CN110636256A - Industrial fault diagnosis method, device, equipment and storage medium based on AR - Google Patents
Industrial fault diagnosis method, device, equipment and storage medium based on AR Download PDFInfo
- Publication number
- CN110636256A CN110636256A CN201910794801.2A CN201910794801A CN110636256A CN 110636256 A CN110636256 A CN 110636256A CN 201910794801 A CN201910794801 A CN 201910794801A CN 110636256 A CN110636256 A CN 110636256A
- Authority
- CN
- China
- Prior art keywords
- equipment
- fault
- diagnosis
- diagnosis method
- information
- 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.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
- G06V20/63—Scene text, e.g. street names
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application discloses an industrial fault diagnosis method, device, equipment and storage medium based on AR, wherein the method acquires an equipment label image in real time and extracts equipment ID information from the equipment label image; acquiring and displaying all preset fault phenomena corresponding to the equipment ID information from a diagnosis method library, and receiving a current fault phenomenon selected from all preset fault phenomena; inquiring and displaying a diagnosis method corresponding to the current fault phenomenon in a diagnosis method library, accessing a diagnosis device corresponding to the diagnosis method, acquiring and displaying an equipment model corresponding to the equipment ID information from an equipment model library, and sequentially acquiring measurement data of each preset measurement position on the equipment model until abnormal measurement data occurs; and determining fault information according to the abnormal measurement data, and displaying a removing method corresponding to the fault information. The method can be combined with equipment description, the description mode is visual, and an operator can quickly determine and remove fault information according to the fault phenomenon.
Description
Technical Field
The invention relates to the technical field of augmented reality and intelligence, in particular to an industrial fault diagnosis method, device, equipment and storage medium based on AR.
Background
In an industrial fault diagnosis scene, the prior art generally provides a maintenance manual for equipment manufacturers, the maintenance manual records maintenance modes corresponding to common fault phenomena of equipment, the content of the maintenance manual is few, hundreds of pages and thousands of pages according to the complexity of the equipment and the diversity of equipment functions, most maintenance personnel of an equipment user do not know the structure of the equipment, and the equipment is maintained by looking up the maintenance manual after the equipment fails; however, for large-scale equipment, the content of a maintenance manual is various, and maintenance personnel cannot quickly find out the fault reason and the fault elimination method according to the fault phenomenon; in addition, the maintenance manual cannot be explained by combining equipment, the explanation mode is not intuitive enough, and the maintenance personnel cannot conveniently position the fault in time.
At present, the application of the AR technology in the industry is still more basic, and as a conventional application, in device management, an AR tag (as a device tag) is usually set on the surface of a device, and then the AR tag is acquired and identified by a camera of an AR system, and a stored AR model (usually, a three-dimensional model of the device corresponding to the AR tag) is displayed by a display device. The function is single, and the practical significance to the equipment management and maintenance work is limited.
Disclosure of Invention
In view of the above-mentioned drawbacks or deficiencies in the prior art, it is desirable to provide an AR-based industrial fault diagnosis method, apparatus, device, and storage medium.
In order to overcome the defects of the prior art, the technical scheme provided by the invention is as follows:
in a first aspect, the present invention provides an AR-based industrial fault diagnosis method, which is characterized in that the method comprises:
acquiring an equipment label image in real time, and extracting equipment ID information from the equipment label image;
acquiring and displaying all preset fault phenomena corresponding to the equipment ID information from a diagnosis method library, and receiving a current fault phenomenon selected from all preset fault phenomena;
inquiring a diagnosis method corresponding to the current fault phenomenon in a diagnosis method library, displaying the diagnosis method, accessing a diagnosis device corresponding to the diagnosis method,
acquiring an equipment model corresponding to the equipment ID information from an equipment model library, displaying the equipment model, and sequentially acquiring measurement data of each preset measurement position on the equipment model until abnormal measurement data occurs;
and determining fault information according to the abnormal measurement data, and displaying a removing method corresponding to the fault information.
