CN109062158B - Industrial robot assembly fault detection method - Google Patents
Industrial robot assembly fault detection method Download PDFInfo
- Publication number
- CN109062158B CN109062158B CN201810904456.9A CN201810904456A CN109062158B CN 109062158 B CN109062158 B CN 109062158B CN 201810904456 A CN201810904456 A CN 201810904456A CN 109062158 B CN109062158 B CN 109062158B
- Authority
- CN
- China
- Prior art keywords
- magnetic field
- detection structure
- production line
- detected
- field gradient
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 72
- 238000004519 manufacturing process Methods 0.000 claims abstract description 50
- 239000003990 capacitor Substances 0.000 claims abstract description 20
- 238000003466 welding Methods 0.000 claims abstract description 13
- 230000035945 sensitivity Effects 0.000 claims abstract description 8
- 239000000523 sample Substances 0.000 claims description 30
- 238000004364 calculation method Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000002360 preparation method Methods 0.000 claims 1
- 238000000034 method Methods 0.000 abstract description 11
- 230000008569 process Effects 0.000 abstract description 5
- 238000012423 maintenance Methods 0.000 abstract description 3
- 238000012360 testing method Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000003638 chemical reducing agent Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
-
- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
- H05K13/00—Apparatus or processes specially adapted for manufacturing or adjusting assemblages of electric components
- H05K13/08—Monitoring manufacture of assemblages
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- General Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Operations Research (AREA)
- Microelectronics & Electronic Packaging (AREA)
- Investigating Or Analyzing Materials By The Use Of Magnetic Means (AREA)
Abstract
In order to detect whether the range of stray capacitance is normal or not in the assembling process of an industrial robot with electronic equipment with a plurality of capacitors, the invention provides an industrial robot assembling fault detection method, wherein a magnetic field gradient low-frequency signal component set and a magnetic field gradient high-frequency signal component set of a magnetic field sensor are obtained in various modes to carry out qualitative judgment, whether the size of the stray capacitance formed by related welding points which just finish capacitance welding exceeds a preset standard or not can be rapidly and qualitatively detected on a production line through lower cost, and the average accuracy reaches 77.9% through 500 times of tests. In addition, the qualitative judgment method does not need to have extra requirements on the precision and the sensitivity of the magnetic field sensors, does not need to have special requirements on batches of the magnetic field sensors, and can facilitate the maintenance and the replacement of the magnetic field sensors in the detection process on the premise of not influencing the detection result.
Description
Technical Field
The invention relates to the technical field of fault detection, in particular to an industrial robot assembly fault detection method.
Background
Industrial robots are multi-joint manipulators or multi-degree-of-freedom machine devices oriented to the industrial field, can automatically execute work, and are machines which realize various functions by means of self power and control capacity. The robot can accept human command and operate according to a preset program, and modern industrial robots can also perform actions according to a principle formulated by artificial intelligence technology.
With the development of science and technology in China, more advanced industrial robots are gradually appeared, and although the preservation amount of industrial robots in China reaches 25% of the world, and more achievements also appear in the aspect of research and test of the industrial robot technology, some parts of the existing industrial robots also need to be introduced into technical equipment of foreign enterprises, such as a real-time control system, a high-precision servo motor, a speed reducer and the like. Therefore, China still needs to invest a great deal of energy in the technical research of industrial robots. Some industrial robots have the functions of fault diagnosis and original program monitoring, and no additional network equipment is needed to support a remote service system. The method can use the original ip wired network to remotely communicate with the robot on the client site to remotely diagnose the fault, and can also help the fault maintenance technician to diagnose and process the site fault.
