CN116399630A - Operation monitoring management method and system based on equipment working condition - Google Patents
Operation monitoring management method and system based on equipment working condition Download PDFInfo
- Publication number
- CN116399630A CN116399630A CN202310660640.4A CN202310660640A CN116399630A CN 116399630 A CN116399630 A CN 116399630A CN 202310660640 A CN202310660640 A CN 202310660640A CN 116399630 A CN116399630 A CN 116399630A
- Authority
- CN
- China
- Prior art keywords
- equipment
- monitoring
- data
- result
- monitored
- 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.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 322
- 238000007726 management method Methods 0.000 title claims abstract description 77
- 238000012795 verification Methods 0.000 claims abstract description 75
- 230000010354 integration Effects 0.000 claims abstract description 59
- 230000002159 abnormal effect Effects 0.000 claims abstract description 47
- 230000003993 interaction Effects 0.000 claims abstract description 29
- 238000000034 method Methods 0.000 claims abstract description 26
- 238000003745 diagnosis Methods 0.000 claims description 40
- 230000005856 abnormality Effects 0.000 claims description 20
- 238000012216 screening Methods 0.000 claims description 20
- 230000004807 localization Effects 0.000 claims description 13
- 238000003032 molecular docking Methods 0.000 claims description 10
- 238000013507 mapping Methods 0.000 claims description 9
- 238000001514 detection method Methods 0.000 claims description 7
- 210000001503 joint Anatomy 0.000 claims description 6
- 238000011156 evaluation Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 4
- 238000003860 storage Methods 0.000 claims description 3
- 238000004590 computer program Methods 0.000 claims description 2
- 238000009826 distribution Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 21
- 238000012545 processing Methods 0.000 abstract description 3
- 238000004458 analytical method Methods 0.000 description 14
- 238000013528 artificial neural network Methods 0.000 description 6
- 210000002569 neuron Anatomy 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000007613 environmental effect Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M99/00—Subject matter not provided for in other groups of this subclass
- G01M99/008—Subject matter not provided for in other groups of this subclass by doing functionality tests
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses an operation monitoring management method and system based on equipment working conditions, and relates to the field of data processing, wherein the method comprises the following steps: executing data reading through the data interaction equipment, and collecting control information of equipment to be monitored; monitoring equipment through monitoring diagnostic equipment to generate equipment monitoring data; generating a data integration result; performing data exception verification on the data integration result and the control information to generate a data exception verification result; and carrying out abnormal integration on the data abnormal verification result, generating real-time early warning information and predictive early warning information, and carrying out operation monitoring management on equipment to be monitored through the real-time early warning information and the predictive early warning information. The technical problems of low monitoring management accuracy aiming at mechanical equipment and poor monitoring management effect of the mechanical equipment in the prior art are solved. The technical effects of improving the monitoring management accuracy of the mechanical equipment and the monitoring management quality of the mechanical equipment are achieved.
Description
Technical Field
The invention relates to the field of data processing, in particular to an operation monitoring management method and system based on equipment working conditions.
Background
The mechanical equipment is widely applied to the production and life of people, and greatly improves the quality of the production and life of people. However, during actual operation, various malfunctions of the mechanical apparatus inevitably occur. The traditional mechanical equipment monitoring management mode mainly depends on manual work, and has the defects of low monitoring management timeliness, poor fault detection effect and the like. The research design of the method for optimizing, monitoring and managing the mechanical equipment has very important practical significance.
In the prior art, the monitoring management accuracy of the mechanical equipment is low, and the technical problem of poor monitoring management effect of the mechanical equipment is caused.
Disclosure of Invention
The application provides an operation monitoring management method and system based on equipment working conditions. The technical problems of low monitoring management accuracy aiming at mechanical equipment and poor monitoring management effect of the mechanical equipment in the prior art are solved. The technical effects of improving the monitoring management accuracy of the mechanical equipment and the monitoring management quality of the mechanical equipment are achieved.
In view of the above problems, the present application provides an operation monitoring management method and system based on the working conditions of equipment.
In a first aspect, the present application provides an operation monitoring management method based on a device working condition, where the method is applied to an operation monitoring management system based on a device working condition, and the method includes: setting equipment connection parameters of equipment to be monitored, and butting the data interaction equipment with the equipment to be monitored; when the detection of the completion of the butt joint with the equipment to be monitored is finished, executing data reading through the data interaction equipment, and collecting control information of the equipment to be monitored; the monitoring and diagnosing equipment is arranged, the arrangement position coordinates are recorded, equipment monitoring is carried out through the monitoring and diagnosing equipment, and equipment monitoring data are generated; performing data integration of the equipment monitoring data based on the layout position coordinates to generate a data integration result; performing data exception verification on the data integration result and the control information to generate a data exception verification result; and carrying out abnormal integration on the data abnormal verification result, generating real-time early warning information and predictive early warning information, and carrying out operation monitoring management on the equipment to be monitored through the real-time early warning information and the predictive early warning information.
In a second aspect, the present application further provides an operation monitoring management system based on a device working condition, where the system includes: the equipment docking module is used for setting equipment connection parameters of equipment to be monitored and docking the data interaction equipment with the equipment to be monitored; the data reading module is used for executing data reading through the data interaction equipment when the data reading module detects that the docking with the equipment to be monitored is completed, and collecting control information of the equipment to be monitored; the equipment monitoring module is used for distributing the monitoring diagnosis equipment, recording the distribution position coordinates, monitoring equipment through the monitoring diagnosis equipment and generating equipment monitoring data; the data integration module is used for integrating the data of the equipment monitoring data based on the layout position coordinates and generating a data integration result; the data anomaly verification module is used for carrying out data anomaly verification on the data integration result and the control information to generate a data anomaly verification result; and the monitoring management module is used for carrying out abnormal integration on the data abnormal verification result, generating real-time early warning information and predictive early warning information, and carrying out operation monitoring management on the equipment to be monitored through the real-time early warning information and the predictive early warning information.