Further, displaying the device model includes:
and extracting device coordinates from the device label image, adjusting the angle and the size of the device model by taking the device coordinates as a reference so as to enable the device model to be overlapped with the entity of the device, and displaying the device model in a semi-transparent mode.
Further, obtaining measurement data of a preset measurement position on the device model includes:
and inquiring a preset measuring position in the diagnosis method library, displaying the measuring position on the equipment model, and acquiring the measuring data of the measuring position.
Further, the device model library includes a component library, and the component library stores structural information of components, parts, and standard components of an assembly body constituting the device, and position information of the components, parts, and standard components in the assembly body of the device.
Further, the diagnostic method library includes a disassembly method library, the disassembly method library records a disassembly method for removing a certain component or part from an assembly of the equipment, and the disassembly method and the diagnostic method have a correlation relationship, where the correlation relationship is: and when the prompt of the diagnosis method is received, acquiring the corresponding disassembly method from the disassembly method library and displaying the corresponding disassembly method.
Further, the diagnosis method includes an automatic diagnosis method which is a detection method for performing a comprehensive inspection on the equipment and a phenomenon diagnosis method which is a rapid failure diagnosis and failure removal after a failure of the equipment.
Further, the device is provided with a plurality of device tags with different coordinate information in a plurality of directions.
Further, the diagnostic device comprises a circuit diagnostic device and a temperature diagnostic device, wherein the circuit diagnostic device is used for testing voltage and current, and the temperature diagnostic device is used for testing temperature.
In a second aspect, the present invention provides an AR-based industrial fault diagnosis apparatus, including:
the image acquisition and processing module is used for acquiring an equipment label image in real time and extracting equipment ID information from the equipment label image;
the fault phenomenon acquisition module is used for acquiring and displaying all preset fault phenomena corresponding to the equipment ID information from the diagnosis method library and receiving the current fault phenomenon selected from all the fault phenomena;
a fault diagnosis module for searching the diagnosis method corresponding to the current fault in a diagnosis method library, displaying the diagnosis method, accessing a diagnosis device corresponding to the diagnosis method,
the fault information determining module is used for acquiring an equipment model corresponding to the equipment ID information from an equipment model library, displaying the equipment model, and sequentially acquiring the measurement data of each preset measurement position on the equipment model until abnormal measurement data occurs;
and the fault information elimination module is used for determining fault information according to the abnormal measurement data and displaying an elimination method corresponding to the fault information.
In a third aspect, the invention provides a computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the method described above when executing the computer program.
In a fourth aspect, the invention provides a computer-readable storage medium, on which a computer program is stored, which is characterized in that the computer program realizes the above-mentioned method when being executed by a processor.
Compared with the prior art, the invention has the beneficial effects that:
the industrial fault diagnosis method based on AR of the invention obtains the equipment label image in real time, extracts the equipment ID information from the equipment label image; acquiring and displaying all preset fault phenomena corresponding to the equipment ID information from a diagnosis method library, and receiving a current fault phenomenon selected from all preset fault phenomena; inquiring and displaying a diagnosis method corresponding to the current fault phenomenon in a diagnosis method library, accessing a diagnosis device corresponding to the diagnosis method, acquiring and displaying an equipment model corresponding to the equipment ID information from an equipment model library, and sequentially acquiring measurement data of each preset measurement position on the equipment model until abnormal measurement data occurs; and determining fault information according to the abnormal measurement data, and displaying a removing method corresponding to the fault information. The method can be described by combining the equipment, the description mode is visual, the operator can quickly determine and remove the fault information according to the fault phenomenon, the normal work of the equipment is recovered, and the method is simple and convenient.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention to its proper form. It is obvious that the drawings in the following description are only some embodiments, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
FIG. 1 is a schematic flow diagram of an AR-based industrial fault diagnosis method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an AR-based industrial fault diagnosis apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
It should be noted that the drawings and the description are not intended to limit the scope of the inventive concept in any way, but to illustrate it by a person skilled in the art with reference to specific embodiments.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
As mentioned in the background art, in an industrial fault diagnosis scenario, in the prior art, a maintenance manual is usually provided for an equipment manufacturer, and the maintenance manual records an overhaul mode corresponding to a common fault phenomenon of equipment, and according to the complexity of the equipment and the diversity of functions of the equipment, the content of the maintenance manual is few hundreds of pages, and many thousands of pages, most of maintainers of an equipment user do not know the structure of the equipment well, and the equipment is overhauled by looking up the maintenance manual after the equipment fails; however, for large-scale equipment, the content of a maintenance manual is various, and maintenance personnel cannot quickly find out the fault reason and the fault elimination method according to the fault phenomenon; in addition, the maintenance manual cannot be explained by combining equipment, the explanation mode is not intuitive enough, and the maintenance personnel cannot conveniently position the fault in time.
Therefore, the problem to be solved by the fault reason and the fault elimination method application is quickly found according to the fault phenomenon, and how to provide a maintenance manual which can be intuitively explained in combination with equipment is an improvement direction of the application. The embodiment of the application provides an industrial fault diagnosis method, device and equipment based on AR and a storage medium.
Referring to fig. 1, an exemplary flowchart of an AR-based industrial fault diagnosis method according to an embodiment of the present application is shown.
In step 110, acquiring a device tag image in real time, and extracting device ID information from the device tag image;
in step 120, all preset fault phenomena corresponding to the device ID information are acquired and displayed from the diagnostic method library, and a current fault phenomenon selected from all preset fault phenomena is received;
in step 130, a diagnosis method corresponding to the current fault phenomenon is inquired in a diagnosis method library, the diagnosis method is displayed, a diagnosis device corresponding to the diagnosis method is accessed,
in step 140, acquiring an equipment model corresponding to the equipment ID information from an equipment model library, displaying the equipment model, and sequentially acquiring measurement data of each preset measurement position on the equipment model until abnormal measurement data occurs;
in step 150, failure information is determined from the abnormal measurement data, and a method of eliminating the failure information is displayed.
It should be noted that, the obtaining of the measurement data of the preset measurement position on the device model includes: and inquiring a preset measuring position in the diagnosis method library, displaying the measuring position on the equipment model, and acquiring the measuring data of the measuring position. The acquiring of the measurement data of the measurement position specifically includes: the operator reads the measurement data at the measurement location using the corresponding diagnostic device and transmits the measurement data. The diagnosis method includes an automatic diagnosis method which is a detection method for performing a comprehensive inspection on the equipment and a phenomenon diagnosis method which is a rapid failure diagnosis and failure removal after a failure of the equipment, and the phenomenon diagnosis method is used herein.
Preferably, a plurality of device tags with different coordinate information are arranged in a plurality of directions of the device, so that the device model is prevented from being lost due to the fact that the device tags exceed the identification range. The diagnostic device comprises a circuit diagnostic device and a temperature diagnostic device, wherein the circuit diagnostic device is used for testing voltage and current, and the temperature diagnostic device is used for testing temperature.
On the basis of the above embodiment, displaying the device model includes:
and extracting device coordinates from the device label image, adjusting the angle and the size of the device model by taking the device coordinates as a reference so as to enable the device model to be overlapped with the entity of the device, and displaying the device model in a semi-transparent mode. The device label image displays a real image of the device, and the measurement position is displayed as a real position on the device in the diagnosis process, so that an operator can find the measurement position more quickly and intuitively, and the diagnosis efficiency is improved.
On the basis of the above embodiment, the device model library includes a component library that stores structural information of components, parts, and standards constituting an assembly of the device, and positional information of the components, parts, and standards in the assembly of the device.
On the basis of the above embodiment, the diagnostic method library includes a disassembly method library, the disassembly method library records a disassembly method for removing a certain component or part from an assembly body of the equipment, and there is a correlation relationship between the disassembly method and the diagnostic method, where the correlation relationship is: and when the prompt of the diagnosis method is received, acquiring the corresponding disassembly method from the disassembly method library and displaying the corresponding disassembly method. For example: the control circuit board of the equipment needs to be independently checked, and the diagnosis method prompts the method for disassembling the control circuit board from the equipment, so that the requirement on the skill of maintenance personnel is reduced, and the condition that the equipment is damaged by operation errors of operators is prevented.