For the assembly of an industrial robot for electronic equipment, such as a television set, having a plurality of capacitors of different types and capacities (more precisely, the assembly in the present invention refers to welding), the capacitance of which is of great importance in terms of stability after welding, in particular the stray capacitance due to the welding spots may pose a potential risk to stability. In the prior art, chinese patent application No. CN200710034549.2 discloses a PWM current measuring method, in which an initial PWM signal is applied to an input pin of an output driver chip, and an output pin of the driver output chip outputs a large-current PWM signal for control; the PWM current flowing out of the output driving chip flows through a sampling resistor arranged in the controller and then flows into an electromagnetic proportional valve outside the controller for controlling an actual controlled object, and the PWM current is directly grounded after passing through the electromagnetic proportional valve; when the PWM current flows through the sampling resistor in the controller, a small voltage which is in direct proportion to the PWM current is generated at two ends of the sampling resistor, the small voltage is introduced into an operational amplifier, after the small voltage is amplified by the operational amplifier, a fluctuating voltage signal is arranged into a stable voltage signal which is in direct proportion to the PWM output current basically through a filter resistor and a filter capacitor which are connected in sequence, and the voltage signal is used for participating in computer control. However, this method cannot satisfy the requirement for detecting whether a failure occurs when the industrial robot mounts the capacitor on the assembly line.
Disclosure of Invention
In order to detect whether the range of stray capacitance is normal or not in the assembly process of an industrial robot of electronic equipment with a plurality of capacitors, the invention provides an industrial robot assembly fault detection method, which comprises the following steps:
(1) carrying out magnetic field gradient detection on electronic equipment assembled with at least part of capacitors on an industrial robot production line in a first mode to obtain a magnetic field gradient low-frequency signal component set L1 and a magnetic field gradient high-frequency signal component set H1;
(2) detecting a magnetic field gradient in a second mode on the electronic equipment assembled with the capacitor on the industrial robot production line relative to a position on the industrial robot production line after the position detected in the first mode to obtain a magnetic field gradient low-frequency signal component set L2 and a magnetic field gradient high-frequency signal component set H2;
(3) detecting the magnetic field gradient of the electronic equipment assembled with the capacitor on the industrial robot production line in a third mode relative to the position on the industrial robot production line after the position detected in the second mode to obtain a magnetic field gradient low-frequency signal component set L3 and a magnetic field gradient high-frequency signal component set H3;
(4) and determining whether the state of the assembly capacitance of the industrial robot is normal or not based on the magnetic field gradient low-frequency signal component sets L1, L2 and L3 and the magnetic field gradient high-frequency signals H1, H2 and H3.
Further, the step (1) includes:
(11) setting N1 first magnetic field sensors around a production line in a three-dimensional spiral mode with the extension direction of the production line where a circuit board to be detected is located as an axial direction and a thread pitch D1 and a cross section radius R1 as parameters, so as to form a first spiral detection structure, wherein N1 is a natural number greater than 5;
(12) before the circuit board to be detected contacts the cross section of the first spiral detection structure, an industrial robot holding electric signal input probe and an electric signal output probe are arranged, and a first signal input interface and a first signal output interface of a sub circuit where a capacitor of the circuit board to be detected is located are connected;
(13) inputting a voltage signal set Vi1 to the electric signal input probe, and recording an output signal set Vo1 detected by the electric signal output probe;
(14) and detecting signals of the N1 first magnetic field sensors during the circuit board to be detected passes through the first spiral detection structure, and decomposing to obtain a magnetic field gradient low-frequency signal component set L1 and a magnetic field gradient high-frequency signal component set H1.
Further, the step (2) includes:
(21) the extending direction of a production line where a circuit board to be detected is located is taken as an axial direction, N2 second magnetic field sensors are arranged around the production line in a three-dimensional spiral mode with a thread pitch D2 and a cross section radius R2 as parameters, so that a second spiral detection structure is formed, the sensitivity of the second magnetic field sensors is higher than that of the first magnetic field sensors, N2 is greater than N1, D2 is less than D1, R2 is less than R1, the second spiral detection structure is adjacent to the first spiral detection structure in the moving direction of the production line, the distance between the second spiral detection structure and the first spiral detection structure is less than 5cm, and N2 is a natural number greater than 10;
(22) stopping inputting the probe input signal set to the electric signal of the industrial robot before the circuit board to be detected contacts the cross section of the second spiral detection structure, and recording an output signal set Vo2 detected by the electric signal output probe during the circuit board to be detected passes through the second spiral detection structure;
(23) and detecting signals of the N2 second magnetic field sensors during the circuit board to be detected passes through the second spiral detection structure, and decomposing to obtain a magnetic field gradient low-frequency signal component set L2 and a magnetic field gradient high-frequency signal component set H2.
