CN110806730A - Big data operation and maintenance platform, server and storage medium - Google Patents
Big data operation and maintenance platform, server and storage medium Download PDFInfo
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- CN110806730A CN110806730A CN201911003997.5A CN201911003997A CN110806730A CN 110806730 A CN110806730 A CN 110806730A CN 201911003997 A CN201911003997 A CN 201911003997A CN 110806730 A CN110806730 A CN 110806730A
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- 238000012544 monitoring process Methods 0.000 claims abstract description 35
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- 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]
- G05B19/4184—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 fault tolerance, reliability of production system
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- 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
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31088—Network communication between supervisor and cell, machine group
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- 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
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Abstract
The invention provides a big data operation platform, a server and a storage medium, wherein the platform comprises: the equipment monitoring module is used for monitoring the state parameters of the large-scale manufacturing plant equipment on line; and the fault diagnosis module is used for analyzing each type of state parameter based on the distributed artificial intelligence mode, screening two state monitoring parameters with the most serious alarm values in the state parameters when the alarm information of the equipment to be tested is monitored, inputting the equipment information and the alarm values corresponding to the two state monitoring parameters into an expert system to generate a corresponding initial analysis result, and inputting the initial analysis result into the main factor model to obtain a final complete analysis result. By monitoring the equipment state parameters of the large-scale manufacturing factory on line, when the faults are monitored, the faults are diagnosed, analyzed and positioned in time from multiple dimensions and multiple levels, the efficiency and the accuracy of equipment fault diagnosis are improved, and the next step of equipment operation and maintenance work can be carried out more efficiently and accurately.
Description
Technical Field
The invention relates to the technical field of equipment monitoring, in particular to a big data operation and maintenance platform, a server and a storage medium.
Background
The method aims at the problem that effective management and operation and maintenance of traditional large-scale manufacturing plant equipment are an important means for protecting company assets, and currently, real-time monitoring is carried out on the equipment in a mode of collecting various parameters such as vibration, temperature and pressure in a large-scale manufacturing plant, operation and maintenance workers are required to be familiar with and master numerous parameters related to monitoring so that whether the equipment to be tested breaks down can be accurately judged, and corresponding solutions are provided, so that the problem of equipment failure can be timely and effectively solved.
However, the existing equipment in a large-scale manufacturing plant usually adopts a single equipment reliability monitoring means, and different operation and maintenance processes are started according to an analysis result, but the analysis mode is single, the fault diagnosis accuracy is low, and each system is not integrated and communicated, so that after the equipment fails, the operation and maintenance processes are started slowly due to various reasons, and the operation and maintenance process efficiency is low.
Disclosure of Invention
In view of the above drawbacks of the prior art, an object of the present invention is to provide a big data operation and maintenance platform, a server, and a storage medium, which are used to solve the problems of low efficiency and low accuracy of the operation and maintenance platform in the process of determining a fault in the prior art.
To achieve the above and other related objects, in a first aspect of the present application, there is provided a big data operation and maintenance platform, including:
the equipment monitoring module is used for monitoring the state parameters of the equipment of the large-scale manufacturing factory on line, wherein the state parameters comprise vibration parameters, stress wave parameters, pressure parameters, temperature parameters and flow parameters of the equipment to be tested;
and the fault diagnosis module is used for analyzing each type of state parameter based on a distributed artificial intelligence mode, screening two state monitoring parameters with the most serious alarm values in the state parameters when the alarm information of the equipment to be tested is monitored, inputting the equipment information and the alarm values corresponding to the two state monitoring parameters into an expert system to generate a corresponding initial analysis result, and inputting the initial analysis result into a main factor model to obtain a final complete analysis result.
In a second aspect of the present application, there is provided a server comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors to execute the instructions, and the one or more processors execute the instructions to cause the electronic device to perform the functions of the modules/units in the big data operation and maintenance platform.
In a third aspect of the present application, a storage medium is provided, which stores at least one program, where the at least one program, when called, performs the functions of the module/unit in the big data operation and maintenance platform.
As described above, the big data operation and maintenance platform, the server and the storage medium of the present invention have the following beneficial effects:
the invention provides a big data operation and maintenance platform, which can diagnose, analyze and locate faults in time from multiple dimensions and multiple levels when the faults are monitored by monitoring the equipment state parameters of a large-scale manufacturing factory on line, thereby improving the efficiency and accuracy of equipment fault diagnosis and being capable of carrying out the next step of equipment operation and maintenance more efficiently and accurately.
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FIG. 1 is a block diagram of a big data operation and maintenance platform according to an embodiment of the present invention;
FIG. 2 is a block diagram illustrating a complete structure of a big data operation and maintenance platform according to an embodiment of the present invention;
FIG. 3 is a complete diagram of a big data operation and maintenance platform according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating a fault handling process for a big data operation and maintenance platform according to an embodiment of the present invention;
fig. 5 is a flowchart illustrating a fault determination process of a big data operation and maintenance platform according to an embodiment of the present invention;
fig. 6 shows a block diagram of a big data maintenance server device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present application is provided for illustrative purposes, and other advantages and capabilities of the present application will become apparent to those skilled in the art from the present disclosure.
In the following description, reference is made to the accompanying drawings that describe several embodiments of the application. It is to be understood that other embodiments may be utilized and that mechanical, structural, electrical, and operational changes may be made without departing from the spirit and scope of the present disclosure. The following detailed description is not to be taken in a limiting sense, and the scope of embodiments of the present application is defined only by the claims of the issued patent. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. Spatially relative terms, such as "upper," "lower," "left," "right," "lower," "below," "lower," "above," "upper," and the like, may be used herein to facilitate describing one element or feature's relationship to another element or feature as illustrated in the figures.
Although the terms first, second, etc. may be used herein to describe various elements in some instances, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, the first preset threshold may be referred to as a second preset threshold, and similarly, the second preset threshold may be referred to as a first preset threshold, without departing from the scope of the various described embodiments. The first preset threshold and the preset threshold are both described as one threshold, but they are not the same preset threshold unless the context clearly indicates otherwise. Similar situations also include a first volume and a second volume.
Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used in this specification, specify the presence of stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, steps, operations, elements, components, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C "are only exceptions to this definition should be done when combinations of elements, functions, steps or operations are inherently mutually exclusive in some manner.
Referring to fig. 1, a block diagram of an integrated energy system according to an embodiment of the present invention includes:
the device monitoring module 1 is used for monitoring the state parameters of the devices of the large-scale manufacturing factory on line, wherein the state parameters comprise vibration parameters, stress wave parameters, pressure parameters, temperature parameters and flow parameters of the devices to be tested;
the fault diagnosis module 2 is used for screening two state monitoring parameters with the most serious alarm values in the state parameters when monitoring alarm information of equipment to be tested, inputting equipment information and alarm values corresponding to the two state monitoring parameters into an expert system to generate corresponding initial analysis results, and inputting the initial analysis results into a main factor model to obtain final complete analysis results.
In this embodiment, see the failure determination flowchart in fig. 5 in detail, obtain device parameters of a large-scale manufacturing plant, select two parameters (the most serious parameter category and the less serious parameter category) with the largest difference between the alarm value (exceeding the preset threshold) of the alarm device and the corresponding threshold, send corresponding device information (unique MAC address, rotation speed, service life, record of recent failure and maintenance, etc.), the alarm value, and the parameter type to an expert system to generate respective initial analysis results (i.e., a first initial analysis result and a second initial analysis result), and input the first initial analysis result and the second initial analysis result to a main factor model to obtain a complete analysis result (specific location of device failure, failure cause).
Specifically, the fault diagnosis module can be integrated with a remote equipment diagnosis result under the support of a monitoring equipment manufacturer, so that the fault diagnosis module is more professional and can be used for diagnosing and positioning equipment faults more accurately and providing suggestions and measures for eliminating the faults; and furthermore, according to the analysis results of different monitoring technologies, the equipment faults are analyzed and positioned in a multi-level and multi-dimensional manner.
In the embodiment, compared with the traditional offline mode for monitoring and positioning the fault or monitoring the equipment by a single vibration technology, the fault diagnosis method has the advantages that the analysis is carried out by combining various parameters, meanwhile, the state parameters are analyzed by adopting a distributed artificial intelligence mode, and the fault can be intelligently diagnosed by combining an expert system, so that on one hand, the fault diagnosis process is simplified; on the other hand, the efficiency and the accuracy of fault diagnosis are improved.
Please refer to fig. 2 and fig. 3, which are a block diagram and a schematic diagram of a complete structure of a big data operation and maintenance platform according to an embodiment of the present invention, respectively, where the big data operation and maintenance platform further includes:
and the operation and maintenance management module 3 is used for checking and counting maintenance information, and is also used for starting an operation and maintenance plan to maintain by using spare parts when a certain equipment fault is monitored, and feeding back a maintenance effect.
Specifically, see fig. 4 for a detailed fault processing flow chart, when a fault of a certain device is monitored, a complete analysis result is obtained by using the main factor model, an operation and maintenance plan for solving the fault is generated according to the complete analysis result, a spare part is used for maintaining the faulty device according to the operation and maintenance plan, and a maintenance record and a maintenance effect are generated after maintenance.
In this embodiment, through logging in to the interface and entering into the operation and maintenance management module, operation and maintenance plan, operation and maintenance task, operation and maintenance feedback can be checked, historical operation and maintenance task and result can be traced back, the maintenance plan can be timely produced after the fault is conveniently found and the fault is positioned for maintenance personnel to execute, and simultaneously, the operation and maintenance feedback result is monitored, so that the maintenance progress can be conveniently followed and mastered at any time, and digital maintenance is realized.
On the basis of the above embodiment, the big data operation and maintenance platform further includes:
and the data center 4 (operation and maintenance supervision big data center) is used for storing maintenance data, wherein the maintenance data comprises maintenance history records, the number of maintenance equipment, spare parts used for maintenance, maintenance personnel, maintenance effects and residual maintenance materials.
Wherein, can easily master the maintenance record and the maintenance effect of each equipment through maintenance data, simultaneously, still monitor the statistics to maintenance goods and materials, the managers of being convenient for can look over, manage the spare parts, is favorable to the administrator to know warehouse goods and materials situation, when breaking down, can have corresponding goods and materials to maintain.
Specifically, the real-time monitoring data of the device is generated at a high frequency (multiple pieces of data can be generated in ten seconds at each monitoring point), the data is heavily dependent on the acquisition time (each piece of data is required to correspond to unique time), and the multi-information quantity of the measuring points is large (conventional real-time monitoring systems all have thousands of monitoring points which generate data every second and generate dozens of GB of data quantity every day). In order to support upper-layer application and well manage mass data, an HBase database is adopted for data storage of the platform, and the HBase is a distributed and column-oriented storage system constructed on an HDFS. When a super-large-scale data set needs to be read and written in real time and randomly accessed, the method is suitable for the condition that the insertion is more frequent than the query operation (the write operation of HBase is more efficient), for example, a history record table and a log file. The service scene is simple: the method does not need too many relational database characteristics, and is listed in a cross column, a cross table, a transaction, a connection and the like, thereby facilitating the query and maintenance of users.
On the basis of the above embodiment, the big data operation and maintenance platform further includes:
the login module 5 is used for verifying biological information (iris, fingerprint and face information) and a password so as to facilitate login of a user, and compared with a password verification mode, the login module improves the safety performance of the platform; wherein, different authorities are set according to different user grades, for example, a user has three roles: company managers, operation and maintenance managers and operation and maintenance personnel. After different roles log in the platform, the roles are different from each other in view and operation. Directly entering an operation and maintenance supervision big data center after a company administrator logs in; after logging in, the operation and maintenance manager directly enters 'equipment monitoring'; after logging in, the operation and maintenance personnel enter into operation and maintenance management to check respective operation and maintenance tasks.
In one embodiment, the platform meets the requirement that a plurality of users access the database simultaneously, displays a real-time dynamic graph of the interface operated by the plurality of users together, namely, the users with different roles operate the data center, equipment monitoring and operation and maintenance management in the database together, and realizes the informationization and paperless operation and maintenance information.
Referring to fig. 6, a big data operation and maintenance server according to the present invention includes:
one or more processors 6;
a memory 7; and
one or more programs, wherein the one or more programs are stored in the memory 7 and configured to be executed by the one or more processors 6, which execute the functions of the modules/units in the big data operation and maintenance platform described above.
The processor 6 is operatively coupled to memory and/or non-volatile storage. More specifically, processor 6 may execute instructions stored in memory and/or non-volatile storage to perform operations in a computing device, such as generating and/or transmitting image data to an electronic display. As such, the processor may include one or more general purpose microprocessors, one or more application specific processors (ASICs), one or more field programmable logic arrays (FPGAs), or any combination thereof.
Suitable for use in electronic devices, such as but not limited to notebook computers, tablet computers, mobile phones, smart phones, media players, Personal Digital Assistants (PDAs), navigators, smart televisions, smart watches, digital cameras, and the like, as well as combinations of two or more thereof, in practical embodiments. It should be understood that the electronic device described in the embodiments of the present application is only one example of an application, and that components of the device may have more or fewer components than shown, or a different configuration of components. The various components of the depicted figures may be implemented in hardware, software, or a combination of hardware and software, including one or more signal processing and/or application specific integrated circuits. In the specific embodiment of the present application, the electronic device will be described as a smart phone.
In another embodiment of the present application, a computer-readable storage medium is further disclosed, where the computer-readable storage medium stores at least one program, and the at least one program, when called, executes the functions of the modules/units in the big data operation and maintenance platform.
The procedure is described in detail in the above embodiments, and is not repeated herein.
It should be noted that, through the above description of the embodiments, those skilled in the art can clearly understand that part or all of the present application can be implemented by software and combined with necessary general hardware platform.
With this understanding in mind, the technical solutions of the present application and/or portions thereof that contribute to the prior art may be embodied in the form of a software product that may include one or more machine-readable media having stored thereon machine-executable instructions that, when executed by one or more machines such as a computer, network of computers, or other electronic devices, may cause the one or more machines to perform operations in accordance with embodiments of the present application. For example, each step in the robot control method is executed. The machine-readable medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs (read-only memories), magneto-optical disks, ROMs (read-only memories), RAMs (random access memories), EPROMs (erasable programmable read-only memories), EEPROMs (electrically erasable programmable read-only memories), magnetic or optical cards, flash memory, or other type of media/machine-readable medium suitable for storing machine-executable instructions. Wherein the storage medium may be located in the robot or in a third party server, such as a server providing an application mall. The specific application mall is not limited, such as the millet application mall, the Huawei application mall, and the apple application mall.
The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In summary, the invention provides a big data operation and maintenance platform, by monitoring the equipment state parameters of a large-scale manufacturing plant on line, when a fault is monitored, the fault is diagnosed, analyzed and positioned in time from multiple dimensions and multiple levels, so that the efficiency and accuracy of equipment fault diagnosis are improved, and the next step of equipment operation and maintenance work can be performed more efficiently and accurately. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
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 (10)
1. A big data operation and maintenance platform, comprising:
the equipment monitoring module is used for monitoring the state parameters of the equipment of the large-scale manufacturing factory on line, wherein the state parameters comprise vibration parameters, stress wave parameters, pressure parameters, temperature parameters and flow parameters of the equipment to be tested;
and the fault diagnosis module is used for analyzing each type of state parameter based on a distributed artificial intelligence mode, screening two state monitoring parameters with the most serious alarm values in the state parameters when the alarm information of the equipment to be tested is monitored, inputting the equipment information and the alarm values corresponding to the two state monitoring parameters into an expert system to generate a corresponding initial analysis result, and inputting the initial analysis result into a main factor model to obtain a final complete analysis result.
2. The big data operation and maintenance platform according to claim 1, further comprising: and the operation and maintenance management module is used for checking and counting maintenance information, and is also used for starting an operation and maintenance plan to maintain by using spare parts when monitoring a certain equipment fault and feeding back a maintenance effect.
3. The big data operation and maintenance platform according to claim 2, wherein the operation and maintenance management module further comprises:
when a fault of a certain device is monitored, a complete analysis result is obtained by using the main factor model, an operation and maintenance plan for solving the fault is generated according to the complete analysis result, a spare part is used for maintaining the fault device according to the operation and maintenance plan, and a maintenance record and a maintenance effect are generated after maintenance.
4. The big data operation and maintenance platform according to claim 1 or 2, further comprising: and the data center is used for storing maintenance data, wherein the maintenance data comprises maintenance history records, the number of maintenance equipment, spare parts used for maintenance, maintenance personnel, maintenance effects and residual maintenance materials.
5. The big data operation and maintenance platform according to claim 4, further comprising: and the login module is used for verifying by utilizing the biological information and the password so as to facilitate the login of the user, wherein different authorities are set according to different user grades.
6. The big data operation and maintenance platform according to claim 4, wherein the data storage of the data center employs Hbase database.
7. The big data operation and maintenance platform according to claim 1, wherein the platform satisfies that a plurality of users simultaneously access the database and display a real-time dynamic graph of the interface operated by the plurality of users together.
8. The big data operation and maintenance platform according to claim 1, wherein the prime factor model employs supervised machine learning to predict the failure source corresponding to the equipment.
9. A server, characterized in that the electronic device comprises:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and are partitioned into one or more modules/units, and the one or more processors execute the one or more programs to implement the functions of the modules/units in the big data operation and maintenance platform according to any one of claims 1 to 8.
10. A storage medium storing at least one program, wherein the at least one program when executed when called implements the functions of the module/unit in the big data operation and maintenance platform according to any one of claims 1 to 8.
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| CN111858120B (en) * | 2020-07-20 | 2023-07-28 | 北京百度网讯科技有限公司 | Fault prediction method and device, electronic equipment and storage medium |
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| CN112527770A (en) * | 2020-12-12 | 2021-03-19 | 南京地铁建设有限责任公司 | Data management method and device for multi-authority escalator monitoring database |
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