invention, in S21, if the proportion of missing values ​​is less than 2%, the mean filling method is used for filling.

[0016] As a preferred solution of the present invention, S22 comprises the following steps:

[0017] S221. Calculate the first quartile Q1 and the third quartile Q3, and let Q R =Q3-Q1;

[0018] S222, define the abnormal value range as less than Q1-1.1×Q R Or greater than Q3+1.1×Q R data points;

[0019] S223. If the deviation degree of the abnormal value is less than or equal to a1, the abnormal value is replaced by the closest non-abnormal value; if the deviation degree of the abnormal value is greater than a1, the magnetic core is unqualified.

[0020] As a preferred solution of the present invention, S23 comprises the following steps:

[0021] S231, respectively judging whether the core data is within the specification range, if not, the core data is abnormal;

[0022] S232. If the number of abnormal data is greater than or equal to two, the magnetic core is unqualified.

[0023] As a preferred solution of the present invention, in S24, the first production line index is expressed as follows:

[0024]

[0025] In the above formula, C1 is the first production line index, A max A is the maximum value of the core data specification. min is the minimum value of the core data specification, x i is the value of the ith core data, n is the number of core data, is the average value of the core data.

[0026] As a preferred solution of the present invention, in S24, the second production line index is expressed as follows:

[0027]

[0028] In the above formula, C2 is the second production line index, A max A is the maximum value of the core data specification. min is the minimum value of the core data specification, xi is the value of the ith core data, n is the number of core data, is the average value of the core data.

[0029] As a preferred solution of the present invention, S25 includes:

[0030] If C1≥a2 and C2≥a2, the production line is normal and the detection cycle of the core data is increased;

[0031] If C1≥a2, 1≤C2<a2 or C2≥a2, 1≤C1<a2, the production line is normal and the detection cycle of the core data is maintained;

[0032] If 1≤C1<a2 and 1≤C2<a2, the production line is normal and the detection cycle of the core data is reduced;

[0033] If C1<1 or C2<1, the production line is abnormal and an abnormal warning is issued.

[0034] A production line anomaly detection system based on SPC, comprising:

[0035] A data detection module is used to detect inductance data and is connected to a data acquisition module;

[0036] Data acquisition module, used to cache inductance data and connect to data analysis module;

[0037] The data analysis module determines whether the inductor production line is abnormal based on the inductance data and connects to the abnormality warning module;

[0038] Abnormal warning module, if the inductor production line is abnormal, it will output warning information.

[0039] As a preferred solution of the present invention, the data detection module includes:

[0040] Inductance tester, used to measure the inductance of the magnetic core, connected to the data acquisition unit via the USB interface;

[0041] The flux meter is used to measure the magnetic flux density of the magnetic core and is connected to the data acquisition unit through the RS-232 serial port;

[0042] Hardness tester, used to measure the hardness of the magnetic core, connected to the data acquisition unit via Bluetooth;

[0043] Micrometer, used to measure the size of the magnetic core, is connected to the data acquisition unit via wireless radio frequency.

[0044] The beneficial effects of the present invention are:

[0045] 1. The present invention integrates a variety of testing equipment and data processing tools through systematic device design, and can comprehensively and accurately detect whether the inductance, magnetic flux density, hardness and size of the magnetic core are qualified;

[0046] 2. The data processing and analysis method of the present invention makes the evaluation of the quality of the magnetic core more accurate and can determine the capacity of the production line;

[0047] 3. The abnormality judgment and early warning method based on production line capacity analysis of the present invention can timely discover abnormal conditions in the magnetic core production process, effectively improve production efficiency and product quality, and reduce production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of the production line abnormality detection method based on SPC of the present invention;

[0049] Figure 2 This is a flow chart of step S2 of the SPC-based production line anomaly detection method of the present invention;

[0050] Figure 3 It is a first schematic diagram of the production line abnormality detection system based on SPC of the present invention;

[0051] Figure 4 is a second schematic diagram of the SPC-based production line anomaly detection system of the present invention;

[0052] In the figure: 1. Data detection module; 2. Data acquisition module; 3. Data analysis module; 4. Abnormal warning module; 11. Inductance tester; 12. Fluxmeter; 13. Hardness tester; 14. Micrometer; 41. Speaker; 42. LED light; 43. Short message; 44. Mobile app. DETAILED DESCRIPTION

[0053] In order to make the technical problems solved by the present invention, the technical solutions adopted and the technical effects achieved clearer, the technical solutions of the embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0054] Embodiment 1:

[0055] like Figure 3 and Figure 4 As shown, a production line anomaly detection system based on SPC includes:

[0056] Data detection module 1, used for detecting inductance data, connected to data acquisition module 2;

[0057] The data acquisition module 2 is used to cache the inductance data and is connected to the data detection module 1 and the data analysis module 3;

[0058] The data analysis module 3 determines whether the inductor production line is abnormal based on the inductance data, and is connected to the data acquisition module 2 and the abnormal warning module 4;

[0059] The abnormal warning module 4 outputs warning information if the inductor production line is abnormal and is connected to the data analysis module 3.

[0060] The data detection module 1 includes an inductance tester 11, a fluxmeter 12, a hardness tester 13 and a micrometer 14, wherein:

[0061] The inductance tester 11 is used to measure the inductance of the magnetic core and transmit the inductance to the data acquisition unit through the USB interface; the inductance tester 11 adopts high-precision inductance measurement technology and has multiple measurement frequency bands. It can select a suitable frequency band for measurement according to different application scenarios of the magnetic core to ensure the accuracy of the measurement.

[0062] The flux meter 12 is used to measure the magnetic flux density of the magnetic core and transmit the magnetic flux density to the data acquisition unit through the RS-232 serial communication interface; the flux meter 12 uses the Hall effect principle to quickly and accurately measure the magnetic flux density of the magnetic core.

[0063] The hardness tester 13 is used to measure the hardness of the magnetic core and transmit the hardness to the data acquisition unit via Bluetooth; the hardness tester 13 uses a Rockwell hardness tester, which converts the indentation depth into a hardness value under the action of a specified test force by an indenter.

[0064] The micrometer 14 is used to measure the size of the magnetic core and transmit the size to the data acquisition unit via wireless radio frequency.

[0065] The data acquisition module 2 is a data acquisition card, which is provided with several data acquisition interfaces, including a USB interface, an RS-232 serial port, a Bluetooth and a wireless radio frequency receiving module. The data acquisition card performs preliminary format conversion and cache processing on the received detection data, and then transmits it to the data analysis module 3 through the PCI-Express bus.

[0066] The data analysis module 3 includes computer hardware and data analysis software. The computer hardware uses a high-performance desktop computer with a multi-core processor, large-capacity memory and high-speed hard disk to meet the data processing and storage requirements of the SPC software. The computer is connected to the data acquisition card through the PCI-Express bus to receive the collected test data. The data analysis software uses Minitab, JMP, etc., which is installed in the computer system.

[0067] The abnormal warning module 4 includes an audio-visual warning unit and a mobile terminal warning unit. The audio-visual warning unit is provided with a speaker 41 and an LED light 42. The speaker 41 broadcasts the warning information, and the color of the LED light 42 shows whether the warning is in progress. The warning module transmits the warning information to the mobile terminal warning unit, and displays the warning information through a short message 43 and a mobile terminal app 44.

[0068] According to the above device structure, the USB interface of the inductance tester 11 is connected to the USB interface of the data acquisition card, the RS-232 serial port of the fluxmeter 12 is connected to the RS-232 serial port of the data acquisition card, the hardness tester 13 is paired with the Bluetooth module of the data acquisition card through Bluetooth, and the micrometer 14 communicates with the RF receiving module of the data acquisition card through RF wireless signals. The data acquisition card is installed on the computer motherboard through the PCI-Express bus. The data acquisition card caches the core data and transmits the core data to the data analysis module 3. The data analysis module 3 determines whether the core is qualified and whether the production line is abnormal based on the core data, and transmits the unqualified core data and the warning information of the production line abnormality to the abnormal warning module 4. The abnormal warning module 4 receives the abnormal warning instruction and the core data of the unqualified core transmitted by the data analysis module 3, and the sound and light warning unit broadcasts the warning voice through the speaker 41, and switches the LED light 42 to a flashing red light so that the staff near the production line can find it in time. The abnormal information of the production line is sent to the mobile phone of the staff through the short message 43, so that the staff who are not near the production line can find the abnormality of the production line in time, and the core data of the unqualified core is sent to the mobile app 44, so that the staff can check the core data of the unqualified core at any time and maintain the production line.

[0069] Embodiment 2:

[0070] like Figure 1 and Figure 2 As shown, a production line anomaly detection method based on SPC includes the following steps:

[0071] S1. Collect core data, including inductance, magnetic flux density, hardness and size;

[0072] S2, detect abnormal conditions of the core and production line based on the core data;

[0073] S3. If the production line is abnormal, an abnormal warning will be issued.

[0074] In step S1, the core data is collected, and the core data includes inductance, magnetic flux density, hardness and size. Specifically, the USB interface of the inductance tester 11 is connected to the USB interface of the data acquisition card, the RS-232 serial port of the fluxmeter 12 is connected to the RS-232 serial port of the data acquisition card, the hardness tester 13 is paired with the Bluetooth module of the data acquisition card through Bluetooth, and the micrometer 14 communicates with the RF receiving module of the data acquisition card through RF wireless signals. The inductance tester 11 adopts high-precision inductance measurement technology and has multiple measurement frequency bands. It can select a suitable frequency band for measurement according to different application scenarios of the magnetic core to ensure the accuracy of the measurement, and transmit the inductance to the data acquisition unit through the USB interface. The fluxmeter 12 uses the Hall effect principle to quickly and accurately measure the magnetic flux density of the magnetic core, and transmits the magnetic flux density to the data acquisition unit through the RS-232 serial communication interface. The hardness tester 13 uses a Rockwell hardness tester 13, and the indentation depth is converted into a hardness value by the indenter under the action of the specified test force, and the hardness is transmitted to the data acquisition unit through Bluetooth. The micrometer 14 transmits the size to the data acquisition unit via wireless radio frequency.

[0075] In step S2, the abnormal conditions of the magnetic core and the production line are detected based on the magnetic core data. Specifically, the data analysis module 3 obtains the magnetic core data, processes and analyzes the magnetic core data, and detects the abnormal conditions of the magnetic core and the production line, including the following steps:

[0076] S21, processing missing values ​​of magnetic core data;

[0077] For the collected magnetic core data, we first check whether there are missing values. If the missing value ratio is less than 2%, we use the mean filling method to fill it, that is, calculate the mean of all valid data and replace the missing values ​​with the mean.

[0078] S22, performing abnormal value detection on the magnetic core data, and judging whether the magnetic core is qualified based on the abnormal value;

[0079] The method based on interquartile range is used to identify and process outliers. First, the first quartile Q1 and the third quartile Q3 are calculated. R =Q3-Q1. The abnormal value range is defined as less than Q1-1.1×Q R Or greater than Q3+1.1×Q R For the identified abnormal value, if its deviation is less than or equal to 2%, the adjacent value correction is adopted, that is, the abnormal value is replaced by the closest non-abnormal value; if the deviation is greater than 2%, the core data is abnormal and the core is marked as unqualified. The data analysis module 3 transmits the core data of the unqualified core to the abnormal warning module 4.

[0080] S23, judging whether the core data is abnormal based on the specification range, and judging whether the core is qualified based on the number of abnormal data;

[0081] In actual production, inductance, magnetic flux density, hardness and size have specification ranges, that is, the maximum value of the core data specification and the minimum value of the core data specification. Determine whether the core data is within the specification range. If the inductance is between the minimum value of the inductance specification and the maximum value of the inductance specification, the inductance is normal, otherwise, the inductance is abnormal; if the magnetic flux density is between the minimum value of the magnetic flux density specification and the maximum value of the magnetic flux density specification, the magnetic flux density is normal, otherwise, the magnetic flux density is abnormal; if the hardness is between the minimum value of the hardness specification and the maximum value of the hardness specification, the hardness is normal, otherwise, the hardness is abnormal; if the size is between the minimum value of the size specification and the maximum value of the size specification, the size is normal, otherwise, the size is abnormal. The data analysis module 3 transmits the core data of the unqualified core to the abnormal warning module 4.

[0082] S24, respectively calculating the first production line index and the second production line index of the magnetic core data;

[0083]

[0084] In the above formula, C1 is the first production line index, A max A is the maximum value of the core data specification. min is the minimum value of the core data specification, x i is the value of the ith core data, n is the number of core data, is the average value of the core data.

[0085]

[0086] In the above formula, C2 is the second production line index, A max A is the maximum value of the core data specification. min is the minimum value of the core data specification, x i is the value of the ith core data, n is the number of core data, is the average value of the core data.

[0087] S25. Determine whether the production line is abnormal based on the first production line index and the second production line index. If the production line is abnormal, issue an abnormal warning.

[0088] If C1≥a2 and C2≥a2, the production line has excellent capacity and can stably produce qualified magnetic cores. The production line is normal and the detection cycle of magnetic core data is increased to reduce resource usage.

[0089] If C1≥a2, 1≤C2<a2 or C2≥a2, 1≤C1<a2, the production line capacity is good and can stably produce qualified magnetic cores. The production line is normal and the detection cycle of the magnetic core data is maintained;

[0090] If 1≤C1<a2 and 1≤C2<a2, the production line capacity is average and the production line is normal. Reduce the detection cycle of the core data to detect abnormal conditions of the production line in time;

[0091] If C1<1 or C2<1, the production line is abnormal and a large number of unqualified magnetic cores have been produced, and an abnormal warning is required. The data analysis module 3 transmits the abnormal warning instruction to the abnormal warning module 4 so that the production line can be maintained in time.