Further, the step (3) includes:
(31) setting N2 third magnetic field sensors around a production line in a three-dimensional spiral mode with the extension direction of the production line where a circuit board to be detected is located as an axial direction and with a thread pitch D2 and a cross section radius R2 as parameters so as to form a third spiral detection structure, setting N1 fourth magnetic field sensors around the production line in a three-dimensional spiral mode with the thread pitch D1 and the cross section radius R1 as parameters so as to form a fourth spiral detection structure, wherein the sensitivity of the fourth magnetic field sensors is higher than that of the third magnetic field sensors, the starting position of a first thread pitch of the fourth spiral detection structure is the same as the starting position of the first thread pitch of the third spiral detection structure in the moving direction of the circuit to be detected, and the length of the third spiral detection structure extending in the production line direction is the same as the length of the fourth spiral detection structure extending in the production line direction, and N2> N1, D2 ═ D1, R2< R1;
(32) before the circuit board to be detected contacts the cross section of the third spiral detection structure, an industrial robot holding electric signal input probe and an electric signal output probe are arranged, and a first signal input interface and a first signal output interface of a sub circuit where a capacitor of the circuit board to be detected is located are connected;
(33) inputting a voltage signal set Vi1 to the electric signal input probe, and recording an output signal set Vo3 detected by the electric signal output probe;
(34) and detecting the signal sets S3 output by the N2 third magnetic field sensors and the signal sets S4 output by the N1 fourth magnetic field sensors during the circuit board to be detected passes through the first spiral detection structure, and obtaining a magnetic field gradient low-frequency signal component set L3 and a magnetic field gradient high-frequency signal component set H3 according to S3 and S4.
Further, the step (34) comprises:
(341) calculating an accumulated signal set S1 of signals output from N2 third magnetic field sensors and N1 fourth magnetic field sensors, where i is 1, 2, …, N1:
(342) and decomposing the accumulated signal set S1 to obtain a magnetic field gradient low-frequency signal component set L3 and a magnetic field gradient high-frequency signal component set H3.
Further, the step (4) includes:
(41) for each element of the sets L1, L2, L3, H1, H2, H3, Vi1, Vo1, Vo2 and Vo3, eigenvalues Ei of the following matrix M are calculated, where i ═ 1, 2, …, (N1) -3,
a voltage effective value representing the electrical signal;
(42) forming a sequence by the characteristic values Ei, wherein the variance of the sequence is, the average value A1 is obtained by calculation after the maximum value of the sequence is removed, and the average value A2 is obtained by calculation after the minimum value of the sequence is removed;
(43) and calculating whether A1/A2 is larger than a preset threshold value or not, and if so, indicating that the size of stray capacitance formed by welding points caused by welding capacitance exceeds the preset value when the industrial robot is assembled on a production line.
Further, the first magnetic field sensor, the second magnetic field sensor, the third magnetic field sensor and the fourth magnetic field sensor are all high-sensitivity magnetometers and do not require that the batches and models are completely consistent.
The invention has the beneficial effects that: whether the size of the stray capacitance formed by the related welding points which just finish the capacitance welding exceeds the preset standard or not can be rapidly and qualitatively detected on a production line through lower cost, and the average accuracy reaches 77.9 percent after 500 times of tests. In addition, the qualitative judgment method does not need to have extra requirements on the precision and the sensitivity of the magnetic field sensors, does not need to have special requirements on batches of the magnetic field sensors, and can facilitate the maintenance and the replacement of the magnetic field sensors in the detection process on the premise of not influencing the detection result.
Drawings
Figure 1 shows a flow chart of the method of the present invention.