In a third aspect, the present application further provides an electronic device, including: a memory for storing executable instructions; and the processor is used for realizing the operation monitoring management method based on the equipment working condition when executing the executable instructions stored in the memory.
In a fourth aspect, the present application further provides a computer readable storage medium storing a computer program, where the program when executed by a processor implements an operation monitoring management method based on a device operating mode provided by the present application.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method comprises the steps of reading data of equipment to be monitored through data interaction equipment to obtain control information of the equipment to be monitored; monitoring equipment to be monitored through monitoring and diagnosing equipment to generate equipment monitoring data; performing data integration on equipment monitoring data based on the layout position coordinates of the monitoring and diagnosis equipment to generate a data integration result; generating a data exception verification result by carrying out data exception verification on the data integration result and the control information; and carrying out abnormal integration on the data abnormal verification result to generate real-time early warning information and predictive early warning information, and carrying out operation monitoring management on equipment to be monitored through the real-time early warning information and the predictive early warning information. The technical effects of improving the monitoring management accuracy of the mechanical equipment and the monitoring management quality of the mechanical equipment are achieved.
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
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly described below. It is apparent that the figures in the following description relate only to some embodiments of the present disclosure and are not limiting of the present disclosure.
FIG. 1 is a flow chart of an operation monitoring and managing method based on the working condition of equipment;
FIG. 2 is a schematic flow chart of a monitoring and diagnosing device arranged in the operation monitoring and managing method based on the working condition of the device;
FIG. 3 is a schematic structural diagram of an operation monitoring management system based on the working condition of the device;
fig. 4 is a schematic structural diagram of an exemplary electronic device of the present application.
Reference numerals illustrate: the device comprises a device docking module 11, a data reading module 12, a device monitoring module 13, a data integration module 14, a data abnormality verification module 15, a monitoring management module 16, a processor 31, a memory 32, an input device 33 and an output device 34.
Detailed Description
The application provides an operation monitoring management method and system based on equipment working conditions. The technical problems of low monitoring management accuracy aiming at mechanical equipment and poor monitoring management effect of the mechanical equipment in the prior art are solved. The technical effects of improving the monitoring management accuracy of the mechanical equipment and the monitoring management quality of the mechanical equipment are achieved.
Example 1
Referring to fig. 1, the present application provides an operation monitoring management method based on a device working condition, where the method is applied to an operation monitoring management system based on a device working condition, the system is in communication connection with a monitoring diagnosis device and a data interaction device, and the method specifically includes the following steps:
step S100: setting equipment connection parameters of equipment to be monitored, and butting the data interaction equipment with the equipment to be monitored;
step S200: when the detection of the completion of the butt joint with the equipment to be monitored is finished, executing data reading through the data interaction equipment, and collecting control information of the equipment to be monitored;
specifically, device connection parameters are set based on the device to be monitored, and the data interaction device is in butt joint with the device to be monitored according to the device connection parameters. And when the equipment to be monitored is in butt joint with the data interaction equipment, the data of the equipment to be monitored is read through the data interaction equipment, and the control information of the equipment to be monitored is obtained. The equipment to be monitored can be any mechanical equipment which is used for intelligent monitoring management by using the operation monitoring management system based on the equipment working condition. The equipment connection parameters comprise interface model parameters and interface size parameters of equipment to be monitored. The data interaction device can be a data acquisition device used for acquiring control parameters of the device to be monitored in the prior art. The data interaction device comprises a data acquisition end, a data transmission end and a data storage end. When the equipment to be monitored is in butt joint with the data interaction equipment, the data acquisition end of the data interaction equipment reads control parameters of the equipment to be monitored, control information of the equipment to be monitored is obtained, and the control information of the equipment to be monitored is transmitted to the data storage end of the data interaction equipment through the data transmission end. The control information comprises a plurality of pieces of real-time working parameter information such as real-time working voltage, real-time working current and the like of equipment to be monitored. The technical effects of data reading of the equipment to be monitored through the data interaction equipment, obtaining the control information of the equipment to be monitored and providing data support for monitoring management of the subsequent equipment to be monitored are achieved.
Step S300: the monitoring and diagnosing equipment is arranged, the arrangement position coordinates are recorded, equipment monitoring is carried out through the monitoring and diagnosing equipment, and equipment monitoring data are generated;
further, as shown in fig. 2, step S300 of the present application further includes:
step S310: acquiring monitoring demand information of equipment to be monitored, and analyzing monitoring accuracy through the monitoring demand information to determine the equipment number of the monitoring diagnosis equipment;
step S320: constructing a device interval angle mapping constraint set;
step S330: matching the equipment interval angle mapping constraint set according to the number of the equipment to obtain a constraint interval angle;
step S340: and arranging the monitoring and diagnosis equipment according to the constraint interval angle and the equipment number.
Specifically, the monitoring demand information is set based on the device to be monitored. The monitoring demand information comprises a plurality of monitoring indexes such as vibration, temperature, noise, motor rotating speed and the like of equipment to be monitored, which are preset and determined. And each monitoring index has a monitoring precision identifier. Further, the number of devices monitoring the diagnostic device is determined by performing monitoring accuracy analysis on the monitoring demand information. Preferably, the number of devices used in the present application to monitor and diagnose the device is 2. That is, the monitoring and diagnosing apparatus includes a first monitoring and diagnosing apparatus and a second monitoring and diagnosing apparatus. And, the first monitoring and diagnosing device and the second monitoring and diagnosing device each comprise a plurality of monitoring sensors such as a vibration sensor, a temperature sensor, a noise sensor, a motor rotating speed sensor and the like. The number of the plurality of monitoring sensors in the first monitoring diagnostic apparatus and the second monitoring diagnostic apparatus is related to the monitoring accuracy of the monitoring demand information. The more the monitoring indexes in the monitoring demand information are, the more the number of the monitoring sensors of the corresponding first monitoring diagnosis equipment and the second monitoring diagnosis equipment are. And the higher the monitoring precision mark in the monitoring demand information is, the larger the monitoring demand precision of the corresponding monitoring index is, and the better the quality of the monitoring sensor of the corresponding monitoring index is.
Further, the number of the devices is subjected to arrangement interval angle matching through the device interval angle mapping constraint set, and constraint interval angles are obtained. And then, arranging monitoring and diagnosing equipment based on the constraint interval angle and the equipment number, recording the arrangement position coordinates, carrying out equipment monitoring on equipment to be monitored through the monitoring and diagnosing equipment, and generating equipment monitoring data. The device interval angle mapping constraint set comprises a plurality of preset device numbers and a plurality of preset constraint interval angles, wherein the preset device number and the preset constraint interval angles are preset and determined. Each preset constraint interval angle comprises layout interval angle information of monitoring and diagnosing equipment corresponding to the number of each preset equipment. The constraint interval angle comprises arrangement interval angle information between the first monitoring diagnosis equipment and the second monitoring diagnosis equipment, wherein the number of the first monitoring diagnosis equipment and the second monitoring diagnosis equipment corresponds to the number of the equipment. The arrangement position coordinates comprise first arrangement position coordinates and second arrangement position coordinates. The first layout position coordinate and the second layout position coordinate comprise layout position coordinate information corresponding to the first monitoring and diagnosis equipment and the second monitoring and diagnosis equipment. The device monitoring data comprises first device monitoring data and second device monitoring data. The first equipment monitoring data comprise a plurality of monitoring index data information such as a plurality of real-time vibration parameters, a plurality of real-time temperature parameters, a plurality of real-time noise parameters, a plurality of real-time motor rotating speeds and the like of equipment to be monitored, which are acquired by the first monitoring diagnosis equipment. The second equipment monitoring data comprise a plurality of monitoring index data information such as a plurality of real-time vibration parameters, a plurality of real-time temperature parameters, a plurality of real-time noise parameters, a plurality of real-time motor rotating speeds and the like of the equipment to be monitored, which are acquired by the second monitoring diagnosis equipment. The technical effects of realizing the arrangement of adaptive and reasonable monitoring and diagnosis equipment through the analysis of the monitoring demand of the equipment to be monitored, thereby improving the comprehensiveness and the accuracy of monitoring the equipment to be monitored are achieved.
Step S400: performing data integration of the equipment monitoring data based on the layout position coordinates to generate a data integration result;
further, step S400 of the present application further includes:
step S410: acquiring a first sound monitoring result of a first monitoring and diagnosing device and a second sound monitoring result of a second monitoring and diagnosing device, wherein the first monitoring and diagnosing device and the second monitoring and diagnosing device are arranged monitoring and diagnosing devices;
step S420: setting a noise constraint intensity threshold based on device position coordinates of the first monitoring diagnostic device and the second monitoring diagnostic device;
step S430: performing similarity matching of noise rules on the first sound monitoring result and the second sound monitoring result through the noise intensity constraint threshold value to obtain a similarity matching result;
specifically, a plurality of real-time noise parameters are extracted from the first equipment monitoring data, a first sound monitoring result is obtained, and noise law analysis is performed on the first sound monitoring result to obtain a first noise law. And similarly, extracting a plurality of real-time noise parameters from the second equipment monitoring data to obtain a second noise monitoring result, and analyzing the noise rule of the second noise monitoring result to obtain a second noise rule. Wherein the first sound monitoring result comprises a plurality of real-time noise parameters in the first device monitoring data. The first noise rule comprises noise change trend information corresponding to the first sound monitoring result. The second sound monitoring result includes a plurality of real-time noise parameters in the second device monitoring data. The second noise rule comprises noise change trend information corresponding to the second noise monitoring result.
Further, based on the noise intensity constraint threshold, the first noise rule and the second noise rule, performing noise rule similarity matching on the first sound monitoring result and the second sound monitoring result to obtain a similarity matching result. The noise constraint intensity threshold comprises noise intensity range information corresponding to a first layout position coordinate and noise intensity range information corresponding to a second layout position coordinate, which are preset and determined by the operation monitoring management system based on equipment working conditions. For example, when the noise intensity range information corresponding to the first layout position coordinate is determined, the operation monitoring management system based on the equipment working condition is connected, and when the equipment to be monitored works normally, the noise intensity parameters corresponding to the first layout position coordinate are subjected to historical data acquisition, so that a plurality of first coordinate noise intensity parameters are obtained. Each first coordinate noise intensity parameter comprises a historical noise intensity parameter corresponding to the first layout position coordinate when the equipment to be monitored works normally. And carrying out the maximum selection based on the plurality of first coordinate noise intensity parameters to obtain noise intensity range information corresponding to the first layout position coordinates. The noise intensity range information corresponding to the first layout position coordinates comprises the maximum value and the minimum value of a plurality of first coordinate noise intensity parameters. The similarity matching result comprises a plurality of similar noises in the first sound monitoring result and the second sound monitoring result. Illustratively, when the first sound monitoring result and the second sound monitoring result are similarly matched in terms of noise law, the first sound monitoring result includes the real-time noise parameter a 1 . The second sound monitoring result comprises a real-time noise parameter B 1 . The first noise rule and the second noise rule indicate the real-time noise parameter A 1 And real-time noise parameter B 1 The noise variation trend of (a) is the same. And, real-time noise parameter A 1 Real-time noise parameter B 1 Satisfying the noise intensity constraint threshold, the similar noise in the similar matching result comprises a real-time noise parameter A 1 Real-time noise parameter B 1 . The first sound monitoring result and the second sound monitoring result are achieved through the noise intensity constraint threshold, the first noise rule and the second noise ruleAnd performing similar matching of noise rules on the sound monitoring result to obtain an accurate similar matching result, thereby improving the technical effect of reliability of noise screening of the equipment monitoring data.
Step S440: performing sound source localization on the similarity matching result to obtain a screening sound monitoring result;
further, step S440 of the present application further includes:
step S441: performing sound source localization on similar noise monitored by the first monitoring diagnosis equipment and the second monitoring diagnosis equipment in the similar matching result to obtain an initial sound source localization result;
step S442: judging whether a sound source is positioned in the equipment of the equipment to be monitored or not based on the initial sound source positioning result;
Step S443: when judging that the device is not in the device, rejecting corresponding sound information from the first sound monitoring result and the second sound monitoring result;
step S444: when the device is judged to be in the device, performing component positioning according to the internal component composition position of the device to be monitored and the initial sound source positioning result, and performing component identification on corresponding sound information;
step S445: and obtaining the screening sound monitoring result according to the eliminating result and the part identification result.
Step S450: and generating the data integration result through the screening sound monitoring result.
Specifically, a plurality of similar noises in the similar matching result are sequentially set as first similar noises, and sound source localization is carried out on the first similar noises to obtain an initial sound source localization result. The first similar noise is each similar noise in the similar matching result in turn. The initial sound source localization result includes sound source location information corresponding to the first similar noise. Further, based on the initial sound source localization result, it is determined whether the sound source of the first similar noise is inside the device to be monitored. If the sound source of the first similar noise is not in the equipment of the equipment to be monitored, the first similar noise is removed from the first sound monitoring result and the second sound monitoring result, and a removal result is obtained. If the sound source of the first similar noise is in the equipment of the equipment to be monitored, performing component positioning and component identification based on the initial sound source positioning result and the first similar noise to obtain a component identification result, and combining the rejection result to obtain a screening sound monitoring result. And carrying out data updating on the equipment monitoring data according to the screening sound monitoring result to obtain a data integration result.
The eliminating results comprise a first sound monitoring result and a second sound monitoring result after deleting similar noise of which the sound source is not in the equipment of the equipment to be monitored. The component identification result comprises the internal component composition position of the equipment to be monitored corresponding to similar noise of the equipment to be monitored, and the sound information corresponding to the internal component composition position. The screening sound monitoring result comprises a rejecting result and a part identification result. The data integration result comprises a screening sound monitoring result and equipment monitoring data. The technical effect of generating the data integration result by integrating the data of the equipment monitoring data is achieved, so that the efficiency of carrying out abnormal verification on the data integration result is improved.
Step S500: performing data exception verification on the data integration result and the control information to generate a data exception verification result;
further, step S500 of the present application further includes:
step S510: acquiring equipment surrounding environment information of the equipment to be monitored;
step S520: initializing an anomaly ratio data set of the equipment to be monitored based on the control information and the equipment surrounding environment information;
Step S530: and performing data exception verification of the data integration result on the basis of the initialized exception ratio.
Specifically, environmental parameter acquisition is performed based on equipment to be monitored, and surrounding environmental information of the equipment is obtained. The equipment surrounding environment information comprises real-time environment temperature parameters, real-time environment noise parameters and real-time environment humidity parameters of equipment to be monitored. Further, based on the control information and the equipment surrounding environment information, initializing an anomaly ratio data set to obtain an anomaly ratio data set after initialization, and performing anomaly detection on a data integration result according to the anomaly ratio data set after initialization to generate a data anomaly verification result. Wherein the anomaly ratio data set comprises a plurality of sets of anomaly ratio data of the device to be monitored. Each group of abnormal comparison data comprises historical data abnormal verification results corresponding to historical data integration results of the equipment to be monitored under the historical control information and the surrounding environment information of the historical equipment. That is, each set of anomaly comparison data includes historical control information of the device to be monitored, historical device surrounding environment information, historical data integration results, and historical data anomaly verification results. The historical data anomaly verification result comprises anomaly data information in the historical data integration result. Illustratively, when initializing the anomaly comparison data set, the control information and the environmental information around the device are used as input information, the anomaly comparison data set is input, and the anomaly comparison data set is used for carrying out anomaly comparison data matching on the control information and the environmental information around the device, so that the anomaly comparison data set with the initialized anomaly comparison data set is obtained. The initialized abnormal comparison data set comprises abnormal comparison data sets corresponding to control information and equipment surrounding environment information. The data anomaly verification result comprises anomaly data information in a data integration result. The technical effects of performing anomaly detection on the data integration result through the initialized anomaly ratio data set and generating an accurate data anomaly verification result are achieved, so that the monitoring management quality of equipment is improved.
Step S600: and carrying out abnormal integration on the data abnormal verification result, generating real-time early warning information and predictive early warning information, and carrying out operation monitoring management on the equipment to be monitored through the real-time early warning information and the predictive early warning information.
Further, step S600 of the present application further includes:
step S610: setting a plurality of monitoring windows of the equipment to be monitored under the same working condition;
step S620: determining an operation protection time node, and screening window data of the plurality of monitoring windows through the operation protection time node to obtain a target monitoring window;
step S630: and integrating the data abnormality verification result in the target monitoring window to generate the real-time early warning information and the predictive early warning information.
