CN112200327B - MES equipment maintenance early warning method and system - Google Patents
MES equipment maintenance early warning method and system Download PDFInfo
- Publication number
- CN112200327B CN112200327B CN202011095562.0A CN202011095562A CN112200327B CN 112200327 B CN112200327 B CN 112200327B CN 202011095562 A CN202011095562 A CN 202011095562A CN 112200327 B CN112200327 B CN 112200327B
- Authority
- CN
- China
- Prior art keywords
- equipment
- early warning
- maintenance
- sub
- criterion layer
- 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.)
- Active
Links
- 238000012423 maintenance Methods 0.000 title claims abstract description 141
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000004458 analytical method Methods 0.000 claims abstract description 27
- 238000011156 evaluation Methods 0.000 claims abstract description 20
- 238000005259 measurement Methods 0.000 claims abstract description 8
- 230000036541 health Effects 0.000 claims description 37
- 238000007726 management method Methods 0.000 claims description 22
- 238000004519 manufacturing process Methods 0.000 claims description 15
- 238000012549 training Methods 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000013213 extrapolation Methods 0.000 claims description 11
- 238000013527 convolutional neural network Methods 0.000 claims description 9
- 238000012937 correction Methods 0.000 claims description 8
- 238000000605 extraction Methods 0.000 claims description 8
- 238000003064 k means clustering Methods 0.000 claims description 6
- 230000015556 catabolic process Effects 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 4
- 238000006731 degradation reaction Methods 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 description 33
- 238000012545 processing Methods 0.000 description 7
- 238000000513 principal component analysis Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 238000012360 testing method Methods 0.000 description 5
- 230000009466 transformation Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 238000010606 normalization Methods 0.000 description 3
- 238000005299 abrasion Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000003449 preventive effect Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 238000011144 upstream manufacturing Methods 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000013210 evaluation model Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- 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
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/04—Ageing analysis or optimisation against ageing
-
- 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
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Evolutionary Computation (AREA)
- Marketing (AREA)
- General Engineering & Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Entrepreneurship & Innovation (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Software Systems (AREA)
- Medical Informatics (AREA)
- Probability & Statistics with Applications (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Manufacturing & Machinery (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a MES equipment maintenance early warning method and a system, wherein the early warning method comprises the following steps: constructing a multi-level MES equipment maintenance early warning decision analysis system, wherein the analysis system comprises a target layer, a criterion layer and a sub-criterion layer; the target layer comprises a grade of MES equipment maintenance early warning, and the criterion layer comprises reliability and economy; the sub-criterion layer comprises the residual service life, the fault frequency and the residual economic life of the equipment; calculating a value of each element of the sub-criterion layer of the device from the measurement data; and determining the maintenance early warning level of the equipment by adopting an APH algorithm according to the value of each element of the sub-criterion layer. The invention establishes a multi-level MES equipment maintenance early warning decision analysis system comprising economy and reliability, adopts an AHP-based fuzzy comprehensive evaluation method to determine the maintenance early warning level of equipment, and performs maintenance according to a decision scheme corresponding to the maintenance early warning level, thereby improving the accuracy of equipment maintenance decision.
Description
Technical Field
The invention relates to the technical field of equipment maintenance, in particular to a MES equipment maintenance early warning method and system.
Background
The MES is a production informatization management system oriented to a workshop execution layer of a manufacturing enterprise, and the equipment maintenance management and the inventory management are important characteristics.
The equipment maintenance comprises equipment spot inspection, preventive maintenance and predictive maintenance. In the equipment spot inspection process, spot inspection data are returned to a server in real time for storage, analysis and alarm, equipment maintenance is performed by using a field terminal through maintenance planning/rules, and the data are returned in real time and travel degree tracking is performed.
Equipment maintenance under the industrial internet, from post-maintenance to preventive maintenance to post-state maintenance. And carrying out equipment fault diagnosis and prediction based on the data of state monitoring, inducing the development trend of the health state of the equipment, and finally making maintenance decision. The state maintenance work saves the maintenance time of equipment and improves the economic benefit. In the early warning work of state maintenance, only the condition of equipment state monitoring is generally considered, and elements such as economy and the like of equipment maintenance are ignored, so that the equipment is not beneficial to making more economic and reasonable maintenance decisions.
Disclosure of Invention
The invention aims to provide a MES equipment maintenance early warning method and system so as to improve the accuracy of equipment maintenance decision.
In order to achieve the above object, the present invention provides the following solutions:
an MES equipment maintenance early warning method comprises the following steps:
constructing a multi-level MES equipment maintenance early warning decision analysis system, wherein the analysis system comprises a target layer, a criterion layer and a sub-criterion layer; the target layer comprises a grade of MES equipment maintenance early warning, and the criterion layer comprises reliability and economy; the sub-criterion layer comprises the residual service life, the fault frequency and the residual economic life of the equipment;
calculating a value of each element of the sub-criterion layer of the device from the measurement data;
determining the maintenance early warning level of the equipment by adopting an APH algorithm according to the value of each element of the sub-criterion layer;
and according to the maintenance early warning level, a decision scheme is designated, and the equipment is managed.
Optionally, the calculating the value of each element of the sub-criterion layer of the device according to the measurement data specifically includes:
inputting the current operation data of the equipment into a trained equipment health value prediction model, and predicting the residual service life of the equipment in an extrapolation mode;
calculating the remaining economic life of the device based on the current operating cost data of the device;
the failure frequency of the equipment is calculated according to the MES event log.
Optionally, the step of inputting the current operation data of the device into the trained device health value prediction model, and predicting the remaining service life of the device in an extrapolation manner further includes:
acquiring equipment operation data of the whole service life of equipment from an MES time sequence database, and establishing an equipment operation data set;
clustering the operation data in the equipment operation data set by adopting a k-means clustering method to obtain the equipment operation data under different working conditions;
and training the equipment health value prediction model by using the operation data under different working conditions to obtain a trained equipment health value prediction model.
Optionally, the training the device health value prediction model by using the operation data under different working conditions, and obtaining the trained device health value prediction model further includes:
respectively carrying out standardized treatment on the operation data under different working conditions to obtain the treated operation data under different working conditions;
and performing feature extraction on the processed operation data under different working conditions by adopting a CNN, DAE or PCA algorithm to obtain feature data of the operation data under different working conditions.
Optionally, the calculating the remaining economic life of the device according to the current operation cost data of the device specifically includes:
using the formulaCalculating the average annual total equipment cost y;
wherein y is 1 Representing the average equipment maintenance costs allocated each year,C P1 lambda is a degradation value for maintenance cost of the first year equipment, (C P1 ++ (t-1) λ represents maintenance fee in the t-th year; y is 2 For the average annual purchase charge allocated, < - > for->K is the original value of the equipment;
determining a device age at which an average annual total device cost is minimal;
using the formula t=t 0 +Δt- (today () -get_date ()) correcting the equipment usage period when the average total annual equipment cost is minimum, and obtaining the corrected equipment usage period when the average total annual equipment cost is minimum;
wherein Δt represents a correction amount of the remaining economic life of the apparatus affected by the working environment, manufacturer, maintenance quality, manufacturing process, get_date () represents the apparatus input use time acquired from the apparatus identification information; today () represents the current time;
using the formulaNormalizing the service life of the equipment with the minimum total equipment cost of the corrected average year to obtain the service life of the equipment with the minimum total equipment cost of the normalized average year as the residual economic life of the equipment;
wherein T is min Indicating the shortest remaining economic life of the plant, T max Indicating the longest remaining economic life of the device.
Optionally, the method for making a decision by using an APH-based equipment maintenance early warning specifically includes:
determining the weight of each element of the sub-criterion layer relative to the criterion layer and the weight of each element of the criterion layer relative to the target layer by adopting a fuzzy comprehensive evaluation method;
and determining the maintenance early warning level of the equipment by adopting a fuzzy comprehensive evaluation method according to the weight of each element of the sub-criterion layer relative to the criterion layer, the weight of each element of the criterion layer relative to the target layer and the value of each element of the sub-criterion layer.
An MES appliance maintenance early warning system, the early warning system comprising:
the evaluation system construction module is used for constructing a multi-level MES equipment maintenance early warning decision analysis system, and the analysis system comprises a target layer, a criterion layer and a sub-criterion layer; the target layer comprises a grade of MES equipment maintenance early warning, and the criterion layer comprises reliability and economy; the sub-criterion layer comprises the residual service life, the fault frequency and the residual economic life of the equipment;
the device information acquisition module is used for calculating the value of each element of the sub-criterion layer of the device according to the measurement data;
the early warning module is used for determining the maintenance early warning level of the equipment by adopting an APH algorithm according to the value of each element of the sub-criterion layer
And the equipment management module is used for designating a decision scheme according to the maintenance early warning level and managing the equipment.
Optionally, the device information acquisition module specifically includes:
the residual service life prediction sub-module is used for inputting the current operation data of the equipment into the trained equipment health value prediction model and predicting the residual service life of the equipment in an extrapolation mode;
an economic remaining life calculation sub-module for calculating the remaining economic life of the device according to the current running cost data of the device;
and the fault frequency calculation sub-module is used for calculating the fault frequency of the equipment according to the MES event log.
Optionally, the device information obtaining module further includes:
the equipment operation data acquisition sub-module is used for acquiring the equipment operation data of the equipment life from the MES time sequence database and establishing an equipment operation data set;
the clustering sub-module is used for clustering the operation data in the equipment operation data set by adopting a k-means clustering method to obtain the equipment operation data under different working conditions;
and the model training sub-module is used for training the equipment health value prediction model by utilizing the operation data under different working conditions to obtain a trained equipment health value prediction model.
Optionally, the device information obtaining module further includes:
the standardized processing submodule is used for respectively carrying out standardized processing on the operation data under different working conditions to obtain the processed operation data under different working conditions;
and the feature extraction sub-module is used for carrying out feature extraction on the processed operation data under different working conditions by adopting a CNN, DAE or PCA algorithm to obtain feature data of the operation data under different working conditions.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a MES equipment maintenance early warning method, which comprises the following steps: constructing a multi-level MES equipment maintenance early warning decision analysis system, wherein the analysis system comprises a target layer, a criterion layer and a sub-criterion layer; the target layer comprises a grade of MES equipment maintenance early warning, and the criterion layer comprises reliability and economy; the sub-criterion layer comprises the residual service life, the fault frequency and the residual economic life of the equipment; calculating a value of each element of the sub-criterion layer of the device from the measurement data; and determining the maintenance early warning level of the equipment by adopting an APH algorithm according to the value of each element of the sub-criterion layer. The invention establishes a multi-level MES equipment maintenance early warning decision analysis system comprising economy and reliability, adopts an AHP-based fuzzy comprehensive evaluation method to determine the maintenance early warning level of equipment, and performs maintenance according to a decision scheme corresponding to the maintenance early warning level, thereby improving the accuracy of equipment maintenance decision.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a MES equipment maintenance early warning method provided by the invention;
FIG. 2 is a block diagram of a MES equipment maintenance early warning system provided by the invention;
FIG. 3 is a diagram of a system for analysis of maintenance early warning decisions of MES equipment;
FIG. 4 is a flow chart of the remaining useful life prediction of the apparatus provided by the present invention;
FIG. 5 is a graph of maintenance costs for an apparatus provided by the present invention over time;
fig. 6 is a graph of the average annual total equipment cost change provided by the present invention.
Detailed Description
The invention aims to provide a MES equipment maintenance early warning method and system so as to improve the accuracy of equipment maintenance decision.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As factors influencing the maintenance mode selection are often multiple and fuzzy, the analytic hierarchy process is an effective decision method for qualitative and quantitative analysis, combines two layers of fuzzy comprehensive judgment, finally builds a multi-level MES equipment maintenance early warning maintenance mode decision model, and aims to provide scientific basis for equipment maintenance and early warning decision.
The invention provides a MES (Manufacturing Execution System, production execution and manufacturing system) equipment maintenance early warning method and system. Constructing a multi-level MES equipment maintenance early warning decision analysis system, and analyzing factors influencing equipment maintenance early warning decisions; acquiring and transmitting equipment information, acquiring the residual service life, the fault frequency and the residual economic life of the equipment in the step 1, and evaluating the factors such as the shutdown economic loss generated by equipment maintenance, spare part supply capacity, technical transformation capacity and the like; the equipment element information is transmitted to an MES equipment maintenance early warning module, the weight is determined by combining a fuzzy comprehensive evaluation method in the early warning scheme selection, an AHP (Analytical Hierarchy Process) based equipment maintenance early warning decision method is adopted, and finally an early warning scheme is obtained; and finally, spare part management is carried out, the MES equipment management system has the functions of recording basic information of the spare part, information management, warehouse-in and warehouse-out management, inventory management and the like, when equipment 1-level early warning is carried out, the corresponding equipment needs to be scrapped and updated, the spare part management module inquires the inventory and requests the update, and if the inventory is not available, an order is placed to an upstream manufacturer of the equipment in time.
As shown in fig. 1, the invention provides a method for early warning maintenance of MES equipment, which comprises the following steps:
step 101, constructing a multi-level MES equipment maintenance early warning decision analysis system, wherein the analysis system comprises a target layer, a criterion layer and a sub-criterion layer; the target layer comprises a grade of MES equipment maintenance early warning, and the criterion layer comprises reliability and economy; the sub-criteria layer includes the remaining useful life, failure frequency, and remaining economic life of the device.
Step 102, calculating the value of each element of the sub-criterion layer of the device according to the measured data;
the calculating the value of each element of the sub-criterion layer of the device according to the measurement data in step 102 specifically includes: inputting the current operation data of the equipment into a trained equipment health value prediction model, and predicting the residual service life of the equipment in an extrapolation mode; calculating the remaining economic life of the device based on the current operating cost data of the device; the failure frequency of the equipment is calculated according to the MES event log.
The method for predicting the residual service life of the equipment by using the extrapolation method comprises the following steps of inputting the current operation data of the equipment into a trained equipment health value prediction model, and predicting the residual service life of the equipment by using the extrapolation method:
acquiring equipment operation data of the whole service life of equipment from an MES time sequence database, and establishing an equipment operation data set;
clustering the operation data in the equipment operation data set by adopting a k-means clustering method to obtain the equipment operation data under different working conditions;
respectively carrying out standardized treatment on the operation data under different working conditions to obtain the treated operation data under different working conditions;
and performing feature extraction on the processed operation data under different working conditions by adopting a CNN (Convolutional Neural Networks, convolutional neural network), DAE (Deep Auto Encoder, deep self-encoder) or PCA (Principal Component Analysis ) algorithm to obtain feature data of the operation data under different working conditions.
And training the equipment health value prediction model by using the operation data under different working conditions to obtain a trained equipment health value prediction model.
The method for calculating the residual economic life of the equipment according to the current operation cost data of the equipment specifically comprises the following steps:
using the formulaCalculating the average annual total equipment cost y;
wherein y is 1 Representing the average equipment maintenance costs allocated each year,C P1 lambda is a degradation value for maintenance cost of the first year equipment, (C P1 ++ (t-1) λ represents maintenance fee in the t-th year; y is 2 For the average annual purchase charge allocated, < - > for->K is the original value of the equipment;
determining a device age at which an average annual total device cost is minimal;
using the formula t=t 0 +Δt- (today () -get_date ()) with minimum total equipment cost per year on averageThe service life of the equipment is corrected, and the service life of the equipment with the minimum average annual total equipment cost after correction is obtained;
wherein Δt represents a correction amount of the remaining economic life of the apparatus affected by the working environment, manufacturer, maintenance quality, manufacturing process, get_date () represents the apparatus input use time acquired from the apparatus identification information; today () represents the current time;
using the formulaNormalizing the service life of the equipment with the minimum total equipment cost of the corrected average year to obtain the service life of the equipment with the minimum total equipment cost of the normalized average year as the residual economic life of the equipment;
wherein T is min Indicating the shortest remaining economic life of the plant, T max Indicating the longest remaining economic life of the device.
And step 103, determining the maintenance early warning level of the equipment by adopting an APH algorithm according to the value of each element of the sub-criterion layer.
Step 103, a device maintenance early warning decision method based on APH is adopted, which specifically includes:
determining the weight of each element of the sub-criterion layer relative to the criterion layer and the weight of each element of the criterion layer relative to the target layer by adopting a fuzzy comprehensive evaluation method;
and determining the maintenance early warning level of the equipment by adopting a fuzzy comprehensive evaluation method according to the weight of each element of the sub-criterion layer relative to the criterion layer, the weight of each element of the criterion layer relative to the target layer and the value of each element of the sub-criterion layer.
And 104, according to the maintenance early warning level, designating a decision scheme, and managing the equipment.
An MES appliance maintenance early warning system, the early warning system comprising:
the evaluation system construction module is used for constructing a multi-level MES equipment maintenance early warning decision analysis system, and the analysis system comprises a target layer, a criterion layer and a sub-criterion layer; the target layer comprises a grade of MES equipment maintenance early warning, and the criterion layer comprises reliability and economy; the sub-criterion layer comprises the residual service life, the fault frequency and the residual economic life of the equipment;
and the equipment information acquisition module is used for calculating the value of each element of the sub-criterion layer of the equipment according to the measured data.
The device information acquisition module specifically comprises: the residual service life prediction sub-module is used for inputting the current operation data of the equipment into the trained equipment health value prediction model and predicting the residual service life of the equipment in an extrapolation mode; an economic remaining life calculation sub-module for calculating the remaining economic life of the device according to the current running cost data of the device; and the fault frequency calculation sub-module is used for calculating the fault frequency of the equipment according to the MES event log.
The device information acquisition module further includes: the equipment operation data acquisition sub-module is used for acquiring the equipment operation data of the equipment life from the MES time sequence database and establishing an equipment operation data set; the clustering sub-module is used for clustering the operation data in the equipment operation data set by adopting a k-means clustering method to obtain the equipment operation data under different working conditions; and the model training sub-module is used for training the equipment health value prediction model by utilizing the operation data under different working conditions to obtain a trained equipment health value prediction model.
The device information acquisition module further includes: the standardized processing submodule is used for respectively carrying out standardized processing on the operation data under different working conditions to obtain the processed operation data under different working conditions; and the feature extraction sub-module is used for carrying out feature extraction on the processed operation data under different working conditions by adopting a CNN, DAE or PCA algorithm to obtain feature data of the operation data under different working conditions.
And the early warning module is used for determining the maintenance early warning level of the equipment by adopting an APH algorithm according to the value of each element of the sub-criterion layer.
And the equipment management module is used for designating a decision scheme according to the maintenance early warning level and managing the equipment.
The present invention also provides a specific embodiment for illustrating the specific implementation and technical effects of the method and system of the present invention.
The embodiment of the invention discloses a MES equipment maintenance early warning method and a system, which are respectively shown in figures 1 and 2, wherein the method comprises the following steps:
step 1: and constructing a multi-level MES equipment maintenance early warning decision analysis system, wherein the analysis system comprises a target layer criterion layer, a sub-criterion layer and a scheme layer.
The target layer is an equipment maintenance early warning decision; the criterion layer comprises: reliability, economy, and other factors; the sub-criteria include: the residual service life of equipment, the failure frequency, the residual economic life of equipment, the spare part supply capacity and the technical transformation capacity due to the shutdown economic loss generated by equipment maintenance; the scheme layer comprises four early warning levels P1, P2, P3 and P4 and four corresponding early warning schemes, the scheme layer is shown in a table 1, and a multi-level MES equipment maintenance early warning decision analysis system is shown in a figure 3.
Table 1 correspondence table of early warning level and early warning scheme:
step 2: and (3) acquiring a plurality of elements of the criterion layer described in the step (1), and evaluating the remaining service life, failure frequency, remaining economic life of the equipment, shutdown economic loss caused by equipment maintenance, spare part supply capacity, technical transformation capacity and other sub-criterion layer factors.
And 2.1, building a device health value prediction model to obtain one of decision factors of device maintenance early warning and a residual service life value of the device. The specific steps are shown in fig. 4.
Step 2.1.1 obtaining industrial production data of the equipment. And acquiring equipment operation data from the MES time sequence database, and constructing a multi-dimensional time sequence matrix of the equipment life.
The MES system adopts OPC UA (Unified Architecture ) as a unified standard for industrial equipment identification data acquisition, reads real-time data through the OPC UA, and then stores the read data into a time-labeled time sequence database, wherein sufficient and valuable data are important basic stones for researching equipment degradation characteristics.
Step 2.1.2 data preprocessing.
Before pretreatment, working condition classification is carried out according to the operating conditions and the operating parameters, and the k-means clustering method is adopted to cluster the operating parameters, so that the speed is high and the robustness is high.
And (3) cleaning the data of the original data obtained in the step (2.1.1), reducing the interference of abnormal information, carrying out standardized processing on sample data under different working conditions, and eliminating the influence of the change of the working conditions, wherein a standardized processing formula is shown in a formula (1).
Where x is the sensor data, u is the calculated mean, and σ is the standard deviation.
And 2.1.3, extracting features of the multidimensional sensor data, and removing redundant features.
And the data is subjected to feature selection by using a CNN model, a DAE model and other models, or the data dimension reduction is effectively realized by using a PCA method and other methods.
And 2.1.4, building equipment health indexes and training an equipment health value prediction model.
After step 2.1.3, determining the health index of the multi-sensor data fusion. Taking an engine as an example, the health index calculation formula is shown as formula (2)
Wherein M is the number of sensors of the engine,the data preprocessed by the jth sensor of the ith engine,the fusion coefficient of the j-th sensor represents its health weight.
Setting a model training label, training a health value prediction model based on a machine learning and deep learning algorithm, and obtaining smaller output errors by adjusting parameters. Predefined failure threshold table threshld And calculating the RUL (Remaining Useful Life, residual service life) prediction result at a certain position by judging the cycle number of the distance reaching the threshold value on the health value prediction curve.
And 2.1.5, inputting test set data, and carrying out RUL prediction.
After the step 2.1.4, constructing a health curve by an indirect method, training a device health value prediction model, inputting the test set into the device health value prediction model, and obtaining the RUL value by extrapolation.
Taking an engine as an example, the service life of the residual equipment is represented by the residual operation cycle number before the equipment fails, namely the maximum operation cycle number is RUL max The shortest remaining life of the apparatus, i.e. the remaining life close to the failure threshold is RUL min With RUL min And 0. The service life of the equipment remaining equipment is normalized, the abrasion degree and the remaining service life of the equipment can be clearly determined, the maintenance early warning is conveniently decided, a normalization formula is shown in a formula (3), RUL' represents the normalized service life of the remaining equipment, the remaining service life of the equipment is the normalized service life of the equipment, and the range is [0,1]]Between them.
And 2.2, obtaining running cost data of the equipment, and calculating the economic life of the equipment.
The Life Cycle Cost (LCC) includes the total Cost of the product during the Life Cycle, i.e., the effective use period. The total cost of a device mainly comprises two parts of depreciation cost and maintenance cost. Wherein the maintenance fee refers to the cost for maintaining the production operation of the equipment, and comprises maintenance fee, overhaul fee, fuel power fee, labor fee and the like; the depreciation cost is the cost of compensating the equipment abrasion so as to ensure that the equipment finishes reproduction. With the increase of the service time of the equipment, the maintenance cost of the equipment is increased, the equipment is gradually deteriorated, the maintenance cost is increased year by year, the annual allocated investment cost is gradually reduced, and the depreciation cost is reduced year by year. According to the change rules of depreciation fees and maintenance fees, the change trend of the total amount of the two fees can be obtained, and the year with the minimum total amount of the two fees is the economic life value of the equipment.
2.2.1 determining the economic lifetime of a device in static mode
The economic life of the device is calculated in static mode regardless of the time value of the capital.
Let K represent the original value of the equipment, O represent the residual value of the equipment scrapped and recovered, and the maintenance cost of the equipment in the first year is C P1 Deterioration value lambda and maintenance charge C in the t-th year P1 As shown in FIG. 5, the maintenance cost of the equipment increases with time, and the maintenance cost of the equipment which is averagely distributed every year is y 1 ：
Ignoring residual error O during equipment updating, and obtaining annual average purchase cost as y 2 There isSumming these two terms gives an average annual total equipment cost y:
the average annual total equipment cost change curve is shown in figure 6, and the service life of the equipment when the average annual total equipment cost is the minimum is set as t 0 。
Let dy/dt=0, there is
2.2.2 optimal economic life correction and calculation of remaining economic life of the equipment.
The economic life of the equipment is influenced by factors such as working environment, manufacturer, maintenance quality, manufacturing process and the like, and delta t is taken as correction quantity. After correction, the residual economic life of the equipment is represented by T, expressed by the time of minimum total equipment cost from the last year, namely the corrected economic life value of the equipment is represented by T max ，T max ＝t 0 +Δt, the shortest remaining economic life of the plant being T min T is provided with min And 0. The device in-use time can be obtained from the device identification information, and is set as get_date (), the current time is today (), and T=t is available 0 +Δt-(today()-get_date())。
2.2.3 carrying out normalization processing on the residual economic life of the equipment, which can clearly determine the use economy sum of the equipment, provide reference for equipment updating, facilitate decision making on maintenance early warning, and lead the normalization formula to be shown as a formula (6):
t' represents the normalized remaining equipment life, and the remaining equipment life mentioned below is the normalized remaining equipment life, ranging between [0,1 ].
Step 2.3, obtaining the fault frequency of equipment through MES event logs, expert feedback and the like, and obtaining information such as spare part supply capacity, technical transformation capacity and the like due to shutdown economic loss generated by equipment maintenance.
Step 3: and the MES equipment maintenance early warning module adopts an AHP-based equipment maintenance early warning decision method for the selection of an early warning scheme. The method specifically comprises the following steps:
step 3.1 Pre-maintenance of the EquipmentDecision factors of the alarm scheme are compared in pairs, a judgment matrix is constructed to determine weight A, namely the importance degree of different factors, and A= (a) is set 1 ,a 2 ,···,a m )，a i The weight of the i-th factor is represented.
Has a value of 0.ltoreq.a i Is less than or equal to 1, andthe method comprises the following steps:
the three factors of the same layer were compared in pairs to determine their relative importance, and the 1-9 scale of Satty (T.L.Saath) is shown in Table 2.
Table 2 Scale definition table for judging matrix elements
And taking a scientific decision of the equipment maintenance early warning scheme P as a target, establishing a first layer of judgment matrix according to expert evaluation, wherein the value of the judgment matrix after the expert evaluation is shown in a table 3.
Table 3 one layer of judgment matrix value table
| P | C1 | C2 | C3 |
| C1 | 1 | 4 | 2 |
| C2 | 1/4 | 1 | 1/3 |
| C3 | 1/2 | 3 | 1 |
The judgment matrix of the first layer can be extracted from Table 3, namely
And obtaining a characteristic vector w= (0.5584,0.1220,0.3196) according to the judgment matrix Q.
The judgment matrix of the second layer is obtained according to expert evaluation as shown in tables 4-6.
Table 4A first two-layer judgment matrix value table
| C1 | Z1 | Z2 |
| Z1 | 1 | 2 |
| Z2 | 1/2 | 1 |
Table 5 second layer judgment matrix value table
| C2 | Z3 | Z4 |
| Z3 | 1 | 3 |
| Z4 | 1/3 | 1 |
Table 6 third two-layer judgment matrix value table
| C3 | Z4 | Z5 |
| Z5 | 1 | 4 |
| Z6 | 1/4 | 1 |
And 3.2, carrying out consistency test on the judgment matrix obtained in the step 3.1, wherein the purpose of introducing the consistency test is to detect whether the weight obtained by the judgment matrix is reasonable.
The CR is a proportion of the consistency and,
CI is a consistency index, RI is an average random consistency index, the value of RI is related to the order of the judgment matrix, and when the order of the P judgment matrix is 1 to 9, the average consistency indexes RI are 0,0,0.58,0.90,1.12,1.24,1.32,1.41,1.45 respectively. The consistency check formula is shown as formula (7):
when CR < 0.1 or lambda of matrix P max When ci=0 < 0.1, it indicates that the consistency of the judgment matrix is acceptable, otherwise, correction adjustment is required to the judgment matrix element value until the verification is passed.
And (3) carrying out consistency test on the matrix P: as calculated, for the judgment matrix P, there is cr=0.0080 < 0.1, and the consistency thereof can be accepted, so the feature vector w= (0.5584,0.1220,0.3196) can be used as the calculation weight of the first layer, i.e., a= (0.5584,0.1220,0.3196).
Similarly, consistency checks are performed on matrices C1, C2, C3: all have a maximum eigenvalue lambda max For matrices C1, C2, C3, a is the case for matrix C1, C2, C3, respectively 1 ＝(0.6667，0.3333)，A 2 ＝(0.7500，0.2500)，A 3 ＝(0.8000，0.2000)。
And 3.3, combining the weights obtained in the step 3.2, constructing a fuzzy relation matrix according to the membership degree of each layer of factors to the equipment maintenance early warning scheme, and obtaining an evaluation result through two layers of fuzzy evaluation. The method comprises the following steps:
(1) The reliability mainly consists of two factors of the residual service life and the failure frequency of the equipment, and the economy mainly consists of two factors of the residual economic life and the equipment maintenance loss of the equipment by combining the step 1 and the step 2.
The fuzzy relation matrix of the residual service life of the equipment to the equipment maintenance early warning scheme is shown in table 7. The remaining service life RUL 'of the device is obtained from step 1, assuming that the remaining service life RUL' =0.9 of a certain device is obtained via step 1, and a 2 And (2) the fuzzy relation vector p= (0.9,0.1,0,0) corresponding to Z1 is less than or equal to 0.9 and less than or equal to 1.
TABLE 7 fuzzy relation matrix table of remaining useful life of device for device maintenance pre-warning scheme
Similarly, the fuzzy relation matrix for the remaining economic life of the device is Table 8. The remaining economic life T 'of the device is obtained from step 2, assuming that the remaining economic life T' =0.8 of a certain device is obtained via step 2, and b 2 And (2) the fuzzy relation vector is less than or equal to 0.8 and less than or equal to 1, namely Z3=0.9, and the fuzzy relation vector corresponding to Z3 is q= (0.8,0.2,0,0).
Table 8 fuzzy relation matrix table of residual economic life of equipment for equipment maintenance early warning scheme
| Z3 | P1 | P2 | P3 | P4 |
| [0,b1) | 0 | 0.2 | 0.3 | 0.5 |
| (b1,b2) | 0.1 | 0.4 | 0.4 | 0.1 |
| (b2,1] | 0.8 | 0.2 | 0 | 0 |
Similarly, fuzzy relation matrices for sub-criterion layer index importance comparisons are shown in tables 9-11 below:
TABLE 9 fuzzy relationship matrix table of operational data versus remaining useful life of a device
| C1 | P1 | P2 | P3 | P4 |
| Z1 | 0.9 | 0.1 | 0 | 0 |
| Z2 | 0.1 | 0.2 | 0.3 | 0.4 |
Table 10 fuzzy relation matrix table of current running cost versus equipment economic life
| C2 | P1 | P2 | P3 | P4 |
| Z3 | 0.8 | 0.2 | 0 | 0 |
| Z4 | 0.1 | 0.4 | 0.1 | 0.4 |
Table 11 fuzzy relation matrix table of MES event log to failure rate
| C3 | P1 | P2 | P3 | P4 |
| Z5 | 0.3 | 0.2 | 0.2 | 0.3 |
| Z6 | 0.2 | 0.3 | 0.2 | 0.3 |
(2) And synthesizing the weight vector A and the fuzzy relation matrix R to obtain a comprehensive evaluation vector B for equipment maintenance early warning scheme decision. The specific calculation formula is shown as formula (8):
obtaining the fuzzy relation matrix R corresponding to C1, C2 and C3 according to the tables 9-11 in the step (1) 1 、R 2 、R 2 According to step 3.2, the weight vector A corresponding to the second layer C1, C2, C3 can be obtained 1 、A 2 、A 3 A can then be calculated separately 1 R 1 ，A 2 R 2 ，A 3 R 3 See formula (9).
The weight vector corresponding to P of the first layer can be obtained according to step 3.2. The evaluation vector B of the two layers of comprehensive calculation is shown in a formula (10):
B＝AR＝(a 1 ,a 1 ,a 3 )(A 1 R 1 ，A 2 R 2 ，A 3 R 3 ) T ＝(0.5182,0.1747,0.1228,0.1823) (10)
(3) According to the maximum membership principle, the final judgment result is obtained, and the maintenance early warning scheme of the equipment in the MES system is P1, namely the equipment runs normally without alarming.
Step 4: and a spare part management module. The early warning result of the last step is P1, the equipment is not required to inquire a spare part library under the condition of normal operation and no alarm, if the early warning result is P4, namely the equipment 1-level early warning is performed, the corresponding equipment is required to be scrapped and updated, the spare part management module inquires the inventory, requests the update, and if the inventory is not available, the upstream manufacturer of the equipment is timely ordered.
The spare part management module performs the organization and management of planning, production, ordering, supply and storage of spare parts, has the functions of recording basic information of the spare parts, information management, warehouse entry and exit management, inventory management and the like in the MES equipment management system, realizes the scientific management and control of the spare parts, and reasonably utilizes the inventory space.
According to the characteristics of the invention, the invention discloses the following technical effects:
1. the fuzzy comprehensive evaluation model based on AHP provided in the maintenance early warning module quantitatively analyzes a plurality of subjective factors influencing final decision by means of weight determination, fuzzy relation matrix construction and the like, and is more visual and accurate than the traditional qualitative evaluation; the method is favorable for making more scientific and reasonable decisions and provides reference for more reasonable equipment maintenance early warning decisions.
2. In the process of establishing a device health value prediction model to obtain the residual service life value of the device, the full-life multi-feature data of the industrial device is processed according to working conditions, feature extraction and life prediction are carried out by combining algorithms such as machine learning, deep learning and the like, and the prediction capability and generalization capability of the prediction model and the prediction accuracy of the residual service life value of the device are improved.
3. In the equipment early warning process, reliability factors such as residual service life prediction of equipment are considered, economic factors such as residual economic life of equipment and shutdown loss generated by equipment maintenance are considered, and decisions can be made on maintenance modes such as equipment maintenance or scrapping update more comprehensively.
4. In the decision-making process of the early warning scheme, various indexes can be added and deleted and modified according to actual conditions, and corresponding measures are taken to improve weak links in equipment operation, so that the health management capability of an MES system on equipment is improved.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, which are intended to be only illustrative of the methods and concepts underlying the invention, and not all examples are intended to be within the scope of the invention as defined by the appended claims.
Claims (2)
1. The MES equipment maintenance early warning method is characterized by comprising the following steps of:
constructing a multi-level MES equipment maintenance early warning decision analysis system, wherein the analysis system comprises a target layer, a criterion layer and a sub-criterion layer; the target layer comprises a grade of MES equipment maintenance early warning, and the criterion layer comprises reliability and economy; the sub-criterion layer comprises the residual service life, the fault frequency and the residual economic life of the equipment; the MES performs a manufacturing system for the production;
based on the measurement data, calculating the value of each element of the sub-criterion layer of the device, specifically comprising: inputting the current operation data of the equipment into a trained equipment health value prediction model, and predicting the residual service life of the equipment in an extrapolation mode; calculating the remaining economic life of the device based on the current operating cost data of the device; calculating the fault frequency of the equipment according to the MES event log;
determining the maintenance early warning level of the equipment by adopting an APH algorithm according to the value of each element of the sub-criterion layer; the APH algorithm is a hierarchical analysis algorithm;
according to the maintenance early warning level, a decision scheme is designated, and equipment is managed;
the method comprises the steps of inputting the current operation data of the equipment into a trained equipment health value prediction model, and predicting the residual service life of the equipment in an extrapolation mode, and the method further comprises the following steps:
acquiring equipment operation data of the whole service life of equipment from an MES time sequence database, and establishing an equipment operation data set;
clustering the operation data in the equipment operation data set by adopting a k-means clustering method to obtain the equipment operation data under different working conditions;
training the equipment health value prediction model by using the operation data under different working conditions to obtain a trained equipment health value prediction model;
the method comprises the steps of training the equipment health value prediction model by utilizing operation data under different working conditions to obtain a trained equipment health value prediction model, and further comprises the following steps:
respectively carrying out standardized treatment on the operation data under different working conditions to obtain the treated operation data under different working conditions;
performing feature extraction on the processed operation data under different working conditions by adopting a CNN convolutional neural network, a DEA deep self-encoder or a PCA main component analysis algorithm to obtain feature data of the operation data under different working conditions;
the method for calculating the residual economic life of the equipment according to the current operation cost data of the equipment specifically comprises the following steps:
using the formulaCalculating the average annual total equipment cost y;
wherein y is 1 Representing the average equipment maintenance costs allocated each year,C P1 lambda is a degradation value for maintenance cost of the first year equipment, (C P1 ++ (t-1) λ represents maintenance fee in the t-th year; y is 2 For the average annual purchase charge allocated, < - > for->K is the original value of the equipment;
determining the service life t of the device when the average annual total equipment cost is minimum 0 ；
Using the formula t=t 0 +Δt- (today () -get_date ()) correcting the equipment usage period when the average total annual equipment cost is minimum, and obtaining the corrected equipment usage period when the average total annual equipment cost is minimum;
wherein Δt represents a correction amount of the remaining economic life of the apparatus affected by the working environment, manufacturer, maintenance quality, manufacturing process, get_date () represents the apparatus input use time acquired from the apparatus identification information; today () represents the current time;
using the formulaNormalizing the service life of the equipment with the minimum total equipment cost of the corrected average year to obtain the service life of the equipment with the minimum total equipment cost of the normalized average year as the residual economic life of the equipment;
wherein T is min Indicating the shortest remaining economic life of the plant, T max Indicating the longest remaining economic life of the device;
the method for determining the maintenance early warning level of the equipment by adopting the APH algorithm specifically comprises the following steps:
determining the weight of each element of the sub-criterion layer relative to the criterion layer and the weight of each element of the criterion layer relative to the target layer by adopting a fuzzy comprehensive evaluation method;
and determining the maintenance early warning level of the equipment by adopting a fuzzy comprehensive evaluation method according to the weight of each element of the sub-criterion layer relative to the criterion layer, the weight of each element of the criterion layer relative to the target layer and the value of each element of the sub-criterion layer.
2. A MES machine maintenance pre-warning system, wherein the pre-warning system is applied to the pre-warning method of claim 1, and the pre-warning system comprises:
the evaluation system construction module is used for constructing a multi-level MES equipment maintenance early warning decision analysis system, and the analysis system comprises a target layer, a criterion layer and a sub-criterion layer; the target layer comprises a grade of MES equipment maintenance early warning, and the criterion layer comprises reliability and economy; the sub-criterion layer comprises the residual service life, the fault frequency and the residual economic life of the equipment; the MES performs a manufacturing system for the production;
the device information acquisition module is used for calculating the value of each element of the sub-criterion layer of the device according to the measurement data; the device information acquisition module specifically comprises: the residual service life prediction sub-module is used for inputting the current operation data of the equipment into the trained equipment health value prediction model and predicting the residual service life of the equipment in an extrapolation mode; an economic remaining life calculation sub-module for calculating the remaining economic life of the device according to the current running cost data of the device; the fault frequency calculation sub-module is used for calculating the fault frequency of the equipment according to the MES event log;
the early warning module is used for determining the maintenance early warning level of the equipment by adopting an APH algorithm according to the value of each element of the sub-criterion layer; the APH algorithm is a hierarchical analysis algorithm;
and the equipment management module is used for designating a decision scheme according to the maintenance early warning level and managing the equipment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011095562.0A CN112200327B (en) | 2020-10-14 | 2020-10-14 | MES equipment maintenance early warning method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011095562.0A CN112200327B (en) | 2020-10-14 | 2020-10-14 | MES equipment maintenance early warning method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112200327A CN112200327A (en) | 2021-01-08 |
| CN112200327B true CN112200327B (en) | 2023-10-17 |
Family
ID=74009643
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011095562.0A Active CN112200327B (en) | 2020-10-14 | 2020-10-14 | MES equipment maintenance early warning method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112200327B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113191698A (en) * | 2021-06-03 | 2021-07-30 | 云上(南昌)大数据运营有限公司 | Resource planning system and method based on urban brain platform |
| CN115657533A (en) * | 2022-10-19 | 2023-01-31 | 扬力集团股份有限公司 | Edge control equipment and control method for press machine |
| CN116050950B (en) * | 2023-04-03 | 2023-06-23 | 西南交通大学 | Method, device, equipment and medium for evaluating railway junction passenger train schedule |
| CN116629843B (en) * | 2023-07-25 | 2023-10-20 | 山东比沃斯机电工程有限公司 | Remote early warning and maintenance decision support system of intelligent diesel generator set |
| CN117592383B (en) * | 2024-01-19 | 2024-03-26 | 四川晟蔚智能科技有限公司 | Method, system, equipment and medium for predicting equipment health life |
| CN118469411A (en) * | 2024-07-15 | 2024-08-09 | 浙江高信技术股份有限公司 | Maintenance method and device for electromechanical equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018053935A1 (en) * | 2016-09-20 | 2018-03-29 | 西南石油大学 | Failure mode occurrence probability based operating status fuzzy evaluation and prediction method for rotating device |
| CN108229042A (en) * | 2018-01-12 | 2018-06-29 | 中国大唐集团科学技术研究院有限公司华东分公司 | A kind of generator tube remaining life early warning system and method for early warning |
| CN109255524A (en) * | 2018-08-16 | 2019-01-22 | 广西电网有限责任公司电力科学研究院 | A kind of measuring equipment data analyzing evaluation method and system |
| CN111160685A (en) * | 2019-09-23 | 2020-05-15 | 上海安恪企业管理咨询有限公司 | Maintenance decision method based on equipment comprehensive health condition analysis and management |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI663510B (en) * | 2017-11-21 | 2019-06-21 | 財團法人資訊工業策進會 | Equipment maintenance forecasting system and operation method thereof |
-
2020
- 2020-10-14 CN CN202011095562.0A patent/CN112200327B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018053935A1 (en) * | 2016-09-20 | 2018-03-29 | 西南石油大学 | Failure mode occurrence probability based operating status fuzzy evaluation and prediction method for rotating device |
| CN108229042A (en) * | 2018-01-12 | 2018-06-29 | 中国大唐集团科学技术研究院有限公司华东分公司 | A kind of generator tube remaining life early warning system and method for early warning |
| CN109255524A (en) * | 2018-08-16 | 2019-01-22 | 广西电网有限责任公司电力科学研究院 | A kind of measuring equipment data analyzing evaluation method and system |
| CN111160685A (en) * | 2019-09-23 | 2020-05-15 | 上海安恪企业管理咨询有限公司 | Maintenance decision method based on equipment comprehensive health condition analysis and management |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112200327A (en) | 2021-01-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112200327B (en) | MES equipment maintenance early warning method and system | |
| US6766283B1 (en) | System and method for monitoring process quality control | |
| CN113779496A (en) | Power equipment state evaluation method and system based on equipment panoramic data | |
| CN111813084A (en) | Mechanical equipment fault diagnosis method based on deep learning | |
| CN112284440B (en) | Sensor data deviation self-adaptive correction method | |
| CN114595623A (en) | XGboost algorithm-based unit equipment reference value prediction method and system | |
| CN118154174B (en) | Intelligent operation and maintenance cloud platform for industrial equipment | |
| CN118228130B (en) | Monitoring method, system and storage medium based on equipment health state | |
| CN117726240A (en) | Quality evaluation classification method and system based on convolutional neural network | |
| CN116705272A (en) | Comprehensive evaluation method for equipment health state based on multidimensional diagnosis | |
| CN118211882B (en) | Product quality management system and method based on big data | |
| CN117541057A (en) | Enterprise operation early warning monitoring method and system based on data analysis | |
| Wang et al. | Applying machine learning and GA for process parameter optimization in car steering wheel manufacturing | |
| CN111832993B (en) | Predictive maintenance method for warehouse logistics system and related components | |
| CN116205622A (en) | Intelligent fault early warning and maintenance decision method for smoke machine equipment | |
| CN116522065A (en) | Coal mill health degree assessment method based on deep learning | |
| CN112215514A (en) | Operation analysis report generation method and system | |
| CN118469268B (en) | Digital factory consumable management system and method | |
| CN118482817B (en) | Digital textile equipment management method and system based on data analysis | |
| CN117910758B (en) | Production and manufacture scheduling method based on self-adaptive optimization algorithm | |
| CN112488432B (en) | Equipment health assessment method, storage device and terminal | |
| CN118411161B (en) | Electromechanical equipment operation and maintenance method and system based on data analysis | |
| CN118521222B (en) | Liquid crystal glass thickness online detection method, system, equipment and medium | |
| CN113327162B (en) | Credit wind control rule making system | |
| CN118263863B (en) | Intelligent control method for power load balance |
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 |