Detailed Description
As shown in fig. 1, according to a preferred embodiment of the present invention, there is provided an industrial robot assembly failure detection method including:
(1) carrying out magnetic field gradient detection on electronic equipment assembled with at least part of capacitors on an industrial robot production line in a first mode to obtain a magnetic field gradient low-frequency signal component set L1 and a magnetic field gradient high-frequency signal component set H1;
(2) detecting a magnetic field gradient in a second mode on the electronic equipment assembled with the capacitor on the industrial robot production line relative to a position on the industrial robot production line after the position detected in the first mode to obtain a magnetic field gradient low-frequency signal component set L2 and a magnetic field gradient high-frequency signal component set H2;
(3) detecting the magnetic field gradient of the electronic equipment assembled with the capacitor on the industrial robot production line in a third mode relative to the position on the industrial robot production line after the position detected in the second mode to obtain a magnetic field gradient low-frequency signal component set L3 and a magnetic field gradient high-frequency signal component set H3;
(4) and determining whether the state of the assembly capacitance of the industrial robot is normal or not based on the magnetic field gradient low-frequency signal component sets L1, L2 and L3 and the magnetic field gradient high-frequency signals H1, H2 and H3.
Preferably, the step (1) comprises:
(11) setting N1 first magnetic field sensors around a production line in a three-dimensional spiral mode with the extension direction of the production line where a circuit board to be detected is located as an axial direction and a thread pitch D1 and a cross section radius R1 as parameters, so as to form a first spiral detection structure, wherein N1 is a natural number greater than 5;
(12) before the circuit board to be detected contacts the cross section of the first spiral detection structure, an industrial robot holding electric signal input probe and an electric signal output probe are arranged, and a first signal input interface and a first signal output interface of a sub circuit where a capacitor of the circuit board to be detected is located are connected;
(13) inputting a voltage signal set Vi1 to the electric signal input probe, and recording an output signal set Vo1 detected by the electric signal output probe;
(14) and detecting signals of the N1 first magnetic field sensors during the circuit board to be detected passes through the first spiral detection structure, and decomposing to obtain a magnetic field gradient low-frequency signal component set L1 and a magnetic field gradient high-frequency signal component set H1.
Preferably, the step (2) includes:
(21) the extending direction of a production line where a circuit board to be detected is located is taken as an axial direction, N2 second magnetic field sensors are arranged around the production line in a three-dimensional spiral mode with a thread pitch D2 and a cross section radius R2 as parameters, so that a second spiral detection structure is formed, the sensitivity of the second magnetic field sensors is higher than that of the first magnetic field sensors, N2 is greater than N1, D2 is less than D1, R2 is less than R1, the second spiral detection structure is adjacent to the first spiral detection structure in the moving direction of the production line, the distance between the second spiral detection structure and the first spiral detection structure is less than 5cm, and N2 is a natural number greater than 10;
(22) stopping inputting the probe input signal set to the electric signal of the industrial robot before the circuit board to be detected contacts the cross section of the second spiral detection structure, and recording an output signal set Vo2 detected by the electric signal output probe during the circuit board to be detected passes through the second spiral detection structure;
(23) and detecting signals of the N2 second magnetic field sensors during the circuit board to be detected passes through the second spiral detection structure, and decomposing to obtain a magnetic field gradient low-frequency signal component set L2 and a magnetic field gradient high-frequency signal component set H2.
Preferably, the step (3) includes:
(31) setting N2 third magnetic field sensors around a production line in a three-dimensional spiral mode with the extension direction of the production line where a circuit board to be detected is located as an axial direction and with a thread pitch D2 and a cross section radius R2 as parameters so as to form a third spiral detection structure, setting N1 fourth magnetic field sensors around the production line in a three-dimensional spiral mode with the thread pitch D1 and the cross section radius R1 as parameters so as to form a fourth spiral detection structure, wherein the sensitivity of the fourth magnetic field sensors is higher than that of the third magnetic field sensors, the starting position of a first thread pitch of the fourth spiral detection structure is the same as the starting position of the first thread pitch of the third spiral detection structure in the moving direction of the circuit to be detected, and the length of the third spiral detection structure extending in the production line direction is the same as the length of the fourth spiral detection structure extending in the production line direction, and N2> N1, D2 ═ D1, R2< R1;
(32) before the circuit board to be detected contacts the cross section of the third spiral detection structure, an industrial robot holding electric signal input probe and an electric signal output probe are arranged, and a first signal input interface and a first signal output interface of a sub circuit where a capacitor of the circuit board to be detected is located are connected;
(33) inputting a voltage signal set Vi1 to the electric signal input probe, and recording an output signal set Vo3 detected by the electric signal output probe;
(34) and detecting the signal sets S3 output by the N2 third magnetic field sensors and the signal sets S4 output by the N1 fourth magnetic field sensors during the circuit board to be detected passes through the first spiral detection structure, and obtaining a magnetic field gradient low-frequency signal component set L3 and a magnetic field gradient high-frequency signal component set H3 according to S3 and S4.
Preferably, the step (34) comprises:
(341) calculating an accumulated signal set S1 of signals output from N2 third magnetic field sensors and N1 fourth magnetic field sensors, where i is 1, 2, …, N1:
(342) and decomposing the accumulated signal set S1 to obtain a magnetic field gradient low-frequency signal component set L3 and a magnetic field gradient high-frequency signal component set H3.
Preferably, the step (4) includes:
(41) for each element of the sets L1, L2, L3, H1, H2, H3, Vi1, Vo1, Vo2 and Vo3, eigenvalues Ei of the following matrix M are calculated, where i ═ 1, 2, …, (N1) -3,
a voltage effective value representing the electrical signal;
(42) forming a sequence by the characteristic values Ei, wherein the variance of the sequence is, the average value A1 is obtained by calculation after the maximum value of the sequence is removed, and the average value A2 is obtained by calculation after the minimum value of the sequence is removed;
(43) and calculating whether A1/A2 is larger than a preset threshold value or not, and if so, indicating that the size of stray capacitance formed by welding points caused by welding capacitance exceeds the preset value when the industrial robot is assembled on a production line.
Preferably, the first magnetic field sensor, the second magnetic field sensor, the third magnetic field sensor and the fourth magnetic field sensor are all high-sensitivity magnetometers and do not require that the batches and models are completely identical.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (1)
1. An industrial robot assembly fault detection method includes:
(1) carrying out magnetic field gradient detection on electronic equipment assembled with at least part of capacitors on an industrial robot production line in a first mode to obtain a magnetic field gradient low-frequency signal component set L1 and a magnetic field gradient high-frequency signal component set H1;
(2) detecting a magnetic field gradient in a second mode on the electronic equipment assembled with the capacitor on the industrial robot production line relative to a position on the industrial robot production line after the position detected in the first mode to obtain a magnetic field gradient low-frequency signal component set L2 and a magnetic field gradient high-frequency signal component set H2;
(3) detecting the magnetic field gradient of the electronic equipment assembled with the capacitor on the industrial robot production line in a third mode relative to the position on the industrial robot production line after the position detected in the second mode to obtain a magnetic field gradient low-frequency signal component set L3 and a magnetic field gradient high-frequency signal component set H3;
(4) determining whether the state of the assembly capacitance of the industrial robot is normal or not based on the magnetic field gradient low-frequency signal component sets L1, L2 and L3 and the magnetic field gradient high-frequency signals H1, H2 and H3;
it is characterized in that the preparation method is characterized in that,
the step (1) comprises the following steps:
(11) setting N1 first magnetic field sensors around a production line in a three-dimensional spiral mode with the extension direction of the production line where a circuit board to be detected is located as an axial direction and a thread pitch D1 and a cross section radius R1 as parameters, so as to form a first spiral detection structure, wherein N1 is a natural number greater than 5;
(12) before the circuit board to be detected contacts the cross section of the first spiral detection structure, an industrial robot holding electric signal input probe and an electric signal output probe are arranged, and a first signal input interface and a first signal output interface of a sub circuit where a capacitor of the circuit board to be detected is located are connected;
(13) inputting a voltage signal set Vi1 to the electric signal input probe, and recording an output signal set Vo1 detected by the electric signal output probe;
(14) detecting signals of the N1 first magnetic field sensors during the circuit board to be detected passes through the first spiral detection structure, and decomposing to obtain a magnetic field gradient low-frequency signal component set L1 and a magnetic field gradient high-frequency signal component set H1; the step (2) comprises the following steps:
(21) the extending direction of a production line where a circuit board to be detected is located is taken as an axial direction, N2 second magnetic field sensors are arranged around the production line in a three-dimensional spiral mode with a thread pitch D2 and a cross section radius R2 as parameters, so that a second spiral detection structure is formed, the sensitivity of the second magnetic field sensors is higher than that of the first magnetic field sensors, N2 is greater than N1, D2 is less than D1, R2 is less than R1, the second spiral detection structure is adjacent to the first spiral detection structure in the moving direction of the production line, the distance between the second spiral detection structure and the first spiral detection structure is less than 5cm, and N2 is a natural number greater than 10;
(22) stopping inputting the probe input signal set to the electric signal of the industrial robot before the circuit board to be detected contacts the cross section of the second spiral detection structure, and recording an output signal set Vo2 detected by the electric signal output probe during the circuit board to be detected passes through the second spiral detection structure;
(23) detecting signals of the N2 second magnetic field sensors during the circuit board to be detected passes through the second spiral detection structure, and decomposing the signals to obtain a magnetic field gradient low-frequency signal component set L2 and a magnetic field gradient high-frequency signal component set H2;
the step (3) comprises the following steps:
(31) setting N2 third magnetic field sensors around a production line in a three-dimensional spiral mode with the extension direction of the production line where a circuit board to be detected is located as an axial direction and with a thread pitch D2 and a cross section radius R2 as parameters so as to form a third spiral detection structure, setting N1 fourth magnetic field sensors around the production line in a three-dimensional spiral mode with the thread pitch D1 and the cross section radius R1 as parameters so as to form a fourth spiral detection structure, wherein the sensitivity of the fourth magnetic field sensors is higher than that of the third magnetic field sensors, the starting position of a first thread pitch of the fourth spiral detection structure is the same as the starting position of the first thread pitch of the third spiral detection structure in the moving direction of the circuit to be detected, and the length of the third spiral detection structure extending in the production line direction is the same as the length of the fourth spiral detection structure extending in the production line direction, and N2> N1, D2 ═ D1, R2< R1;
(32) before the circuit board to be detected contacts the cross section of the third spiral detection structure, an industrial robot holding electric signal input probe and an electric signal output probe are arranged, and a first signal input interface and a first signal output interface of a sub circuit where a capacitor of the circuit board to be detected is located are connected;
(33) inputting a voltage signal set Vi1 to the electric signal input probe, and recording an output signal set Vo3 detected by the electric signal output probe;
(34) detecting a signal set S3 output by the N2 third magnetic field sensors and a signal set S4 output by the N1 fourth magnetic field sensors during the circuit board to be detected passes through the first spiral detection structure, and obtaining a magnetic field gradient low-frequency signal component set L3 and a magnetic field gradient high-frequency signal component set H3 according to S3 and S4;
said step (34) comprises:
(341) calculating an accumulated signal set S1 of signals output from N2 third magnetic field sensors and N1 fourth magnetic field sensors, where i is 1, 2, …, N1:
(342) decomposing the accumulated signal set S1 to obtain a magnetic field gradient low-frequency signal component set L3 and a magnetic field gradient high-frequency signal component set H3;
the step (4) comprises the following steps:
(41) for each element of the sets L1, L2, L3, H1, H2, H3, Vi1, Vo1, Vo2 and Vo3, eigenvalues Ei of the following matrix M are calculated, where i ═ 1, 2, …, (N1) -3,
a voltage effective value representing the electrical signal;
(42) forming a sequence by the characteristic values Ei, wherein the variance of the sequence is, the average value A1 is obtained by calculation after the maximum value of the sequence is removed, and the average value A2 is obtained by calculation after the minimum value of the sequence is removed;
(43) calculating whether A1/A2 is larger than a preset threshold value or not, and if so, indicating that the size of stray capacitance formed by welding points caused by welding capacitance exceeds a preset value when the industrial robot is assembled on a production line;
the first magnetic field sensor, the second magnetic field sensor, the third magnetic field sensor and the fourth magnetic field sensor are all high-sensitivity magnetometers and do not require the batch and model to be completely consistent.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810904456.9A CN109062158B (en) | 2018-08-09 | 2018-08-09 | Industrial robot assembly fault detection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810904456.9A CN109062158B (en) | 2018-08-09 | 2018-08-09 | Industrial robot assembly fault detection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109062158A CN109062158A (en) | 2018-12-21 |
| CN109062158B true CN109062158B (en) | 2021-01-05 |
Family
ID=64683184
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810904456.9A Active CN109062158B (en) | 2018-08-09 | 2018-08-09 | Industrial robot assembly fault detection method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109062158B (en) |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100541221C (en) * | 2007-07-17 | 2009-09-16 | 蹇兴亮 | Low-intensity magnetic field gradient measuring device and method |
| WO2011004536A1 (en) * | 2009-07-10 | 2011-01-13 | 日本電気株式会社 | Electromagnetic field measuring device, electromagnetic field measuring method used for the measuring device, and non-transitory computer-readable medium in which electromagnetic field measurement control program is stored |
| JP6185472B2 (en) * | 2011-09-09 | 2017-08-23 | ワイトリシティ コーポレーションＷｉｔｒｉｃｉｔｙ Ｃｏｒｐｏｒａｔｉｏｎ | Foreign object detection in wireless energy transmission systems |
| JP6465348B2 (en) * | 2015-02-19 | 2019-02-06 | 国立大学法人九州工業大学 | Method and apparatus for detecting bonding wire current magnetic field distribution of power semiconductor device |
| CN106125024B (en) * | 2016-08-19 | 2019-11-05 | 深圳市正祥医疗科技有限公司 | MRI system and its gradient amplifier driving circuit, test macro |
-
2018
- 2018-08-09 CN CN201810904456.9A patent/CN109062158B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN109062158A (en) | 2018-12-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109164381B (en) | Method and device for online monitoring mechanical state and fault identification of high-voltage circuit breaker | |
| JP6348137B2 (en) | Machining machine system for judging the quality of workpieces | |
| CN106844775B (en) | Spacecraft fault rapid detection system | |
| CN104390657B (en) | A kind of Generator Unit Operating Parameters measurement sensor fault diagnosis method and system | |
| US6694286B2 (en) | Method and system for monitoring the condition of an individual machine | |
| CN106500900A (en) | A kind of Cable force measuring device and its measuring method | |
| CN106197558B (en) | A kind of valve state on-line monitoring system and its working method | |
| CN108695569B (en) | Current sensor calibration device | |
| CN111025988A (en) | Intelligent motor control device and control method based on multi-physical-field detection | |
| CN108363005A (en) | Motor reliability test method | |
| DE102018201975A1 (en) | DIAGNOSTIC DATA COLLECTION SYSTEM, DIAGNOSTIC SYSTEM AND DIAGNOSTIC DATA COLLECTION PROGRAM | |
| CN109933020A (en) | Abnormal omen notice system, abnormal omen notification method and program | |
| CN103063333B (en) | Airplane frame part assembly stress detection and control method | |
| CN109062158B (en) | Industrial robot assembly fault detection method | |
| CN109062159B (en) | Unmanned monitoring system for television assembly line | |
| CN109062157B (en) | Intelligent control system for assembly line of unmanned television set | |
| CN106123939A (en) | A kind of magnetic suspension sensor rigidity testing system and method for testing | |
| CN109060378A (en) | A kind of automatization test system rigidly tested for auto parts and components and its test method | |
| CN112798143A (en) | Molding frame state monitoring method based on integrated optical fiber sensor | |
| CN109341504B (en) | Method for automatically calibrating position coordinates of platform | |
| EP3224614B1 (en) | Measuring and sampling lance and method | |
| CN113258849B (en) | Intelligent identification method for control parameters of permanent magnet synchronous motor | |
| CN109932640A (en) | A kind of high-precision FPGA solder joint failure real-time diagnosis method and diagnostic device | |
| CN115308664A (en) | Calibration device and method of Hall current sensor | |
| KR200449437Y1 (en) | Performace test system for control rod coils |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |