CN113568815A - Industrial manufacturing equipment fault diagnosis method and device based on big data and electronic equipment - Google Patents
Industrial manufacturing equipment fault diagnosis method and device based on big data and electronic equipment Download PDFInfo
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- CN113568815A CN113568815A CN202110927887.9A CN202110927887A CN113568815A CN 113568815 A CN113568815 A CN 113568815A CN 202110927887 A CN202110927887 A CN 202110927887A CN 113568815 A CN113568815 A CN 113568815A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3051—Monitoring arrangements for monitoring the configuration of the computing system or of the computing system component, e.g. monitoring the presence of processing resources, peripherals, I/O links, software programs
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
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- G06F11/327—Alarm or error message display
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Abstract
The invention provides a fault diagnosis method and device for industrial manufacturing equipment based on big data and electronic equipment, which are used for acquiring first operating condition information and second operating condition information in the operating process of the industrial manufacturing equipment, acquiring operating noise information of the first operating condition information and the second operating condition information, converting the operating noise information into a target operating fault event, and determining operating fault data during the operating process of the industrial manufacturing equipment in the operating time period between the first operating condition information and the second operating condition information according to signal indication information of a fault occurrence reason in the target operating fault event. In this way, the accuracy of the diagnosis result can be ensured.
Description
Technical Field
The invention relates to the technical field of big data and equipment fault diagnosis, in particular to a big data-based industrial manufacturing equipment fault diagnosis method and device and electronic equipment.
Background
At present, most of the existing equipment fault diagnosis methods are manual diagnosis methods. However, the manual diagnosis method only performs fault diagnosis by workers according to work experience, so that it is difficult to ensure that the diagnosis result is inaccurate.
Disclosure of Invention
In order to solve the problems, the invention provides a fault diagnosis method and device for industrial manufacturing equipment based on big data and electronic equipment.
A big-data based industrial manufacturing equipment fault diagnosis method, the method comprising:
acquiring first operating condition information and second operating condition information in the operating process of industrial manufacturing equipment, wherein the first operating condition information and the second operating condition information are operating condition information corresponding to a preset time period in the operating process of the industrial manufacturing equipment;
acquiring operation noise information of the first operation condition information and the second operation condition information, wherein the operation noise information represents an operation noise analysis result between corresponding failure occurrence reasons between the first operation condition information and the second operation condition information;
converting the operating noise information into a target operating fault event, wherein the target operating fault event comprises a plurality of fault occurrence reasons;
and determining the operation fault data of the industrial manufacturing equipment during operation in the operation time interval from the first operation condition information to the second operation condition information according to the signal indication information of the fault occurrence reason in the target operation fault event.
Further, the acquiring the operation noise information of the first operation condition information and the second operation condition information includes: determining first operation audio signal information corresponding to the first operation condition information and second operation audio signal information corresponding to the second operation condition information; and comparing the first operation audio signal information with the second operation audio signal information to obtain the operation noise information, wherein the operation noise information is expressed in a streaming data form, and the noise source position information comprises client preference information corresponding to operation noise analysis results among failure occurrence reasons.
Further, said converting said operational noise information into a target operational failure event comprises: acquiring noise fluctuation information in noise source position information corresponding to the operation noise information; and converting the noise fluctuation information into signal indication information to obtain the target operation fault event, wherein the target operation fault event is expressed in a signal indication information matrix form.
Further, the determining, according to the signal indication information of the cause of the occurrence of the fault in the target operation fault event, the operation fault data of the industrial manufacturing equipment during operation in the operation period from the first operation condition information to the second operation condition information includes: determining from the target operational fault event that a maximum signal indicative of a cause of the occurrence of the fault is time consuming; and responding to the maximum signal indication time consumption matching signal indication information requirement, and determining operation fault data of the industrial manufacturing equipment during operation in the operation time period from the first operation condition information to the second operation condition information.
A big data based industrial manufacturing equipment fault diagnosis apparatus, the apparatus comprising:
the system comprises an operation condition information acquisition module, a data processing module and a data processing module, wherein the operation condition information acquisition module is used for acquiring first operation condition information and second operation condition information in the operation process of the industrial manufacturing equipment, and the first operation condition information and the second operation condition information are operation condition information corresponding to a preset time period in the operation process of the industrial manufacturing equipment;
the operation noise information determination module is used for acquiring operation noise information of the first operation condition information and the second operation condition information, and the operation noise information represents an operation noise analysis result between corresponding failure occurrence reasons between the first operation condition information and the second operation condition information;
an operation fault event determination module, configured to convert the operation noise information into a target operation fault event, where the target operation fault event includes multiple fault occurrence reasons;
and the operation fault data acquisition module is used for determining operation fault data of the industrial manufacturing equipment during operation in the operation time period from the first operation working condition information to the second operation working condition information according to the signal indication information of the fault occurrence reason in the target operation fault event.
By applying the method and the device, first operation condition information and second operation condition information in the operation process of the industrial manufacturing equipment are obtained, operation noise information of the first operation condition information and the second operation condition information is obtained, the operation noise information is converted into a target operation fault event, and operation fault data during the operation of the industrial manufacturing equipment in the operation time interval between the first operation condition information and the second operation condition information is determined according to signal indication information of a fault occurrence reason in the target operation fault event. In this way, the accuracy of the diagnosis result can be ensured.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a method for diagnosing a fault of an industrial manufacturing device based on big data according to an embodiment of the present invention.
Fig. 2 is a functional block diagram of a fault diagnosis apparatus for industrial manufacturing equipment based on big data according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.
Referring to fig. 1, a big data based fault diagnosis method for an industrial manufacturing device is shown, which is applied to an electronic device and includes the following steps S110 to S140.
Step S110, acquiring first operating condition information and second operating condition information in the operating process of the industrial manufacturing equipment, wherein the first operating condition information and the second operating condition information are operating condition information corresponding to a preset time period in the operating process of the industrial manufacturing equipment.
Step S120, obtaining operation noise information of the first operation condition information and the second operation condition information, wherein the operation noise information represents an operation noise analysis result between corresponding failure occurrence reasons between the first operation condition information and the second operation condition information.
Step S130, converting the operation noise information into a target operation fault event, wherein the target operation fault event comprises a plurality of fault occurrence reasons.
Step S140, determining operation fault data of the industrial manufacturing equipment during operation in the operation time period between the first operation condition information and the second operation condition information according to the signal indication information of the fault occurrence reason in the target operation fault event.
By executing the steps S110 to S140, acquiring first operating condition information and second operating condition information during the operating process of the industrial manufacturing equipment, acquiring operating noise information of the first operating condition information and the second operating condition information, converting the operating noise information into a target operating fault event, and determining operating fault data during the operating process of the industrial manufacturing equipment in an operating period between the first operating condition information and the second operating condition information according to signal indication information of a fault occurrence reason in the target operating fault event. In this way, the accuracy of the diagnosis result can be ensured.
Further, the acquiring the operation noise information of the first operation condition information and the second operation condition information includes: determining first operation audio signal information corresponding to the first operation condition information and second operation audio signal information corresponding to the second operation condition information; and comparing the first operation audio signal information with the second operation audio signal information to obtain the operation noise information, wherein the operation noise information is expressed in a streaming data form, and the noise source position information comprises client preference information corresponding to operation noise analysis results among failure occurrence reasons.
Further, said converting said operational noise information into a target operational failure event comprises: acquiring noise fluctuation information in noise source position information corresponding to the operation noise information; and converting the noise fluctuation information into signal indication information to obtain the target operation fault event, wherein the target operation fault event is expressed in a signal indication information matrix form.
Further, the determining, according to the signal indication information of the cause of the occurrence of the fault in the target operation fault event, the operation fault data of the industrial manufacturing equipment during operation in the operation period from the first operation condition information to the second operation condition information includes: determining from the target operational fault event that a maximum signal indicative of a cause of the occurrence of the fault is time consuming; and responding to the maximum signal indication time consumption matching signal indication information requirement, and determining operation fault data of the industrial manufacturing equipment during operation in the operation time period from the first operation condition information to the second operation condition information.
As shown in fig. 2, there is shown a big data based industrial manufacturing equipment fault diagnosis apparatus 200, the apparatus comprising:
an operation condition information obtaining module 210, configured to obtain first operation condition information and second operation condition information in an operation process of an industrial manufacturing device, where the first operation condition information and the second operation condition information are operation condition information corresponding to a preset time period in the operation process of the industrial manufacturing device;
an operation noise information determining module 220, configured to obtain operation noise information of the first operation condition information and the second operation condition information, where the operation noise information represents an operation noise analysis result between corresponding failure occurrence reasons between the first operation condition information and the second operation condition information;
an operational failure event determination module 230, configured to convert the operational noise information into a target operational failure event, where the target operational failure event includes a plurality of failure occurrence reasons;
and an operation fault data acquisition module 240, configured to determine, according to the signal indication information of the fault occurrence cause in the target operation fault event, operation fault data when the industrial manufacturing device operates in an operation time period between the first operation condition information and the second operation condition information.
Referring to fig. 3, a hardware structure diagram of the electronic device 110 is provided.
Fig. 3 is a block diagram of an electronic device 110 according to an embodiment of the present invention. The electronic device 110 in the embodiment of the present invention may be a server with data storage, transmission, and processing functions, as shown in fig. 3, the electronic device 110 includes: the device comprises a memory 111, a processor 112, a network module 113 and a big data-based industrial manufacturing equipment fault diagnosis device 200.
The memory 111, the processor 112, and the network module 113 are electrically connected directly or indirectly to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 111 stores therein a big data-based industrial manufacturing equipment fault diagnosis device 200, the big data-based industrial manufacturing equipment fault diagnosis device 200 includes at least one software functional module which can be stored in the memory 111 in the form of software or firmware (firmware), and the processor 112 executes various functional applications and data processing by running the software programs and modules stored in the memory 111, such as the big data-based industrial manufacturing equipment fault diagnosis device 200 in the embodiment of the present invention, so as to implement the big data-based industrial manufacturing equipment fault diagnosis method in the embodiment of the present invention.
The Memory 111 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 111 is used for storing a program, and the processor 112 executes the program after receiving the execution instruction.
The processor 112 may be an integrated circuit chip having data processing capabilities. The Processor 112 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps and logic blocks disclosed in embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The network module 113 is configured to establish a communication connection between the electronic device 110 and another communication terminal device through a network, so as to implement transceiving operation of network signals and data. The network signal may include a wireless signal or a wired signal.
It will be appreciated that the configuration shown in fig. 3 is merely illustrative, and that electronic device 110 may include more or fewer components than shown in fig. 3, or have a different configuration than shown in fig. 3. The components shown in fig. 3 may be implemented in hardware, software, or a combination thereof.
An embodiment of the present invention also provides a computer-readable storage medium, which includes a computer program. The computer program controls the electronic device 110 where the readable storage medium is located to execute the above-mentioned method when running.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, 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.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention or a part thereof, which essentially contributes to the prior art, can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, an electronic device 10, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (6)
1. A big data based industrial manufacturing equipment fault diagnosis method, characterized in that the method comprises:
acquiring first operating condition information and second operating condition information in the operating process of industrial manufacturing equipment, wherein the first operating condition information and the second operating condition information are operating condition information corresponding to a preset time period in the operating process of the industrial manufacturing equipment;
acquiring operation noise information of the first operation condition information and the second operation condition information, wherein the operation noise information represents an operation noise analysis result between the first operation condition information and the second operation condition information and corresponding equipment operation rates;
converting the operating noise information into a target operating fault event, wherein the target operating fault event comprises a plurality of fault occurrence reasons;
and determining the operation fault data of the industrial manufacturing equipment during operation in the operation time interval from the first operation condition information to the second operation condition information according to the signal indication information of the fault occurrence reason in the target operation fault event.
2. The method of claim 1, wherein the obtaining operational noise information for the first and second operational condition information comprises: determining first operation audio signal information corresponding to the first operation condition information and second operation audio signal information corresponding to the second operation condition information; and comparing the first operation audio signal information with the second operation audio signal information to obtain the operation noise information.
3. The method of claim 2, wherein said converting the operational noise information into a target operational failure event comprises: acquiring noise fluctuation information in noise source position information corresponding to the operation noise information; and converting the noise fluctuation information into signal indication information to obtain the target operation fault event, wherein the target operation fault event is expressed in a signal indication information matrix form.
4. The method of any of claims 1 to 3, wherein determining operational fault data during operation of the industrial manufacturing device during the operational time period between the first operating condition information and the second operating condition information based on the signal indicative of the cause of the occurrence of the fault in the target operational fault event comprises: determining from the target operational fault event that a maximum signal indicative of a cause of the occurrence of the fault is time consuming; and responding to the maximum signal indication time consumption matching signal indication information requirement, and determining operation fault data of the industrial manufacturing equipment during operation in the operation time period from the first operation condition information to the second operation condition information.
5. An apparatus for fault diagnosis of industrial manufacturing equipment based on big data, the apparatus comprising:
the system comprises an operation condition information acquisition module, a data processing module and a data processing module, wherein the operation condition information acquisition module is used for acquiring first operation condition information and second operation condition information in the operation process of the industrial manufacturing equipment, and the first operation condition information and the second operation condition information are operation condition information corresponding to a preset time period in the operation process of the industrial manufacturing equipment;
the operation noise information determination module is used for acquiring operation noise information of the first operation condition information and the second operation condition information, and the operation noise information represents an operation noise analysis result between corresponding failure occurrence reasons between the first operation condition information and the second operation condition information;
an operation fault event determination module, configured to convert the operation noise information into a target operation fault event, where the target operation fault event includes multiple fault occurrence reasons;
and the operation fault data acquisition module is used for determining operation fault data of the industrial manufacturing equipment during operation in the operation time period from the first operation working condition information to the second operation working condition information according to the signal indication information of the fault occurrence reason in the target operation fault event.
6. An electronic device, comprising a processor that when executed performs the method of any of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110927887.9A CN113568815A (en) | 2021-08-13 | 2021-08-13 | Industrial manufacturing equipment fault diagnosis method and device based on big data and electronic equipment |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202110927887.9A CN113568815A (en) | 2021-08-13 | 2021-08-13 | Industrial manufacturing equipment fault diagnosis method and device based on big data and electronic equipment |
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| CN113568815A true CN113568815A (en) | 2021-10-29 |
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| CN202110927887.9A Withdrawn CN113568815A (en) | 2021-08-13 | 2021-08-13 | Industrial manufacturing equipment fault diagnosis method and device based on big data and electronic equipment |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115758225A (en) * | 2023-01-06 | 2023-03-07 | 中建科技集团有限公司 | Fault prediction method and device based on multi-mode data fusion and storage medium |
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2021
- 2021-08-13 CN CN202110927887.9A patent/CN113568815A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115758225A (en) * | 2023-01-06 | 2023-03-07 | 中建科技集团有限公司 | Fault prediction method and device based on multi-mode data fusion and storage medium |
| CN115758225B (en) * | 2023-01-06 | 2023-08-29 | 中建科技集团有限公司 | Fault prediction method and device based on multi-mode data fusion and storage medium |
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Application publication date: 20211029 |