[0092] In step S3, if the production line is abnormal, an abnormal warning is performed. Specifically, the abnormal warning module 4 receives the abnormal warning instruction and the core data of the unqualified magnetic core transmitted by the data analysis module 3, and the sound and light warning unit broadcasts the warning voice through the speaker 41, and switches the LED light 42 to a flashing red light so that the staff near the production line can find it in time. The information about the production line abnormality is sent to the staff's mobile phone through a short message 43, so that the staff who are not near the production line can find the production line abnormality in time, and the core data of the unqualified magnetic core is sent to the mobile app 44, so that the staff can check the core data of the unqualified magnetic core at any time, and then maintain the production line.

[0093] The present invention integrates a variety of detection equipment and data processing tools through systematic device design, and can comprehensively and accurately detect various quality characteristics of the magnetic core; the data processing and analysis methods make the assessment of the quality of the magnetic core more accurate and can judge the capacity of the production line; the abnormal judgment and early warning method based on the production line capacity analysis can timely discover abnormal situations in the magnetic core production process, effectively improve production efficiency and product quality, and reduce production costs.

[0094] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0095] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A production line anomaly detection method based on SPC, characterized in that: The following steps are involved: S1. Collect core data, including inductance, magnetic flux density, hardness and size; S2, detect abnormal conditions of the core and production line based on the core data; S3. If the production line is abnormal, an abnormal warning will be issued.

2. The SPC-based production line anomaly detection method according to claim 1, characterized in that: S2 includes the following steps: S21, processing missing values ​​of magnetic core data; S22, performing abnormal value detection on the magnetic core data, and judging whether the magnetic core is qualified based on the abnormal value; S23, judging whether the core data is abnormal based on the specification range, and judging whether the core is qualified based on the number of abnormal data; S24, respectively calculating the first production line index and the second production line index of the magnetic core data; S25. Determine whether the production line is abnormal based on the first production line index and the second production line index. If the production line is abnormal, issue an abnormal warning.

3. The SPC-based production line anomaly detection method according to claim 2, characterized in that: In S21, if the proportion of missing values ​​is less than 2%, the mean filling method is used.

4. The SPC-based production line anomaly detection method according to claim 2, characterized in that: S22 includes the following steps: S221. Calculate the first quartile Q1 and the third quartile Q3, let Q R =Q3-Q1; S222, define the abnormal value range as less than Q1-1.1×Q R Or greater than Q3+1.1×Q R data points; S223. If the deviation degree of the abnormal value is less than or equal to a1, the abnormal value is replaced by the closest non-abnormal value; if the deviation degree of the abnormal value is greater than a1, the magnetic core is unqualified.

5. The SPC-based production line anomaly detection method according to claim 2, characterized in that: S23 includes the following steps: S231, respectively judging whether the core data is within the specification range, if not, the core data is abnormal; S232. If the number of abnormal data is greater than or equal to two, the magnetic core is unqualified.

6. The SPC-based production line anomaly detection method according to claim 2, characterized in that: In S24, the first production line index is expressed as follows: In the above formula, C1 is the first production line index, A max A is the maximum value of the core data specification. min is the minimum value of the core data specification, x i is the value of the ith core data, n is the number of core data, is the average value of the core data.

7. The SPC-based production line anomaly detection method according to claim 6, characterized in that: In S24, the second production line index is expressed as follows: In the above formula, C2 is the second production line index, A max A is the maximum value of the core data specification. min is the minimum value of the core data specification, x i is the value of the ith core data, n is the number of core data, is the average value of the core data.

8. The SPC-based production line anomaly detection method according to claim 7, characterized in that: S25 includes: If C1≥a2 and C2≥a2, the production line is normal and the detection cycle of the core data is increased; If C1≥a2, 1≤C2<a2 or C2≥a2, 1≤C1<a2, the production line is normal and the detection cycle of the core data is maintained; If 1≤C1<a2 and 1≤C2<a2, the production line is normal and the detection cycle of the core data is reduced; If C1<1 or C2<1, the production line is abnormal and an abnormal warning is issued.

9. A production line anomaly detection system based on SPC, applicable to a production line anomaly detection method based on SPC according to any one of claims 1 to 8, characterized in that: include: A data detection module is used to detect inductance data and is connected to a data acquisition module; Data acquisition module, used to cache inductance data and connect to data analysis module; The data analysis module determines whether the inductor production line is abnormal based on the inductance data and connects to the abnormality warning module; Abnormal warning module, if the inductor production line is abnormal, it will output warning information.

10. The SPC-based production line anomaly detection system according to claim 9, characterized in that: The data detection module includes: Inductance tester, used to measure the inductance of the magnetic core, connected to the data acquisition unit via the USB interface; The flux meter is used to measure the magnetic flux density of the magnetic core and is connected to the data acquisition unit through the RS-232 serial port; Hardness tester, used to measure the hardness of the magnetic core, connected to the data acquisition unit via Bluetooth; Micrometer, used to measure the size of the magnetic core, is connected to the data acquisition unit via wireless radio frequency.

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