Referring to fig. 2, a schematic structural diagram of an AR-based industrial fault diagnosis apparatus 200 according to an embodiment of the present invention is shown. As shown in fig. 2, the apparatus may implement the method shown in fig. 1, and the apparatus may include:
an image obtaining and processing module 210, configured to obtain an apparatus tag image in real time, and extract apparatus ID information from the apparatus tag image;
a failure phenomenon obtaining module 220, configured to obtain and display all preset failure phenomena corresponding to the device ID information from the diagnosis method library, and receive a current failure phenomenon selected from all the failure phenomena;
a fault diagnosis module 230 for searching the diagnosis method corresponding to the current fault in the diagnosis method library, displaying the diagnosis method, accessing the diagnosis device corresponding to the diagnosis method,
a fault information determining module 240, configured to obtain an equipment model corresponding to the equipment ID information from an equipment model library, display the equipment model, and sequentially obtain measurement data of each preset measurement position on the equipment model until abnormal measurement data occurs;
and a fault information elimination module 250, configured to determine fault information according to the abnormal measurement data, and display an elimination method corresponding to the fault information.
It should be noted that the image acquisition and processing module, the fault phenomenon acquisition module, the fault phenomenon diagnosis module, the fault information determination module and the fault information elimination module may be disposed on the same handheld terminal, and the intelligent terminal may be a smart phone, a tablet computer or a PDA.
The invention is further described below with reference to a specific application scenario.
Taking an industrial robot as an example, when the industrial robot runs normally, the industrial robot is comprehensively checked through an automatic diagnosis method and used for daily inspection of equipment. When the industrial robot has a shutdown fault, the equipment label on the surface of the industrial robot is scanned through the handheld terminal, the models of the industrial robot and the industrial robot are displayed in an augmented reality mode, and an operator selects a phenomenon diagnosis method and selects a fault phenomenon at the interactive terminal. In the diagnosis method library, a power supply is detected firstly, an interactive terminal prompts a connection circuit diagnosis device to detect the power supply voltage, the measurement position is a power supply interface, the power supply interface position is highlighted, the circuit diagnosis device is connected with the industrial fault diagnosis device, the circuit diagnosis device comprises two test pens, the measurement position is measured by the test pens, the circuit detection device sends power supply voltage information to the industrial fault diagnosis device after obtaining the power supply voltage information, and the power supply voltage is judged to be normal through the normal power supply voltage range information in the diagnosis method library. The second step of the diagnosis method corresponding to the shutdown fault detects the control circuit, detects the signal lamp of the control circuit board, inquires the method for disassembling the control circuit board in the disassembling method library, an operator unloads the control circuit board and then scans the equipment label on the control circuit board, the interactive device displays the model of the control circuit board in an augmented reality mode, the control circuit board is diagnosed to be damaged and needs to be replaced by a diagnosis means, and the fault diagnosis is completed.
Fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention. As shown in fig. 3, a schematic structural diagram of a computer system 300 suitable for implementing a terminal device or a server of the embodiment of the present application is shown.
As shown in fig. 3, the computer system 300 includes a Central Processing Unit (CPU)301 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)302 or a program loaded from a storage section 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the system 300 are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 303 is also connected to bus 304.
The following components are connected to the I/O interface 305: an input portion 309 including a keyboard, a mouse, and the like; an output section 307 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. The driver 310 is also connected to the I/O interface 309 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 310 as necessary, so that a computer program read out therefrom is mounted into the storage section 308 as necessary.
In particular, the process described above with reference to fig. 3 may be implemented as a computer software program, according to an embodiment of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising program code for performing the above-described method of stand allocation for a plurality of aircraft. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 309, and/or installed from the removable medium 311.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present application may be implemented by software or hardware. The described units or modules may also be provided in a processor. The names of these units or modules do not in some cases constitute a limitation of the unit or module itself.
As another aspect, the present application also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the foregoing device in the foregoing embodiment; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the method for stand allocation for a plurality of aircraft described herein.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. An AR-based industrial fault diagnosis method, characterized in that the method comprises:
acquiring an equipment label image in real time, and extracting equipment ID information from the equipment label image;
acquiring and displaying all preset fault phenomena corresponding to the equipment ID information from a diagnosis method library, and receiving a current fault phenomenon selected from all preset fault phenomena;
inquiring a diagnosis method corresponding to the current fault phenomenon in a diagnosis method library, displaying the diagnosis method, accessing a diagnosis device corresponding to the diagnosis method,
acquiring an equipment model corresponding to the equipment ID information from an equipment model library, displaying the equipment model, and sequentially acquiring measurement data of each preset measurement position on the equipment model until abnormal measurement data occurs;
and determining fault information according to the abnormal measurement data, and displaying a removing method corresponding to the fault information.
2. The AR-based industrial fault diagnosis method according to claim 1, wherein displaying the device model comprises:
and extracting device coordinates from the device label image, adjusting the angle and the size of the device model by taking the device coordinates as a reference so as to enable the device model to be overlapped with the entity of the device, and displaying the device model in a semi-transparent mode.
3. The AR-based industrial fault diagnosis method according to claim 1 or 2, wherein the device model library includes a component library that stores structural information of components, parts, standards that constitute an assembly of the device, and positional information of the components, parts, standards in the assembly of the device.
4. The AR-based industrial fault diagnosis method according to claim 3, wherein the library of diagnosis methods comprises a disassembly method library, the disassembly method library records a disassembly method for removing a certain component or part from an assembly of the equipment, and the disassembly method and the diagnosis method have a correlation relationship, the correlation relationship is: and when the prompt of the diagnosis method is received, acquiring the corresponding disassembly method from the disassembly method library and displaying the corresponding disassembly method.
5. The AR-based industrial fault diagnosis method according to claim 1, wherein the diagnosis method includes an automatic diagnosis method which is a detection method for performing a comprehensive inspection of the equipment and a phenomenon diagnosis method which is a rapid fault diagnosis and troubleshooting after the equipment is out of order.
6. The AR-based industrial fault diagnosis method according to claim 1, wherein the device has a plurality of device tags whose direction setting coordinate information is different.
7. The AR-based industrial fault diagnosis method according to claim 1, wherein the diagnosis means includes a circuit diagnosis means for testing voltage and current and a temperature diagnosis means for testing temperature.
8. An AR-based industrial fault diagnosis apparatus, comprising:
the image acquisition and processing module is used for acquiring an equipment label image in real time and extracting equipment ID information from the equipment label image;
the fault phenomenon acquisition module is used for acquiring and displaying all preset fault phenomena corresponding to the equipment ID information from the diagnosis method library and receiving the current fault phenomenon selected from all the fault phenomena;
a fault diagnosis module for searching the diagnosis method corresponding to the current fault in a diagnosis method library, displaying the diagnosis method, accessing a diagnosis device corresponding to the diagnosis method,
the fault information determining module is used for acquiring an equipment model corresponding to the equipment ID information from an equipment model library, displaying the equipment model, and sequentially acquiring the measurement data of each preset measurement position on the equipment model until abnormal measurement data occurs;
and the fault information elimination module is used for determining fault information according to the abnormal measurement data and displaying an elimination method corresponding to the fault information.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor when executing the computer program implements the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910794801.2A CN110636256A (en) | 2019-08-27 | 2019-08-27 | Industrial fault diagnosis method, device, equipment and storage medium based on AR |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910794801.2A CN110636256A (en) | 2019-08-27 | 2019-08-27 | Industrial fault diagnosis method, device, equipment and storage medium based on AR |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110636256A true CN110636256A (en) | 2019-12-31 |
Family
ID=68969125
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910794801.2A Pending CN110636256A (en) | 2019-08-27 | 2019-08-27 | Industrial fault diagnosis method, device, equipment and storage medium based on AR |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110636256A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113467742A (en) * | 2021-07-28 | 2021-10-01 | 中国电信股份有限公司 | Visual obstacle removing method and system |
| WO2024008130A1 (en) * | 2022-07-07 | 2024-01-11 | 阿里云计算有限公司 | Faulty hardware processing method, apparatus and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106230628A (en) * | 2016-07-29 | 2016-12-14 | 山东工商学院 | A kind of equipment auxiliary repair method and system |
| CN107277451A (en) * | 2017-07-14 | 2017-10-20 | 福建铁工机智能机器人有限公司 | A kind of utilization AR realizes the method and apparatus of remote guide scene investigation failure |
| CN108134710A (en) * | 2017-11-09 | 2018-06-08 | 珠海格力电器股份有限公司 | Equipment fault display method and device, storage medium, equipment and server |
| CN108492378A (en) * | 2018-03-15 | 2018-09-04 | 南京智格电力科技有限公司 | A kind of troubleshooting methodology based on AR image enhancement techniques |
-
2019
- 2019-08-27 CN CN201910794801.2A patent/CN110636256A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106230628A (en) * | 2016-07-29 | 2016-12-14 | 山东工商学院 | A kind of equipment auxiliary repair method and system |
| CN107277451A (en) * | 2017-07-14 | 2017-10-20 | 福建铁工机智能机器人有限公司 | A kind of utilization AR realizes the method and apparatus of remote guide scene investigation failure |
| CN108134710A (en) * | 2017-11-09 | 2018-06-08 | 珠海格力电器股份有限公司 | Equipment fault display method and device, storage medium, equipment and server |
| CN108492378A (en) * | 2018-03-15 | 2018-09-04 | 南京智格电力科技有限公司 | A kind of troubleshooting methodology based on AR image enhancement techniques |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113467742A (en) * | 2021-07-28 | 2021-10-01 | 中国电信股份有限公司 | Visual obstacle removing method and system |
| WO2024008130A1 (en) * | 2022-07-07 | 2024-01-11 | 阿里云计算有限公司 | Faulty hardware processing method, apparatus and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108683562B (en) | Anomaly detection positioning method and device, computer equipment and storage medium | |
| US11587219B2 (en) | Method and apparatus for detecting pixel defect of optical module, and device | |
| CN110865917A (en) | AR technology-based electric power machine room inspection operation method, system and application | |
| US8345950B2 (en) | System and method for testing a multimeter | |
| CN107907775B (en) | Wiring detection method and wiring detection device | |
| KR20220041212A (en) | PCB maintenance system and maintenance method based on false point defect detection | |
| CN109725247B (en) | Circuit board remote maintenance guarantee method and system | |
| CN111292327B (en) | Machine room inspection method, device, equipment and storage medium | |
| CN110636256A (en) | Industrial fault diagnosis method, device, equipment and storage medium based on AR | |
| CN102789405A (en) | Automated testing method and system for mainboard | |
| CN111208140A (en) | Defect rechecking system and method | |
| CN111693089A (en) | Product quality control method, device, equipment and storage medium for assembly line | |
| CN103970628A (en) | Automatic detection system and automatic detection method thereof | |
| CN115937101A (en) | Quality detection method, device, equipment and storage medium | |
| CN113496661A (en) | LED display control system detection method, device and system | |
| CN104390982A (en) | Test method for SMT first article inspection | |
| CN113920057A (en) | Method, device and system for identifying color of product indicator light | |
| CN110962128B (en) | Substation inspection and stationing method and inspection robot control method | |
| CN109285138B (en) | Distributed processing system and method for machine vision analysis | |
| CN112330583A (en) | Product defect detection method, device, equipment and storage medium | |
| CN115686969A (en) | Hard disk backboard testing method, device, equipment and medium | |
| CN115327480A (en) | Sound source positioning method and system | |
| CN103824143A (en) | Component assembly indicating system and method | |
| CN112181744A (en) | Fault detection method, system, terminal and storage medium of converter interface | |
| CN107203449B (en) | Device for intelligently displaying test state of rack |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191231 |
|
| RJ01 | Rejection of invention patent application after publication |