CN117312794A - Concrete mixing equipment fault identification method and system based on multi-source data analysis - Google Patents
Concrete mixing equipment fault identification method and system based on multi-source data analysis Download PDFInfo
- Publication number
- CN117312794A CN117312794A CN202311282202.5A CN202311282202A CN117312794A CN 117312794 A CN117312794 A CN 117312794A CN 202311282202 A CN202311282202 A CN 202311282202A CN 117312794 A CN117312794 A CN 117312794A
- Authority
- CN
- China
- Prior art keywords
- equipment
- data
- attribute
- fault
- operation data
- 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.)
- Withdrawn
Links
- 238000007405 data analysis Methods 0.000 title claims abstract description 60
- 238000002156 mixing Methods 0.000 title claims abstract description 57
- 238000000034 method Methods 0.000 title claims abstract description 47
- 230000002159 abnormal effect Effects 0.000 claims abstract description 87
- 238000003756 stirring Methods 0.000 claims abstract description 77
- 238000012544 monitoring process Methods 0.000 claims abstract description 49
- 238000004458 analytical method Methods 0.000 claims abstract description 37
- 238000012545 processing Methods 0.000 claims abstract description 34
- 238000013507 mapping Methods 0.000 claims abstract description 18
- 238000003745 diagnosis Methods 0.000 claims abstract description 17
- 230000000694 effects Effects 0.000 claims abstract description 16
- 230000015654 memory Effects 0.000 claims description 45
- 238000012549 training Methods 0.000 claims description 37
- 238000003860 storage Methods 0.000 claims description 20
- 238000004590 computer program Methods 0.000 claims description 14
- 238000012795 verification Methods 0.000 claims description 14
- 238000012098 association analyses Methods 0.000 claims description 13
- 230000006870 function Effects 0.000 claims description 13
- 238000012360 testing method Methods 0.000 claims description 13
- 238000002372 labelling Methods 0.000 claims description 8
- 238000005265 energy consumption Methods 0.000 claims description 7
- 239000000843 powder Substances 0.000 claims description 7
- 238000005516 engineering process Methods 0.000 claims description 6
- 230000011218 segmentation Effects 0.000 claims description 6
- 230000000007 visual effect Effects 0.000 claims description 5
- 238000007418 data mining Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 8
- 230000001360 synchronised effect Effects 0.000 description 4
- 239000004568 cement Substances 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000002093 peripheral effect Effects 0.000 description 3
- 238000003491 array Methods 0.000 description 2
- 230000001413 cellular effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 238000010295 mobile communication Methods 0.000 description 2
- 238000002360 preparation method Methods 0.000 description 2
- 239000002994 raw material Substances 0.000 description 2
- 239000004576 sand Substances 0.000 description 2
- 239000004575 stone Substances 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a concrete mixing equipment fault identification method and system based on multisource data analysis, and relates to the technical field of data processing, wherein the method comprises the following steps: monitoring the concrete mixing equipment in real time based on a sensor monitoring network, and then marking and classifying and integrating the multi-dimensional attribute of the multi-source equipment operation data stream obtained by monitoring to obtain the multi-dimensional equipment operation attribute data stream; setting operation data analysis multi-channel based on the device performance factor index set; mapping the multi-dimensional equipment operation attribute data stream to an operation data analysis multi-channel for identification to obtain a multi-dimensional equipment operation abnormal data stream; and diagnosing and analyzing the abnormal operation data flow of the multidimensional equipment based on the fault analysis network of the stirring equipment, and outputting fault diagnosis information of the concrete stirring equipment. The intelligent identification of the fault information of the stirring equipment is realized through multi-source operation data analysis, the fault identification accuracy and the identification processing efficiency are improved, and the technical effect of the fault identification timeliness is further ensured.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a concrete mixing equipment fault identification method and system based on multi-source data analysis.
Background
The concrete stirring equipment is one of the indispensable equipment on the construction site, and has the function of uniformly mixing cement, sand, stones and other raw materials so as to prepare the concrete. In order to ensure the preparation quality of concrete, the running state of concrete stirring equipment needs to be accurately controlled, and equipment faults of the concrete stirring equipment are timely identified and solved. However, the prior art concrete mixing plant has low accuracy of fault identification and low timeliness of fault identification.
Disclosure of Invention
According to the method and the system for identifying the faults of the concrete mixing equipment based on the multi-source data analysis, the technical problems that in the prior art, the fault identification accuracy of the concrete mixing equipment is low and the fault identification timeliness is low are solved, the intelligent identification of the fault information of the mixing equipment is achieved through the multi-source operation data analysis, the fault identification accuracy and the identification processing efficiency are improved, and then the technical effect of the fault identification timeliness is guaranteed.
In view of the above problems, the invention provides a method and a system for identifying faults of concrete mixing equipment based on multi-source data analysis.
In a first aspect, the present application provides a method for identifying a failure of a concrete mixing plant based on multi-source data analysis, the method comprising: a sensor monitoring network is arranged, real-time monitoring is carried out on the concrete mixing equipment based on the sensor monitoring network, and a multi-source equipment operation data stream is acquired and acquired; performing multidimensional attribute marking on the multi-source equipment operation data stream to obtain equipment multidimensional operation data attribute information; classifying and integrating the multi-source equipment operation data stream according to the multi-dimensional operation data attribute information of the equipment to obtain a multi-dimensional equipment operation attribute data stream; acquiring a device performance factor index set, wherein the device performance factor index set comprises motor power, stirring effect, powder conveying, gas path metering, stirring noise and operation energy consumption; setting an operation data analysis multichannel based on the equipment performance factor index set; mapping the multi-dimensional equipment operation attribute data stream to the operation data analysis multi-channel to perform association analysis and identification, so as to obtain a multi-dimensional equipment operation abnormal data stream; and diagnosing and analyzing the abnormal operation data flow of the multidimensional equipment based on the fault analysis network of the stirring equipment, and outputting fault diagnosis information of the concrete stirring equipment.
In another aspect, the present application also provides a system for identifying a failure of a concrete mixing plant based on multi-source data analysis, the system comprising: the operation data stream acquisition module is used for arranging a sensor monitoring network, monitoring the concrete mixing equipment in real time based on the sensor monitoring network, and acquiring multi-source equipment operation data streams; the multi-dimensional attribute marking module is used for carrying out multi-dimensional attribute marking on the multi-source equipment operation data stream to obtain equipment multi-dimensional operation data attribute information; the data multidimensional attribute marking module is used for classifying and integrating the multi-source equipment operation data stream according to the equipment multidimensional operation data attribute information to obtain a multidimensional equipment operation attribute data stream; the factor index set acquisition module is used for acquiring a device performance factor index set, wherein the device performance factor index set comprises motor power, stirring effect, powder conveying, gas path metering, stirring noise and operation energy consumption; the analysis multichannel setting module is used for setting operation data analysis multichannel based on the equipment performance factor index set; the association analysis and identification module is used for mapping the multi-dimensional equipment operation attribute data stream to the operation data analysis multi-channel to carry out association analysis and identification so as to obtain a multi-dimensional equipment operation abnormal data stream; and the fault diagnosis and analysis module is used for carrying out diagnosis and analysis on the operation abnormal data flow of the multidimensional equipment based on the stirring equipment fault analysis network and outputting fault diagnosis information of the concrete stirring equipment.
In a third aspect, the present application provides an electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, the computer program implementing the steps of any of the methods described above when executed by the processor.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described above.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
because the sensor monitoring network is arranged, the concrete stirring equipment is monitored in real time based on the sensor monitoring network, and then the acquired multi-source equipment operation data stream is subjected to multi-dimensional attribute marking and classification integration to obtain the multi-dimensional equipment operation attribute data stream; setting operation data analysis multi-channel based on the device performance factor index set; mapping the operation attribute data stream of the multi-dimensional equipment to the operation data analysis multi-channel to perform association analysis and identification, so as to obtain an operation abnormal data stream of the multi-dimensional equipment; and diagnosing and analyzing the abnormal operation data flow of the multidimensional equipment based on the fault analysis network of the stirring equipment, and outputting fault diagnosis information of the concrete stirring equipment. And further, intelligent identification of fault information of the stirring equipment is realized through multi-source operation data analysis, the fault identification accuracy and the identification processing efficiency are improved, and the technical effect of timeliness of fault identification is further ensured.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
FIG. 1 is a schematic flow chart of a method for identifying faults of a concrete mixing device based on multi-source data analysis;
FIG. 2 is a schematic flow chart of obtaining equipment multidimensional operation data attribute information in the method for identifying faults of concrete mixing equipment based on multi-source data analysis;
FIG. 3 is a schematic structural diagram of a system for identifying faults of a concrete mixing plant based on multi-source data analysis according to the present application;
fig. 4 is a schematic structural diagram of an exemplary electronic device of the present application.
Reference numerals illustrate: the system comprises an operation data stream acquisition module 11, a multi-dimensional attribute marking module 12, a data multi-dimensional attribute marking module 13, a factor index set acquisition module 14, an analysis multi-channel setting module 15, a correlation analysis identification module 16, a fault diagnosis analysis module 17, a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, an operating system 1151, an application 1152 and a user interface 1160.
Detailed Description
In the description of the present application, those skilled in the art will appreciate that the present application may be embodied as methods, apparatuses, electronic devices, and computer-readable storage media. Accordingly, the present application may be embodied in the following forms: complete hardware, complete software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, the present application may also be embodied in the form of a computer program product in one or more computer-readable storage media, which contain computer program code.
Any combination of one or more computer-readable storage media may be employed by the computer-readable storage media described above. The computer-readable storage medium includes: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer readable storage medium include the following: portable computer magnetic disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disk read-only memories, optical storage devices, magnetic storage devices, or any combination thereof. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device.
The technical scheme of the application is that the acquisition, storage, use, processing and the like of the data meet the relevant regulations of national laws.
The present application describes methods, apparatus, and electronic devices provided by the flowchart and/or block diagram.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in a computer readable storage medium that can cause a computer or other programmable data processing apparatus to function in a particular manner. Thus, instructions stored in a computer-readable storage medium produce an instruction means which implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The present application is described below with reference to the drawings in the present application.
Example 1
As shown in fig. 1, the present application provides a method for identifying a fault of a concrete mixing apparatus based on multi-source data analysis, the method comprising:
step S1: a sensor monitoring network is arranged, real-time monitoring is carried out on the concrete mixing equipment based on the sensor monitoring network, and a multi-source equipment operation data stream is acquired and acquired;
further, the step of deploying the sensor monitoring network further includes:
acquiring structural dimension parameter information of the concrete mixing equipment, and performing visual modeling by using the structural dimension parameter information to generate a three-dimensional model of the concrete mixing equipment;
Carrying out structural facility segmentation on the three-dimensional model of the concrete stirring equipment according to the application function to obtain a functional structural facility set of the stirring equipment;
respectively analyzing the data to be monitored for the facility sets of the stirring equipment functional structure, and determining a facility target monitoring data attribute set;
and carrying out sensor layout based on the facility target monitoring data attribute set, and determining the sensor monitoring network.
Concrete stirring equipment is one of indispensable equipment on building sites, and has the function of uniformly mixing cement, sand, stones and other raw materials to prepare concrete. In order to ensure the preparation quality of concrete, the running state of concrete stirring equipment needs to be accurately controlled, and equipment faults of the concrete stirring equipment are timely identified and solved.
For realizing the accurate accuse to concrete mixing equipment, lay sensor monitoring network, sensor monitoring network comprises multiple sensor, including temperature and humidity sensor, voltage current sensor, measurement sensor and figure acquisition sensor etc. for carry out the monitoring of full-scale operation data to concrete mixing equipment and gather. Firstly, obtaining structural dimension parameter information of concrete mixing equipment through equipment production drawing, then carrying out visual modeling by utilizing the structural dimension parameter information, and generating a three-dimensional model of the concrete mixing equipment through a three-dimensional modeling technology. And (3) carrying out structural facility segmentation on the three-dimensional model of the concrete stirring equipment according to the application function, namely carrying out composition facility segmentation according to the application function of each structure of the equipment to obtain corresponding facility sets of the stirring equipment function structure, such as facilities of cement bins, aggregate bins, stirring machines, screw conveyors and the like.
And respectively analyzing data to be monitored of the functional structure facility sets of the stirring equipment, namely analyzing the demand monitoring data of each structural facility to determine a facility target monitoring data attribute set, wherein the stirring temperature, stirring speed, stirring quantity and other attribute data of the stirring equipment structure are monitored. And carrying out sensor layout based on the facility target monitoring data attribute set, namely carrying out corresponding sensor determination according to the data attribute to be monitored, for example, monitoring the stirring temperature by arranging a temperature sensor, so as to determine a sensor monitoring network. The operation monitoring of the whole-coverage concrete mixing equipment is distributed through the sensor monitoring network, and the comprehensiveness and the acquisition timeliness of operation monitoring data are improved.
Step S2: performing multidimensional attribute marking on the multi-source equipment operation data stream to obtain equipment multidimensional operation data attribute information;
as shown in fig. 2, further, the obtaining the multi-dimensional operation data attribute information of the device further includes:
acquiring a data attribute identification rule, wherein the data attribute identification rule comprises an application type identification rule and a data format identification rule;
Classifying the attributes of the multi-source equipment operation data stream based on the application type identification rule to obtain equipment operation data type information;
constructing a format attribute classifier through the data format identification rule, wherein the format attribute classifier comprises generation time, a data hierarchy and a storage format;
classifying and identifying the multi-source equipment operation data stream according to the format attribute classifier to obtain equipment operation data format attribute information;
and combining and generating the equipment multidimensional operation data attribute information based on the equipment operation data type information and the equipment operation data format attribute information.
Specifically, in order to improve the data stream processing efficiency, the multi-dimensional attribute marking is performed on the multi-source device operation data stream. Firstly, a data attribute identification rule is obtained, wherein the data attribute identification rule is a data multidimensional attribute identification basis and comprises an application type identification rule and a data format identification rule. The application type representation rule is a source application type identification basis of the monitoring data, attribute classification is carried out on the multi-source equipment operation data stream based on the application type identification rule, and the operation data stream is identified according to the source type to obtain equipment operation data type information, such as stirring temperature type data, stirring speed type data and the like.
The data format identification rule is a data application format identification basis, and a format attribute classifier is constructed through the data format identification rule and is used for classifying data format attributes, wherein the data format attribute classifier comprises generation time, namely data generation time; a data hierarchy, i.e. a data impact hierarchy, for example a higher impact hierarchy of motor power data on the operation of the device; the storage format, i.e., the data storage format, includes values, images, etc. And classifying and identifying the multi-source equipment operation data stream according to the format attribute classifier to obtain corresponding equipment operation data format attribute information. And based on the equipment operation data type information and the equipment operation data format attribute information, combining and carrying out data stream attribute identification to generate corresponding equipment multidimensional operation data attribute information. And the attribute identification is carried out on the data stream by combining the application type and the data format, so that the comprehensiveness of the data attribute identification is improved, and the subsequent processing efficiency of the data stream is further improved.
Step S3: classifying and integrating the multi-source equipment operation data stream according to the multi-dimensional operation data attribute information of the equipment to obtain a multi-dimensional equipment operation attribute data stream;
Step S4: acquiring a device performance factor index set, wherein the device performance factor index set comprises motor power, stirring effect, powder conveying, gas path metering, stirring noise and operation energy consumption;
step S5: setting an operation data analysis multichannel based on the equipment performance factor index set;
specifically, the multi-source equipment operation data streams are classified and integrated according to the equipment multi-dimensional operation data attribute information, namely, the data streams with the same attribute are classified into one type, and the integrated multi-dimensional equipment operation attribute data streams are obtained. The method comprises the steps of obtaining a device performance factor index set, wherein the device performance factor index set is an operation performance evaluation index of the concrete stirring device, and comprises motor power, stirring effect, powder conveying, gas path metering, stirring noise, operation energy consumption and the like, and the operation condition of the device is evaluated by the integrated multidimensional performance factor index. And setting operation data analysis multi-channels based on the equipment performance factor index set, wherein the number of the channels of the operation data analysis multi-channels corresponds to the equipment performance factor index one by one so as to improve the operation data processing efficiency.
Step S6: mapping the multi-dimensional equipment operation attribute data stream to the operation data analysis multi-channel to perform association analysis and identification, so as to obtain a multi-dimensional equipment operation abnormal data stream;
Further, the mapping the multi-dimensional equipment operation attribute data stream to the operation data analysis multi-channel for association analysis and identification, and the steps of the application further include:
extracting the performance factor index of the equipment by analyzing the operation data and multiple channels to obtain the target processing performance data index of the channels;
respectively carrying out associated data attribute analysis based on the channel target processing performance data indexes, and marking to obtain a channel associated operation data attribute set;
drawing a correlation network according to the channel correlation operation data attribute set to generate a channel correlation data attribute network;
and mapping and splitting the multi-dimensional equipment operation attribute data stream and the operation data analysis multi-channel based on the channel associated data attribute network.
Specifically, mapping the multi-dimensional equipment operation attribute data stream into the operation data analysis multi-channel for carrying out association analysis and identification, and extracting equipment performance factor indexes from the operation data analysis multi-channel to obtain channel target processing performance data indexes;
respectively carrying out associated data attribute analysis based on the channel target processing performance data indexes, and marking to obtain a channel associated operation data attribute set;
Drawing a correlation network according to the channel correlation operation data attribute set to generate a channel correlation data attribute network;
and mapping and splitting the multi-dimensional equipment operation attribute data stream and the operation data analysis multi-channel based on the channel associated data attribute network. Firstly, extracting the device performance factor indexes of the operation data analysis multi-channel, namely sequentially analyzing the processable factor index data of each data channel to obtain corresponding channel target processing performance data indexes. And respectively carrying out associated data attribute analysis based on the channel target processing performance data indexes, namely determining the type of the data attribute associated with the data indexes of the processable data indexes of each channel, wherein for the stirring effect factor indexes, the associated data attributes comprise stirring uniformity, stirring quantity and the like, and marking to obtain a channel associated operation data attribute set.
And carrying out association network drawing according to the channel association operation data attribute set, namely connecting and drawing each factor index and the corresponding operation data attribute thereof, and generating a channel association data attribute network for displaying association attribute data which can be processed by each data channel. And mapping and splitting the operation attribute data stream of the multi-dimensional equipment and the operation data analysis multi-channel based on the channel associated data attribute network, and splitting the data stream into corresponding data channels according to the data attribute matching of the data stream for processing. And the abnormal operation data stream of the multidimensional equipment, namely the equipment operation fault data, is obtained through the identification, analysis and processing of each channel. Abnormal data identification processing of the multidimensional operation data stream is realized through multichannel mapping and shunting, so that the accuracy and the efficiency of abnormal data identification are improved, and the timeliness of fault identification is further ensured.
Step S7: and diagnosing and analyzing the abnormal operation data flow of the multidimensional equipment based on the fault analysis network of the stirring equipment, and outputting fault diagnosis information of the concrete stirring equipment.
Further, the steps of the present application further include:
obtaining a device fault factor set, wherein the device fault factor set comprises a fault type, a fault grade and a generation reason;
extracting fault attributes of the equipment fault factor set to obtain an equipment operation fault attribute set;
constructing an equipment operation fault knowledge graph based on the equipment operation fault attribute set;
and constructing a concrete mixing equipment fault library, and training the equipment operation fault knowledge graph based on the concrete mixing equipment fault library to obtain the mixing equipment fault analysis network.
Specifically, the operation abnormal data flow of the multi-dimensional equipment is diagnosed and analyzed based on a fault analysis network of the stirring equipment, and an equipment fault factor set is firstly formulated and obtained, wherein the equipment fault factor set is a fault diagnosis processing key point and comprises a fault type, a fault grade, a generation reason and the like. Extracting fault attributes of the equipment fault factor set, namely sequentially subdividing specific attributes of each fault factor, wherein the fault types comprise motor faults, mechanical faults, conveying faults, stirring faults and the like; the fault grade can be set to be classified according to the fault severity; the generation reasons comprise that conveying equipment is not reset, a belt is deviated, tripping is carried out, stirring time is too short, and the like, and corresponding equipment operation fault attribute sets are obtained through subdivision.
And constructing an equipment operation fault knowledge graph based on the equipment operation fault attribute set, and taking each equipment operation fault attribute as a constituting node of the knowledge graph. And constructing a concrete mixing equipment fault database by a big data technology, wherein the concrete mixing equipment fault database is a historical equipment operation fault database and comprises operation fault data of all attribute types. And training the equipment operation fault knowledge graph by using the neural network based on the concrete mixing equipment fault library to obtain a mixing equipment fault analysis network with the fault analysis accuracy reaching the standard, wherein the mixing equipment fault analysis network is used for accurately analyzing specific fault attributes according to the equipment operation abnormal data. The intelligent identification of the fault information of the stirring equipment is realized, the fault identification accuracy and the identification processing efficiency are improved, and the fault identification timeliness is further ensured.
Further, the step of obtaining the abnormal data flow of the multi-dimensional equipment operation further includes:
acquiring an equipment abnormal operation sample data set through a data mining technology;
analyzing multiple channels based on the operation data, and determining an abnormal data tag set;
classifying and labeling the equipment abnormal operation sample data set according to the abnormal data label set to obtain an abnormal operation characteristic training sample data set;
Model training and verification are carried out based on the abnormal operation feature training sample data set, and an equipment abnormal operation data classifier is generated;
and identifying the multi-dimensional equipment operation attribute data stream based on the equipment abnormal operation data classifier to obtain the multi-dimensional equipment operation abnormal data stream.
Further, the generating device abnormal operation data classifier further comprises:
proportional division is carried out on the abnormal operation characteristic training sample data set to obtain a sample training set, a sample verification set and a sample test set;
performing network model supervision training based on the sample training set to obtain a basic abnormal operation data classifier;
and verifying and testing the basic abnormal operation data classifier based on the sample verification set and the sample test set until the model accuracy reaches a preset requirement, and generating the equipment abnormal operation data classifier.
Specifically, the abnormal data identification processing process of the operation data analysis multichannel specifically comprises the following steps: firstly, acquiring an equipment abnormal operation sample data set by a data mining technology, wherein the equipment abnormal operation sample data set is an abnormal operation characteristic data set of historical concrete mixing equipment. Based on the operation data analysis multi-channel, the abnormal data label set is sequentially determined, and for the stirring noise channel, for example, labels such as normal stirring sound, abnormal stirring noise and the like can be set. And classifying and labeling the abnormal operation sample data set of the equipment according to the abnormal data label set, namely labeling the sample data by manual labeling to obtain an abnormal operation characteristic training sample data set after label labeling.
Model training and verification are performed based on the abnormal operation feature training sample data set, and the abnormal operation feature training sample data set is firstly divided into a sample training set, a sample verification set and a sample test set according to a ratio of 6:2:2. And performing network model supervision training based on the sample training set to obtain a basic abnormal operation data classifier corresponding to initial training, and then verifying and testing the basic abnormal operation data classifier based on the sample verification set and the sample test set, and automatically setting a preset accuracy requirement until the model accuracy reaches the preset requirement to generate an equipment abnormal operation data classifier with standard training accuracy, wherein the equipment abnormal operation data classifier is used for identifying and classifying the equipment operation data. And identifying the multi-dimensional equipment operation attribute data stream based on the equipment abnormal operation data classifier, and outputting the multi-dimensional equipment operation abnormal data stream with the obtained abnormal label. And generating an equipment abnormal operation data classifier through the labeled sample data training, so as to intelligently identify the equipment operation abnormal data stream, and improve the accuracy and the efficiency of identification processing of the abnormal data.
In summary, the method and the system for identifying the faults of the concrete mixing equipment based on the multi-source data analysis have the following technical effects:
because the sensor monitoring network is arranged, the concrete stirring equipment is monitored in real time based on the sensor monitoring network, and then the acquired multi-source equipment operation data stream is subjected to multi-dimensional attribute marking and classification integration to obtain the multi-dimensional equipment operation attribute data stream; setting operation data analysis multi-channel based on the device performance factor index set; mapping the operation attribute data stream of the multi-dimensional equipment to the operation data analysis multi-channel to perform association analysis and identification, so as to obtain an operation abnormal data stream of the multi-dimensional equipment; and diagnosing and analyzing the abnormal operation data flow of the multidimensional equipment based on the fault analysis network of the stirring equipment, and outputting fault diagnosis information of the concrete stirring equipment. And further, intelligent identification of fault information of the stirring equipment is realized through multi-source operation data analysis, the fault identification accuracy and the identification processing efficiency are improved, and the technical effect of timeliness of fault identification is further ensured.
Example two
Based on the same inventive concept as the method for identifying the faults of the concrete mixing equipment based on the multi-source data analysis in the previous embodiment, the invention also provides a system for identifying the faults of the concrete mixing equipment based on the multi-source data analysis, as shown in fig. 3, wherein the system comprises:
The operation data stream acquisition module 11 is used for arranging a sensor monitoring network, monitoring the concrete mixing equipment in real time based on the sensor monitoring network, and acquiring multi-source equipment operation data streams;
the multidimensional attribute marking module 12 is configured to perform multidimensional attribute marking on the multi-source device operation data stream to obtain device multidimensional operation data attribute information;
the data multidimensional attribute marking module 13 is configured to classify and integrate the multi-source device operation data stream according to the multidimensional operation data attribute information of the device to obtain a multidimensional device operation attribute data stream;
a factor index set obtaining module 14, configured to obtain a device performance factor index set, where the device performance factor index set includes a motor power, a stirring effect, powder conveying, gas path metering, stirring noise, and operation energy consumption;
an analysis multi-channel setting module 15, configured to set an operation data analysis multi-channel based on the device performance factor index set;
the association analysis and identification module 16 is configured to map the multi-dimensional equipment operation attribute data stream to the operation data analysis multi-channel for association analysis and identification, so as to obtain a multi-dimensional equipment operation abnormal data stream;
The fault diagnosis and analysis module 17 is used for performing diagnosis and analysis on the multi-dimensional equipment operation abnormal data stream based on the stirring equipment fault analysis network and outputting fault diagnosis information of the concrete stirring equipment.
Further, the system further comprises:
the identification rule acquisition unit is used for acquiring a data attribute identification rule, wherein the data attribute identification rule comprises an application type identification rule and a data format identification rule;
the attribute classification unit is used for classifying the attributes of the multi-source equipment operation data stream based on the application type identification rule to obtain equipment operation data type information;
an attribute classifier construction unit, configured to construct a format attribute classifier according to the data format identification rule, where the format attribute classifier includes a generation time, a data hierarchy, and a storage format;
the classification identifier classifies and identifies the multi-source equipment operation data stream according to the format attribute classifier to obtain equipment operation data format attribute information;
and the data attribute information generating unit is used for generating the multi-dimensional operation data attribute information of the equipment based on the operation data type information of the equipment and the operation data format attribute information of the equipment in a combined mode.
Further, the system further comprises:
the visual modeling unit is used for acquiring structural dimension parameter information of the concrete mixing equipment, and performing visual modeling by utilizing the structural dimension parameter information to generate a three-dimensional model of the concrete mixing equipment;
the structure facility segmentation unit is used for carrying out structure facility segmentation on the three-dimensional model of the concrete stirring equipment according to the application function to obtain a stirring equipment function structure facility set;
the monitoring data analysis unit is used for respectively analyzing the data to be monitored of the facility set of the stirring equipment functional structure and determining a facility target monitoring data attribute set;
and the sensor layout unit is used for carrying out sensor layout based on the facility target monitoring data attribute set and determining the sensor monitoring network.
Further, the system further comprises:
the factor index extraction unit is used for extracting the equipment performance factor index of the operation data analysis multichannel to obtain a channel target processing performance data index;
the data attribute analysis unit is used for respectively carrying out associated data attribute analysis based on the channel target processing performance data indexes and marking to obtain a channel associated operation data attribute set;
The associated network drawing unit is used for carrying out associated network drawing according to the channel associated operation data attribute set to generate a channel associated data attribute network;
and the mapping and splitting unit is used for mapping and splitting the multi-dimensional equipment operation attribute data stream and the operation data analysis multi-channel based on the channel associated data attribute network.
Further, the system further comprises:
the abnormal sample data acquisition unit is used for acquiring an equipment abnormal operation sample data set through a data mining technology;
the data tag set determining unit is used for analyzing multiple channels based on the operation data and determining an abnormal data tag set;
the classification labeling unit is used for classifying and labeling the equipment abnormal operation sample data set according to the abnormal data label set to obtain an abnormal operation characteristic training sample data set;
the model training verification unit is used for carrying out model training and verification based on the abnormal operation feature training sample data set to generate an equipment abnormal operation data classifier;
and the attribute data stream identification unit is used for identifying the multi-dimensional equipment operation attribute data stream based on the equipment abnormal operation data classifier to obtain the multi-dimensional equipment operation abnormal data stream.
Further, the system further comprises:
the sample proportion dividing unit is used for carrying out proportion division on the abnormal operation characteristic training sample data set to obtain a sample training set, a sample verification set and a sample test set;
the model supervision training unit is used for performing network model supervision training based on the sample training set to obtain a basic abnormal operation data classifier;
and the model verification test unit is used for verifying and testing the basic abnormal operation data classifier based on the sample verification set and the sample test set until the model accuracy reaches a preset requirement, and generating the equipment abnormal operation data classifier.
Further, the system further comprises:
the system comprises a fault factor set obtaining unit, a fault factor generation unit and a fault analysis unit, wherein the fault factor set obtaining unit is used for obtaining a device fault factor set, and the device fault factor set comprises a fault type, a fault grade and a generation reason;
the fault attribute extraction unit is used for extracting fault attributes of the equipment fault factor set to obtain an equipment operation fault attribute set;
the fault knowledge graph construction unit is used for constructing a device operation fault knowledge graph based on the device operation fault attribute set;
The fault analysis network obtaining unit is used for constructing a fault library of the concrete stirring equipment, training the equipment operation fault knowledge graph based on the fault library of the concrete stirring equipment, and obtaining the fault analysis network of the stirring equipment.
The foregoing various modifications and specific examples of the method for identifying a failure of a concrete mixing plant based on multi-source data analysis in the first embodiment of fig. 1 are equally applicable to the system for identifying a failure of a concrete mixing plant based on multi-source data analysis in this embodiment, and by the foregoing detailed description of the method for identifying a failure of a concrete mixing plant based on multi-source data analysis, those skilled in the art can clearly know the implementation method of the system for identifying a failure of a concrete mixing plant based on multi-source data analysis in this embodiment, so that details of this embodiment will not be described herein for brevity.
In addition, the application further provides an electronic device, which comprises a bus, a transceiver, a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the transceiver, the memory and the processor are respectively connected through the bus, and when the computer program is executed by the processor, the processes of the method embodiment for controlling output data are realized, and the same technical effects can be achieved, so that repetition is avoided and redundant description is omitted.
Exemplary electronic device
In particular, referring to FIG. 4, the present application also provides an electronic device comprising a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In this application, the electronic device further includes: computer programs stored on the memory 1150 and executable on the processor 1120, which when executed by the processor 1120, implement the various processes of the method embodiments described above for controlling output data.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In this application, a bus architecture (represented by bus 1110), the bus 1110 may include any number of interconnected buses and bridges, with the bus 1110 connecting various circuits, including one or more processors, represented by the processor 1120, and memory, represented by the memory 1150.
Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such an architecture includes: industry standard architecture buses, micro-channel architecture buses, expansion buses, video electronics standards association, and peripheral component interconnect buses.
Processor 1120 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments may be implemented by instructions in the form of integrated logic circuits in hardware or software in a processor. The processor includes: general purpose processors, central processing units, network processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, complex programmable logic devices, programmable logic arrays, micro control units or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components. The methods, steps and logic blocks disclosed in the present application may be implemented or performed. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated on a single chip or located on multiple different chips.
The processor 1120 may be a microprocessor or any conventional processor. The method steps disclosed in connection with the present application may be performed directly by a hardware decoding processor or by a combination of hardware and software modules in a decoding processor. The software modules may be located in random access memory, flash memory, read only memory, programmable read only memory, erasable programmable read only memory, registers, and the like, as known in the art. The readable storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
Bus 1110 may also connect together various other circuits such as peripheral devices, voltage regulators, or power management circuits, bus interface 1140 providing an interface between bus 1110 and transceiver 1130, all of which are well known in the art. Therefore, this application will not be further described.
The transceiver 1130 may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 is configured to transmit the data processed by the processor 1120 to the other devices. Depending on the nature of the computer device, a user interface 1160 may also be provided, for example: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It should be appreciated that in this application, the memory 1150 may further include memory located remotely from the processor 1120, which may be connected to a server through a network. One or more portions of the above-described networks may be an ad hoc network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, an internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless fidelity network, and combinations of two or more of the foregoing. For example, the cellular telephone network and wireless network may be global system for mobile communications devices, code division multiple access devices, worldwide interoperability for microwave access devices, general packet radio service devices, wideband code division multiple access devices, long term evolution devices, LTE frequency division duplex devices, LTE time division duplex devices, advanced long term evolution devices, general mobile communications devices, enhanced mobile broadband devices, mass machine class communications devices, ultra-reliable low-latency communications devices, and the like.
It should be appreciated that the memory 1150 in this application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, or flash memory.
The volatile memory includes: random access memory, which serves as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronous link dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described herein includes, but is not limited to, the memory described above and any other suitable type of memory.
In this application, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an extended set thereof.
Specifically, the operating system 1151 includes various device programs, such as: a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The applications 1152 include various applications such as: and the media player and the browser are used for realizing various application services. A program for implementing the method of the present application may be included in the application 1152. The application 1152 includes: applets, objects, components, logic, data structures, and other computer apparatus-executable instructions that perform particular tasks or implement particular abstract data types.
In addition, the application further provides a computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements each process of the above-mentioned method embodiment for controlling output data, and the same technical effects can be achieved, and for avoiding repetition, a detailed description is omitted herein.
The foregoing is merely a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. The fault identification method for the concrete mixing equipment based on the multi-source data analysis is characterized by comprising the following steps of:
a sensor monitoring network is arranged, real-time monitoring is carried out on the concrete mixing equipment based on the sensor monitoring network, and a multi-source equipment operation data stream is acquired and acquired;
performing multidimensional attribute marking on the multi-source equipment operation data stream to obtain equipment multidimensional operation data attribute information;
classifying and integrating the multi-source equipment operation data stream according to the multi-dimensional operation data attribute information of the equipment to obtain a multi-dimensional equipment operation attribute data stream;
acquiring a device performance factor index set, wherein the device performance factor index set comprises motor power, stirring effect, powder conveying, gas path metering, stirring noise and operation energy consumption;
setting an operation data analysis multichannel based on the equipment performance factor index set;
mapping the multi-dimensional equipment operation attribute data stream to the operation data analysis multi-channel to perform association analysis and identification, so as to obtain a multi-dimensional equipment operation abnormal data stream;
and diagnosing and analyzing the abnormal operation data flow of the multidimensional equipment based on the fault analysis network of the stirring equipment, and outputting fault diagnosis information of the concrete stirring equipment.
2. The method of claim 1, wherein obtaining device multidimensional operational data attribute information comprises:
acquiring a data attribute identification rule, wherein the data attribute identification rule comprises an application type identification rule and a data format identification rule;
classifying the attributes of the multi-source equipment operation data stream based on the application type identification rule to obtain equipment operation data type information;
constructing a format attribute classifier through the data format identification rule, wherein the format attribute classifier comprises generation time, a data hierarchy and a storage format;
classifying and identifying the multi-source equipment operation data stream according to the format attribute classifier to obtain equipment operation data format attribute information;
and combining and generating the equipment multidimensional operation data attribute information based on the equipment operation data type information and the equipment operation data format attribute information.
3. The method of claim 1, wherein the deployment sensor monitoring network comprises:
acquiring structural dimension parameter information of the concrete mixing equipment, and performing visual modeling by using the structural dimension parameter information to generate a three-dimensional model of the concrete mixing equipment;
Carrying out structural facility segmentation on the three-dimensional model of the concrete stirring equipment according to the application function to obtain a functional structural facility set of the stirring equipment;
respectively analyzing the data to be monitored for the facility sets of the stirring equipment functional structure, and determining a facility target monitoring data attribute set;
and carrying out sensor layout based on the facility target monitoring data attribute set, and determining the sensor monitoring network.
4. The method of claim 1, wherein mapping the multi-dimensional device operational attribute data stream into the operational data analysis multi-channel for associative analysis identification comprises:
extracting the performance factor index of the equipment by analyzing the operation data and multiple channels to obtain the target processing performance data index of the channels;
respectively carrying out associated data attribute analysis based on the channel target processing performance data indexes, and marking to obtain a channel associated operation data attribute set;
drawing a correlation network according to the channel correlation operation data attribute set to generate a channel correlation data attribute network;
and mapping and splitting the multi-dimensional equipment operation attribute data stream and the operation data analysis multi-channel based on the channel associated data attribute network.
5. The method of claim 1, wherein the deriving the multi-dimensional device operational anomaly data stream comprises:
acquiring an equipment abnormal operation sample data set through a data mining technology;
analyzing multiple channels based on the operation data, and determining an abnormal data tag set;
classifying and labeling the equipment abnormal operation sample data set according to the abnormal data label set to obtain an abnormal operation characteristic training sample data set;
model training and verification are carried out based on the abnormal operation feature training sample data set, and an equipment abnormal operation data classifier is generated;
and identifying the multi-dimensional equipment operation attribute data stream based on the equipment abnormal operation data classifier to obtain the multi-dimensional equipment operation abnormal data stream.
6. The method of claim 5, wherein generating a device abnormal operation data classifier comprises:
proportional division is carried out on the abnormal operation characteristic training sample data set to obtain a sample training set, a sample verification set and a sample test set;
performing network model supervision training based on the sample training set to obtain a basic abnormal operation data classifier;
and verifying and testing the basic abnormal operation data classifier based on the sample verification set and the sample test set until the model accuracy reaches a preset requirement, and generating the equipment abnormal operation data classifier.
7. The method of claim 1, wherein the method comprises:
obtaining a device fault factor set, wherein the device fault factor set comprises a fault type, a fault grade and a generation reason;
extracting fault attributes of the equipment fault factor set to obtain an equipment operation fault attribute set;
constructing an equipment operation fault knowledge graph based on the equipment operation fault attribute set;
and constructing a concrete mixing equipment fault library, and training the equipment operation fault knowledge graph based on the concrete mixing equipment fault library to obtain the mixing equipment fault analysis network.
8. A concrete mixing plant fault identification system based on multi-source data analysis, the system comprising:
the operation data stream acquisition module is used for arranging a sensor monitoring network, monitoring the concrete mixing equipment in real time based on the sensor monitoring network, and acquiring multi-source equipment operation data streams;
the multi-dimensional attribute marking module is used for carrying out multi-dimensional attribute marking on the multi-source equipment operation data stream to obtain equipment multi-dimensional operation data attribute information;
the data multidimensional attribute marking module is used for classifying and integrating the multi-source equipment operation data stream according to the equipment multidimensional operation data attribute information to obtain a multidimensional equipment operation attribute data stream;
The factor index set acquisition module is used for acquiring a device performance factor index set, wherein the device performance factor index set comprises motor power, stirring effect, powder conveying, gas path metering, stirring noise and operation energy consumption;
the analysis multichannel setting module is used for setting operation data analysis multichannel based on the equipment performance factor index set;
the association analysis and identification module is used for mapping the multi-dimensional equipment operation attribute data stream to the operation data analysis multi-channel to carry out association analysis and identification so as to obtain a multi-dimensional equipment operation abnormal data stream;
and the fault diagnosis and analysis module is used for carrying out diagnosis and analysis on the operation abnormal data flow of the multidimensional equipment based on the stirring equipment fault analysis network and outputting fault diagnosis information of the concrete stirring equipment.
9. A concrete mixing plant fault identification electronic device based on multi-source data analysis, comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that the computer program when executed by the processor implements the steps in the method according to any one of claims 1-7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311282202.5A CN117312794A (en) | 2023-10-07 | 2023-10-07 | Concrete mixing equipment fault identification method and system based on multi-source data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311282202.5A CN117312794A (en) | 2023-10-07 | 2023-10-07 | Concrete mixing equipment fault identification method and system based on multi-source data analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117312794A true CN117312794A (en) | 2023-12-29 |
Family
ID=89280756
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311282202.5A Withdrawn CN117312794A (en) | 2023-10-07 | 2023-10-07 | Concrete mixing equipment fault identification method and system based on multi-source data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117312794A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117671393A (en) * | 2024-01-31 | 2024-03-08 | 江苏奥派电气科技有限公司 | Fault monitoring method and system for electrical mechanical equipment |
-
2023
- 2023-10-07 CN CN202311282202.5A patent/CN117312794A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117671393A (en) * | 2024-01-31 | 2024-03-08 | 江苏奥派电气科技有限公司 | Fault monitoring method and system for electrical mechanical equipment |
| CN117671393B (en) * | 2024-01-31 | 2024-04-26 | 江苏奥派电气科技有限公司 | Fault monitoring method and system for electrical mechanical equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116842768B (en) | Steel structural member production process optimization method and system | |
| CN117312794A (en) | Concrete mixing equipment fault identification method and system based on multi-source data analysis | |
| CN109238455B (en) | A kind of characteristic of rotating machines vibration signal monitoring method and system based on graph theory | |
| CN114997605B (en) | Intelligent planning method and system for degradable resin production route | |
| CN113506082B (en) | VR-based supervision method and system for digital factory production line | |
| CN117595504A (en) | Intelligent monitoring and early warning method for power grid running state | |
| CN115618269A (en) | Big data analysis method and system based on industrial sensor production | |
| CN116932384A (en) | Software defect prediction method based on feature fusion and feature selection | |
| CN117671303B (en) | Stereoscopic ranging hidden danger early warning method and system based on twin network | |
| CN113420722A (en) | Emergency linkage method and system for airport security management platform | |
| CN114268365B (en) | Communication optical cable intelligent early warning method and system based on visualization technology | |
| CN117226846A (en) | Control method and system for substation equipment maintenance robot | |
| CN115481941B (en) | Multifunctional area combined intelligent security management method and system | |
| CN115242684B (en) | Full-link pressure measurement method and device, computer equipment and storage medium | |
| US10372849B2 (en) | Performing and communicating sheet metal simulations employing a combination of factors | |
| CN117591283B (en) | Cloud cutting equipment management method and system based on cross-platform data fusion | |
| CN117167366B (en) | Automatic flow control method and system for hydraulic distributor | |
| CN110971483B (en) | Pressure testing method and device and computer system | |
| CN117388374B (en) | Method and system for detecting strength of concrete for building | |
| CN114138581A (en) | Mainboard test method and system | |
| CN117953148A (en) | Substation three-dimensional model optimization method and system based on operation and detection data fusion | |
| CN117593670B (en) | Method and system for intelligent investigation and monitoring of forest resources | |
| CN117633592A (en) | Intelligent monitoring disc system of new energy power station | |
| CN114926154B (en) | Protection switching method and system for multi-scene data identification | |
| CN117009909B (en) | Automatic fault reporting model construction method, automatic fault reporting method and device |
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 | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20231229 |
|
| WW01 | Invention patent application withdrawn after publication |