Specifically, window data screening is conducted on the plurality of monitoring windows through the operation protection time node, and the target monitoring window is obtained. And based on the target monitoring window, matching the data abnormality verification result to obtain a window abnormality verification result. And then, generating real-time early warning information and predictive early warning information based on the window abnormality verification result. The monitoring windows comprise a plurality of monitoring time intervals of the equipment to be monitored, which are preset and determined under the same working condition. And, a plurality of monitoring time intervals are inconsistent. The operation protection time node comprises operation protection time interval information of equipment to be monitored, which is preset and determined. The target monitoring window includes a plurality of monitoring windows within the run-time protection node. The window abnormity verification result comprises a target monitoring window and a data abnormity verification result corresponding to the target monitoring window. The real-time early warning information includes real-time early warning level information. The predictive early warning information includes predictive early warning level information.
Illustratively, when generating real-time early warning information and predictive early warning information, big data query is performed based on the window abnormality verification result, and multiple groups of construction data are obtained. Each group of construction data comprises a history window abnormal verification result, and history real-time early warning information and history prediction early warning information corresponding to the history window abnormal verification result. The random 70% of the data information in the plurality of sets of build data is divided into training data sets. Random 30% of the data information in the plurality of sets of build data is divided into test data sets. Based on the BP neural network, cross supervision training is carried out on the training data set, and an early warning analysis model is obtained. And taking the test data set as input information, inputting the input information into the early warning analysis model, and updating parameters of the early warning analysis model through the test data set. The BP neural network is a multi-layer feedforward neural network trained according to an error back propagation algorithm. The BP neural network comprises an input layer, a plurality of layers of neurons and an output layer. The BP neural network can perform forward calculation and backward calculation. When calculating in the forward direction, the input information is processed layer by layer from the input layer through a plurality of layers of neurons and is turned to the output layer, and the state of each layer of neurons only affects the state of the next layer of neurons. If the expected output cannot be obtained at the output layer, the reverse calculation is carried out, the error signal is returned along the original connecting path, and the weight of each neuron is modified to minimize the error signal. The early warning analysis model accords with the BP neural network, namely, the early warning analysis model comprises an input layer, an hidden layer and an output layer. And taking the window abnormality verification result as input information, inputting the input information into an early warning analysis model, and carrying out real-time early warning grade assessment and early warning grade prediction identification on the window abnormality verification result through the early warning analysis model to obtain real-time early warning information and early warning prediction information. The technical effects of generating real-time early warning information and predicting early warning information by carrying out abnormal integration on the data abnormal verification result are achieved, and therefore the operation monitoring management quality of equipment to be monitored is improved.
Further, step S630 of the present application further includes:
step S631: acquiring data abnormality verification results of the equipment to be monitored under a plurality of working conditions;
step S632: carrying out comprehensive abnormal evaluation through data abnormal verification results under a plurality of working conditions to generate early warning adjustment information;
step S633: and adjusting the early warning level of the real-time early warning information and the predicted early warning information based on the early warning adjustment information.
Specifically, the operation monitoring management system based on the equipment working conditions is connected, historical data query is carried out on equipment to be monitored, and data abnormality verification results of the equipment to be monitored under a plurality of working conditions are obtained. The data abnormality verification results under the multiple working conditions comprise multiple historical data abnormality verification results corresponding to multiple historical working conditions of the equipment to be monitored. And then, carrying out comprehensive abnormal evaluation on the window abnormal verification result through the data abnormal verification results under a plurality of working conditions, generating early warning adjustment information, and carrying out early warning level adjustment on the real-time early warning information and the predicted early warning information according to the early warning adjustment information. The frequency characteristic analysis is performed on the window abnormal verification result based on the data abnormal verification results under a plurality of working conditions, and abnormal data characteristic frequency is obtained. The abnormal data characteristic frequency comprises frequency parameters of occurrence of window abnormal verification results in data abnormal verification results under a plurality of working conditions. And calculating the ratio of the characteristic frequency of the abnormal data to a preset and determined characteristic frequency threshold of the abnormal data to obtain an abnormal characteristic frequency ratio. The abnormal characteristic frequency ratio includes ratio information between an abnormal data characteristic frequency and an abnormal data characteristic frequency threshold. And outputting the abnormal characteristic frequency ratio as early warning adjustment information. And multiplying the early warning adjustment information with the real-time early warning grade information in the real-time early warning information to obtain the real-time early warning adjustment information. And multiplying the early warning adjustment information and the predicted early warning grade information in the predicted early warning information to obtain the predicted early warning adjustment information. The technical effects of adaptively adjusting the early warning level of the real-time early warning information and the predicted early warning information according to the data abnormality verification results under a plurality of working conditions are achieved, and the early warning accuracy of equipment to be monitored is improved.
In summary, the operation monitoring management method based on the equipment working condition provided by the application has the following technical effects:
1. the method comprises the steps of reading data of equipment to be monitored through data interaction equipment to obtain control information of the equipment to be monitored; monitoring equipment to be monitored through monitoring and diagnosing equipment to generate equipment monitoring data; performing data integration on equipment monitoring data based on the layout position coordinates of the monitoring and diagnosis equipment to generate a data integration result; generating a data exception verification result by carrying out data exception verification on the data integration result and the control information; and carrying out abnormal integration on the data abnormal verification result to generate real-time early warning information and predictive early warning information, and carrying out operation monitoring management on equipment to be monitored through the real-time early warning information and the predictive early warning information. The technical effects of improving the monitoring management accuracy of the mechanical equipment and the monitoring management quality of the mechanical equipment are achieved.
2. Through the monitoring demand analysis of the equipment to be monitored, the adaptive and reasonable arrangement of the monitoring and diagnosis equipment is realized, so that the comprehensiveness and the accuracy of monitoring the equipment to be monitored are improved.
3. And carrying out anomaly detection on the data integration result through the initialized anomaly ratio data set to generate an accurate data anomaly verification result, thereby improving the monitoring management quality of the equipment.
Example 2
Based on the same inventive concept as the operation monitoring management method based on the equipment working condition in the foregoing embodiment, the present invention further provides an operation monitoring management system based on the equipment working condition, where the system is in communication connection with a monitoring diagnosis device and a data interaction device, please refer to fig. 3, and the system includes:
the device docking module 11 is used for setting device connection parameters of the device to be monitored and docking the data interaction device with the device to be monitored;
the data reading module 12 is configured to, when the docking with the device to be monitored is detected, execute data reading through the data interaction device, and collect control information of the device to be monitored;
the device monitoring module 13 is used for laying out the monitoring and diagnosing device, recording the position coordinates of the laying out, and carrying out device monitoring through the monitoring and diagnosing device to generate device monitoring data;
the data integration module 14 is configured to perform data integration of the device monitoring data based on the layout position coordinates, and generate a data integration result;
the data anomaly verification module 15 is configured to perform data anomaly verification on the data integration result and the control information, and generate a data anomaly verification result;
The monitoring management module 16 is configured to perform anomaly integration on the data anomaly verification result, generate real-time early warning information and predictive early warning information, and perform operation monitoring management on the device to be monitored according to the real-time early warning information and the predictive early warning information.
Further, the system further comprises:
the monitoring precision analysis module is used for obtaining monitoring requirement information of equipment to be monitored, and carrying out monitoring precision analysis through the monitoring requirement information so as to determine the equipment number of the monitoring diagnosis equipment;
the mapping constraint set construction module is used for constructing a device interval angle mapping constraint set;
the constraint interval angle obtaining module is used for matching the equipment interval angle mapping constraint set through the equipment number to obtain a constraint interval angle;
the first execution module is used for distributing the monitoring and diagnosis equipment through the constraint interval angle and the equipment number.
Further, the system further comprises:
the sound monitoring result obtaining module is used for collecting and obtaining a first sound monitoring result of a first monitoring diagnosis device and a second sound monitoring result of a second monitoring diagnosis device, wherein the first monitoring diagnosis device and the second monitoring diagnosis device are arranged monitoring diagnosis devices;
A noise constraint intensity threshold determination module for setting a noise constraint intensity threshold based on device position coordinates of the first and second monitoring diagnostic devices;
the noise similarity matching module is used for performing similarity matching of noise rules on the first sound monitoring result and the second sound monitoring result through the noise intensity constraint threshold value to obtain a similarity matching result;
the sound source positioning module is used for performing sound source positioning on the similar matching result to obtain a screening sound monitoring result;
and the second execution module is used for generating the data integration result through the screening sound monitoring result.
Further, the system further comprises:
the third execution module is used for executing sound source localization on similar noise monitored by the first monitoring diagnosis equipment and the second monitoring diagnosis equipment in the similar matching result to obtain an initial sound source localization result;
the sound source judging module is used for judging whether a sound source is positioned in the equipment of the equipment to be monitored or not based on the initial sound source positioning result;
The sound eliminating module is used for eliminating corresponding sound information from the first sound monitoring result and the second sound monitoring result when judging that the sound eliminating module is not in the equipment;
the component identification module is used for carrying out component positioning according to the internal component composition position of the equipment to be monitored and the initial sound source positioning result when judging that the equipment is positioned in the equipment, and carrying out component identification on corresponding sound information;
and the fourth execution module is used for obtaining the screening sound monitoring result according to the eliminating result and the component identification result.
Further, the system further comprises:
the environment information acquisition module is used for acquiring the surrounding environment information of the equipment to be monitored;
the initialization module is used for initializing an anomaly comparison data set of the equipment to be monitored based on the control information and the equipment surrounding environment information;
and the fifth execution module is used for executing data exception verification of the data integration result on the basis of the initialized exception ratio data set.
Further, the system further comprises:
the monitoring window setting module is used for setting a plurality of monitoring windows of the equipment to be monitored under the same working condition;
the window data screening module is used for determining an operation protection time node, and performing window data screening on the plurality of monitoring windows through the operation protection time node to obtain a target monitoring window;
and the sixth execution module is used for integrating the data abnormality verification result in the target monitoring window and generating the real-time early warning information and the predictive early warning information.
Further, the system further comprises:
the seventh execution module is used for acquiring data abnormality verification results of the equipment to be monitored under a plurality of working conditions;
the comprehensive abnormality evaluation module is used for carrying out comprehensive abnormality evaluation through data abnormality verification results under a plurality of working conditions to generate early warning adjustment information;
and the early warning level adjustment module is used for carrying out early warning level adjustment on the real-time early warning information and the predicted early warning information based on the early warning adjustment information.
The operation monitoring management system based on the equipment working condition provided by the embodiment of the invention can execute the operation monitoring management method based on the equipment working condition provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
All the included modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention.
Example 3
Fig. 4 is a schematic structural diagram of an electronic device provided in a third embodiment of the present invention, and shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. The electronic device shown in fig. 4 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention. As shown in fig. 4, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 4, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 4, by bus connection is taken as an example.
The memory 32 is used as a computer readable storage medium for storing software programs, computer executable programs and modules, such as program instructions/modules corresponding to a device condition-based operation monitoring management method in an embodiment of the present invention. The processor 31 executes various functional applications and data processing of the computer device by running software programs, instructions and modules stored in the memory 32, i.e. implements an operation monitoring management method based on the operating conditions of the device.
The application provides an operation monitoring management method based on equipment working conditions, wherein the method is applied to an operation monitoring management system based on the equipment working conditions, and the method comprises the following steps: the method comprises the steps of reading data of equipment to be monitored through data interaction equipment to obtain control information of the equipment to be monitored; monitoring equipment to be monitored through monitoring and diagnosing equipment to generate equipment monitoring data; performing data integration on equipment monitoring data based on the layout position coordinates of the monitoring and diagnosis equipment to generate a data integration result; generating a data exception verification result by carrying out data exception verification on the data integration result and the control information; and carrying out abnormal integration on the data abnormal verification result to generate real-time early warning information and predictive early warning information, and carrying out operation monitoring management on equipment to be monitored through the real-time early warning information and the predictive early warning information. The technical problems of low monitoring management accuracy aiming at mechanical equipment and poor monitoring management effect of the mechanical equipment in the prior art are solved. The technical effects of improving the monitoring management accuracy of the mechanical equipment and the monitoring management quality of the mechanical equipment are achieved.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (10)
1. An operation monitoring management method based on equipment working conditions is characterized by being applied to an operation monitoring management system, wherein the operation monitoring management system is in communication connection with monitoring diagnosis equipment and data interaction equipment, and the method comprises the following steps:
setting equipment connection parameters of equipment to be monitored, and butting the data interaction equipment with the equipment to be monitored;
when the detection of the completion of the butt joint with the equipment to be monitored is finished, executing data reading through the data interaction equipment, and collecting control information of the equipment to be monitored;
The monitoring and diagnosing equipment is arranged, the arrangement position coordinates are recorded, equipment monitoring is carried out through the monitoring and diagnosing equipment, and equipment monitoring data are generated;
performing data integration of the equipment monitoring data based on the layout position coordinates to generate a data integration result;
performing data exception verification on the data integration result and the control information to generate a data exception verification result;
and carrying out abnormal integration on the data abnormal verification result, generating real-time early warning information and predictive early warning information, and carrying out operation monitoring management on the equipment to be monitored through the real-time early warning information and the predictive early warning information.
2. The method of claim 1, wherein the method further comprises:
acquiring monitoring demand information of equipment to be monitored, and analyzing monitoring accuracy through the monitoring demand information to determine the equipment number of the monitoring diagnosis equipment;
constructing a device interval angle mapping constraint set;
matching the equipment interval angle mapping constraint set according to the number of the equipment to obtain a constraint interval angle;
and arranging the monitoring and diagnosis equipment according to the constraint interval angle and the equipment number.
3. The method of claim 1, wherein the method further comprises:
acquiring a first sound monitoring result of a first monitoring and diagnosing device and a second sound monitoring result of a second monitoring and diagnosing device, wherein the first monitoring and diagnosing device and the second monitoring and diagnosing device are arranged monitoring and diagnosing devices;
setting a noise constraint intensity threshold based on device position coordinates of the first monitoring diagnostic device and the second monitoring diagnostic device;
performing similarity matching of noise rules on the first sound monitoring result and the second sound monitoring result through the noise intensity constraint threshold value to obtain a similarity matching result;
performing sound source localization on the similarity matching result to obtain a screening sound monitoring result;
and generating the data integration result through the screening sound monitoring result.
4. The method of claim 3, wherein performing sound source localization on the similarity matching results to obtain screening sound monitoring results further comprises:
performing sound source localization on similar noise monitored by the first monitoring diagnosis equipment and the second monitoring diagnosis equipment in the similar matching result to obtain an initial sound source localization result;
Judging whether a sound source is positioned in the equipment of the equipment to be monitored or not based on the initial sound source positioning result;
when judging that the device is not in the device, rejecting corresponding sound information from the first sound monitoring result and the second sound monitoring result;
when the device is judged to be in the device, performing component positioning according to the internal component composition position of the device to be monitored and the initial sound source positioning result, and performing component identification on corresponding sound information;
and obtaining the screening sound monitoring result according to the eliminating result and the part identification result.
5. The method of claim 1, wherein the method further comprises:
acquiring equipment surrounding environment information of the equipment to be monitored;
initializing an anomaly ratio data set of the equipment to be monitored based on the control information and the equipment surrounding environment information;
and performing data exception verification of the data integration result on the basis of the initialized exception ratio.
6. The method of claim 1, wherein the method further comprises:
setting a plurality of monitoring windows of the equipment to be monitored under the same working condition;
Determining an operation protection time node, and screening window data of the plurality of monitoring windows through the operation protection time node to obtain a target monitoring window;
and integrating the data abnormality verification result in the target monitoring window to generate the real-time early warning information and the predictive early warning information.
7. The method of claim 1, wherein the method further comprises:
acquiring data abnormality verification results of the equipment to be monitored under a plurality of working conditions;
carrying out comprehensive abnormal evaluation through data abnormal verification results under a plurality of working conditions to generate early warning adjustment information;
and adjusting the early warning level of the real-time early warning information and the predicted early warning information based on the early warning adjustment information.
8. An operation monitoring management system based on equipment working conditions is characterized in that the system is in communication connection with monitoring diagnosis equipment and data interaction equipment, and the system comprises:
the equipment docking module is used for setting equipment connection parameters of equipment to be monitored and docking the data interaction equipment with the equipment to be monitored;
the data reading module is used for executing data reading through the data interaction equipment when the data reading module detects that the docking with the equipment to be monitored is completed, and collecting control information of the equipment to be monitored;
The equipment monitoring module is used for distributing the monitoring diagnosis equipment, recording the distribution position coordinates, monitoring equipment through the monitoring diagnosis equipment and generating equipment monitoring data;
the data integration module is used for integrating the data of the equipment monitoring data based on the layout position coordinates and generating a data integration result;
the data anomaly verification module is used for carrying out data anomaly verification on the data integration result and the control information to generate a data anomaly verification result;
and the monitoring management module is used for carrying out abnormal integration on the data abnormal verification result, generating real-time early warning information and predictive early warning information, and carrying out operation monitoring management on the equipment to be monitored through the real-time early warning information and the predictive early warning information.
9. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
a processor, configured to implement the device condition-based operation monitoring management method according to any one of claims 1 to 7 when executing the executable instructions stored in the memory.
10. A computer-readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements a device condition-based operation monitoring management method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310660640.4A CN116399630B (en) | 2023-06-06 | 2023-06-06 | Operation monitoring management method and system based on equipment working condition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310660640.4A CN116399630B (en) | 2023-06-06 | 2023-06-06 | Operation monitoring management method and system based on equipment working condition |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116399630A true CN116399630A (en) | 2023-07-07 |
| CN116399630B CN116399630B (en) | 2023-09-05 |
Family
ID=87012636
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310660640.4A Active CN116399630B (en) | 2023-06-06 | 2023-06-06 | Operation monitoring management method and system based on equipment working condition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116399630B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117057579A (en) * | 2023-08-31 | 2023-11-14 | 浙江万胜智通科技有限公司 | Operation maintenance method and system for distributed power distribution network |
| CN117433978A (en) * | 2023-12-15 | 2024-01-23 | 张家港市华申工业橡塑制品有限公司 | Monitoring and early warning method and system of dust remover for transmission |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114723082A (en) * | 2022-04-19 | 2022-07-08 | 镇江西门子母线有限公司 | Abnormity early warning method and system for intelligent low-voltage complete equipment |
| CN115426177A (en) * | 2022-09-01 | 2022-12-02 | 北京赛博易安科技有限公司 | Abnormal data detection method and system based on big data |
| CN115643159A (en) * | 2022-11-18 | 2023-01-24 | 奇点创新(江苏)智能科技有限公司 | Equipment abnormity early warning method and system based on edge calculation |
| CN115833400A (en) * | 2023-02-07 | 2023-03-21 | 山东盛日电力集团有限公司 | Monitoring and early warning method and system for power equipment of transformer substation |
| CN116187867A (en) * | 2023-04-27 | 2023-05-30 | 苏州上舜精密工业科技有限公司 | Intelligent transmission module production management method and system |
-
2023
- 2023-06-06 CN CN202310660640.4A patent/CN116399630B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114723082A (en) * | 2022-04-19 | 2022-07-08 | 镇江西门子母线有限公司 | Abnormity early warning method and system for intelligent low-voltage complete equipment |
| CN115426177A (en) * | 2022-09-01 | 2022-12-02 | 北京赛博易安科技有限公司 | Abnormal data detection method and system based on big data |
| CN115643159A (en) * | 2022-11-18 | 2023-01-24 | 奇点创新(江苏)智能科技有限公司 | Equipment abnormity early warning method and system based on edge calculation |
| CN115833400A (en) * | 2023-02-07 | 2023-03-21 | 山东盛日电力集团有限公司 | Monitoring and early warning method and system for power equipment of transformer substation |
| CN116187867A (en) * | 2023-04-27 | 2023-05-30 | 苏州上舜精密工业科技有限公司 | Intelligent transmission module production management method and system |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117057579A (en) * | 2023-08-31 | 2023-11-14 | 浙江万胜智通科技有限公司 | Operation maintenance method and system for distributed power distribution network |
| CN117057579B (en) * | 2023-08-31 | 2024-04-02 | 浙江万胜智通科技有限公司 | Operation maintenance method and system for distributed power distribution network |
| CN117433978A (en) * | 2023-12-15 | 2024-01-23 | 张家港市华申工业橡塑制品有限公司 | Monitoring and early warning method and system of dust remover for transmission |
| CN117433978B (en) * | 2023-12-15 | 2024-03-08 | 张家港市华申工业橡塑制品有限公司 | Monitoring and early warning method and system of dust remover for transmission |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116399630B (en) | 2023-09-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116399630B (en) | Operation monitoring management method and system based on equipment working condition | |
| JP5203984B2 (en) | Method and apparatus for analyzing and evaluating measurement data of a measurement system | |
| CN111767930A (en) | Method for detecting abnormal time series data of Internet of things and related equipment thereof | |
| US20060230313A1 (en) | Diagnostic and prognostic method and system | |
| CN112284440B (en) | Sensor data deviation self-adaptive correction method | |
| CN111459700A (en) | Method and apparatus for diagnosing device failure, diagnostic device, and storage medium | |
| CN102265227A (en) | Method and apparatus for creating state estimation models in machine condition monitoring | |
| CN113671917B (en) | Detection method, system and equipment for abnormal state of multi-modal industrial process | |
| CN111737909A (en) | Structural health monitoring data anomaly identification method based on space-time graph convolutional network | |
| US11726469B2 (en) | Error-based method for calculating a remaining useful life of an apparatus | |
| KR102321607B1 (en) | Rotating machine fault detecting apparatus and method | |
| CN113569457A (en) | Demand function model construction method and system based on digital twin | |
| CN116625683A (en) | Wind turbine generator system bearing fault identification method, system and device and electronic equipment | |
| CN117270514A (en) | Production process whole-flow fault detection method based on industrial Internet of things | |
| CN117131364B (en) | Rolling bearing process detection integration method and system | |
| CN116754022B (en) | On-line detection emergency early warning method and system for cable tunnel | |
| CN109990803A (en) | The method, apparatus of method, apparatus and the sensor processing of detection system exception | |
| CN116705272A (en) | Comprehensive evaluation method for equipment health state based on multidimensional diagnosis | |
| Wagner et al. | An overview of useful data and analyzing techniques for improved multivariate diagnostics and prognostics in condition-based maintenance | |
| Zhang | Comparison of data-driven and model-based methodologies of wind turbine fault detection with SCADA data | |
| KR20220068799A (en) | System for detecting error of automation equipment and method thereof | |
| RU2648413C1 (en) | Method of the modes control based on the neural network diagnostics of faults and technical condition of the electric-drive gas-compressor unit | |
| CN114139589A (en) | Fault diagnosis method, device, equipment and computer readable storage medium | |
| CN112380782A (en) | Rotating equipment fault prediction method based on mixed indexes and neural network | |
| CN114626162B (en) | Quantitative recognition method for loss degree of contact ball bearing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |