CN107862394B - Equipment active maintenance support cooperative method - Google Patents
Equipment active maintenance support cooperative method Download PDFInfo
- Publication number
- CN107862394B CN107862394B CN201711052317.XA CN201711052317A CN107862394B CN 107862394 B CN107862394 B CN 107862394B CN 201711052317 A CN201711052317 A CN 201711052317A CN 107862394 B CN107862394 B CN 107862394B
- Authority
- CN
- China
- Prior art keywords
- equipment
- data
- maintenance
- maintenance support
- information
- 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
Images
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
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
Abstract
The invention relates to a cooperative method for equipment active maintenance support, and relates to the technical field of equipment fault diagnosis. The method comprises the following steps: (1) the sensor senses the multidimensional original data of the equipment and stores the data locally; (2) after the data are sampled, selecting partial data to be uploaded periodically and storing the partial data in an equipment maintenance guarantee management cloud; (3) evaluating the equipment state according to the uploaded data and the historical data, generating a preliminary equipment maintenance guarantee plan, recording a corresponding result, and sending the result to an equipment maintenance guarantee mobile terminal; (4) connecting the maintenance support mobile terminal with the equipment, reading original data of the equipment, and further diagnosing and verifying the equipment state; (5) and equipment maintenance is carried out, and the mobile terminal and the remote expert carry out cooperative interaction to realize active and cooperative maintenance guarantee of the equipment. The invention can effectively improve the cooperative efficiency of equipment and maintenance support personnel and realize distributed and cooperative active maintenance support.
Description
Technical Field
The invention relates to the technical field of equipment fault diagnosis, in particular to a cooperative method for equipment active maintenance support.
Background
With the rapid development of sensing technology, communication technology, and cloud computing technology, various complex engineering systems and devices are applied to various industries. In the using process of the system, timely and effective equipment maintenance guarantee is of great importance to the system. The core of the equipment maintenance support is to realize the cooperation and interaction of information such as equipment perception information, maintenance support command information, maintenance support information, expert guidance and the like through efficient organization, so that the equipment obtains the best support efficiency. Complex system equipment maintenance safeguards have many specificities, such as: the guarantee power is distributed, the interactive information has heterogeneity, and the self-organization and mutual cooperation of the guarantee power and the personnel behavior are emphasized. Therefore, when a system maintenance support system is implemented, difficulties such as complex system structure, heterogeneous interaction information, distributed type and the like are faced, and therefore, an efficient maintenance support cooperation method for complex equipment is needed.
The traditional equipment maintenance support usually adopts a passive maintenance support mode, and generally can correspondingly support equipment only when the equipment fails, which often causes improper use and short service life of the equipment; on the other hand, when equipment maintenance support is carried out, organization and cooperation of maintenance support force are generally not paid attention to, and when various complex equipment is aimed at, especially when maintenance support is carried out by cooperation of various support force, the capacity of a support mode is slightly insufficient, and the efficiency is low.
Therefore, a method capable of solving the problem of low cooperative efficiency of the conventional maintenance support, realizing active maintenance support of the equipment and improving organization and cooperative efficiency of maintenance support force is urgently needed.
Disclosure of Invention
Technical problem to be solved
The technical problem to be solved by the invention is as follows: how to effectively improve the cooperative efficiency of equipment and maintenance support personnel.
(II) technical scheme
In order to solve the technical problem, the invention provides a cooperative method for actively maintaining and guaranteeing equipment, which comprises the following steps:
step 1, sensing multi-dimensional original data of equipment by various equipment sensors, and storing the multi-dimensional original data locally;
step 2, after the original data are sampled, selecting partial data to be uploaded regularly and storing the partial data to an equipment maintenance guarantee cloud;
step 3, the maintenance support cloud end evaluates the equipment state according to the uploaded data and the historical data, generates a preliminary equipment maintenance support plan, records a corresponding result and sends the result to the equipment maintenance support mobile terminal;
step 4, connecting the maintenance support mobile terminal with the equipment, reading original data of the equipment, further diagnosing and verifying the state of the equipment, and generating a maintenance support scheme according to a preliminary maintenance support plan and an intelligent algorithm;
and 5, performing equipment maintenance by maintenance support personnel according to the scheme, and performing cooperative interaction with remote personnel through the mobile terminal to realize active and cooperative maintenance support of the equipment.
Preferably, step 1 specifically comprises:
step 11: sensing device information: the system senses the M-dimensional state information X ═ X of system equipment through various sensors1,X2,…,XM]The method comprises the following steps of obtaining basic information including identification information, time, position information and working state of equipment, wherein the information acquisition period is T;
step 12: storage device information: in the local device, the data of sensing device information M dimension is stored, and the data of D days is stored.
Preferably, step 2 specifically comprises:
step 21: screening of raw data: after local data of the equipment are screened, selecting a small amount of N-dimensional key data, wherein the sampling period is I '. multidot.T, I' is not less than 1, and N is less than M;
step 22: uploading screened data, uploading the screened data according to the guarantee period requirement and the transmission network limit, and storing the data in a cloud server according to a period P days, wherein P is less than or equal to D;
in step 22, the uploading period is selected to be 12 hours, that is, 0.5 day, and uploading is performed at each 12-point time.
Preferably, step 3 specifically comprises:
step 31: preliminary evaluation of data: the cloud end carries out preliminary state evaluation on the equipment by combining historical data of the equipment when the equipment uploads data each time, and generates a preliminary maintenance guarantee plan of the equipment according to a state evaluation result;
step 32: and storing the initial maintenance guarantee plan of the equipment after the initial maintenance guarantee plan of the equipment is generated, and directly sending the result to the mobile terminal for the maintenance guarantee of the equipment.
Preferably, step 4 specifically includes:
step 41: verifying the state of the device: according to the requirements of a maintenance guarantee plan, firstly, connecting a maintenance guarantee mobile terminal with equipment in a wireless or wired mode, reading original data of the equipment, and verifying whether the equipment state is consistent with the guarantee plan or not;
step 42: and (3) generating a maintenance guarantee scheme: and after the verification result is consistent, the maintenance support mobile terminal generates a maintenance support implementation scheme.
Preferably, step 42 specifically comprises:
step 421: the inference step comprises: inputting key data of current equipment, searching similar cases in a case library, giving fault diagnosis results and solving measures if the similar cases exist, switching to a deep neural network model based on a time sequence if the similar cases do not exist, searching the cases by adopting a searching mode based on K-neighbor matching, and searching the cases by adopting a searching mode based on the K-neighbor matching specifically comprises the following steps:
each case contains
Species characteristics, failure case Ci(i ═ 1, 2.., n) can be represented by an m-dimensional vector: a. thei＝(ai1，ai2，...，aim)，aij(j ═ 1, 2.., m) is fault case CiThe value of the jth feature of (1);
the similarity between cases is defined as:
wherein 0 is less than or equal to Sim (C)i，Cj)≤1；ωkRepresents the weight of the kth feature in the case feature vector, an
Step 422: aiming at the characteristic of time serialization of the acquired equipment state information sequence, modeling the data characteristic vector fed back by the equipment according to the time sequence by adopting a generalized autoregressive conditional variance model GARCH, and judging whether the equipment fails according to the obtained calculation result;
the modeling of the data characteristic vector fed back by the equipment according to the time sequence by adopting the generalized autoregressive conditional variance model GARCH specifically comprises the following steps:
time series Xt：
Xt＝E{Xt|Ψt-1}+εt (3)
Wherein psit-1Represents all time series X obtained at time t-11,…,Xt-1，εtRepresenting the residual error, establishing a description equation for the residual error:
in the formula: ztIs a random variable with a mean value of zero and a variance of 1; p and q are the orders of the model respectively; alpha is alphaiAnd betajAs a parameter to be estimated of the model, for making the conditional variance ht>Requirement of 0 for alphaiAnd betajAre all greater than 0, and alpha is used to make the model wide and smoothiAnd betajThe conditions also need to be satisfied:
∑iαi+∑jβj<1 (6)
in the GARCH model, the parameter alpha of the conditional variance is determined by adopting the maximum likelihood principle0，α1，β1Make an estimate if { X }1,X2,…XTIs a signal produced by the GARCH model, then the likelihood function is defined by the formula:
where h istThe logarithm of the above formula is taken to obtain a log-likelihood function which is obtained by a recursion method as follows:
wherein: x ═ X (X)1,…,Xj)T，h＝(h1,…,hj)TThe limiting condition is formula (6), and the parameter alpha of the model0，α1，β1Solving by a maximization formula (9);
judging whether the equipment fails or not according to the result obtained by the model; if no fault is diagnosed, directly returning no fault information, if fault, going to step 424;
step 424, neural network fault prediction step based on hierarchy: according to the characteristics of the diagnosis object, decomposing the system, establishing a proper comprehensive hierarchical classification model, calculating by using the model to obtain a corresponding fault type, and generating a corresponding maintenance guarantee scheme according to the fault type.
Preferably, the M is 13, the 13-dimensional state information includes an identification information ID of the device, time and location information of the device, and 10 operating states of the device, and the ID is obtained according to a unique serial number of the device when the device leaves a factory or a serial number of a disk of the device; acquiring equipment time and position information according to system time and GPS module information; the working state of the equipment comprises 10 items of data of temperature, humidity, speed, acceleration, air pressure, voltage and flow, and the information acquisition period is 1 second.
Preferably, in step 21, a small amount of 5-dimensional key data including device identification information, time, space information, temperature and humidity information is selected, and the sampling period is 10 seconds.
(III) advantageous effects
The invention can effectively improve the cooperative efficiency of equipment and maintenance support personnel and realize distributed and cooperative active maintenance support.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
fig. 2 is a schematic block diagram of a method implementation of the present invention.
Detailed Description
In order to make the objects, contents, and advantages of the present invention clearer, the following detailed description of the embodiments of the present invention will be made in conjunction with the accompanying drawings and examples.
As shown in fig. 1, the cooperative method for active maintenance and safeguard of equipment of the present invention includes the following steps:
step 1) multi-dimensional original data of a plurality of kinds of equipment sensor sensing equipment are stored locally;
step 2), after sampling the original data of the equipment, selecting a part of key small data to upload periodically and storing the part of key small data to an equipment maintenance guarantee management cloud;
step 3) the maintenance support cloud end evaluates the equipment state according to the uploaded data and the historical data, generates a preliminary equipment maintenance support plan, records a corresponding result and sends the result to the equipment maintenance support mobile terminal;
step 4) the maintenance support personnel connect the maintenance support mobile terminal with the equipment, read the original data of the equipment, further diagnose and verify the state of the equipment, and generate a maintenance support scheme according to a preliminary maintenance support plan and an intelligent algorithm;
and step 5) maintenance support personnel maintain the equipment according to the scheme, and carry out cooperative interaction with a remote expert through the mobile terminal to realize active and cooperative maintenance support of the equipment.
The step 1 comprises the following steps:
step 11: sensing equipment information, and sensing M-dimensional state information X ═ X of system equipment by various sensors1,X2,…,XM]The method comprises basic information such as identification information, time, position information and working state of the equipment, and the information acquisition period is T.
In practice, M may be chosen as 13-dimensional information. Including identification information ID of the device, time and location information of the device, and 10 states of the device operation. The ID can be obtained according to the unique serial number of the equipment when the equipment leaves a factory or the serial number of a disk of the equipment and the like; acquiring equipment time and position information according to system time and GPS module information; the working state of the device can comprise 10 items of data such as temperature, humidity, speed, acceleration, air pressure, voltage, flow and the like. The information acquisition period is 1 second.
Step 12: the method is characterized in that original data of the equipment, namely all collected data are stored, and the data can be stored for only D days generally due to limited local storage.
In practice, data stored for 10 days may be selected.
The step 2 includes:
step 21: screening original data, after screening local data of the equipment, selecting a small amount of N-dimensional (N < M) key data, wherein the sampling period is i x T (i is more than or equal to 1).
In implementation, a small amount of key data may be selected as 5-dimensional data, including device identification information, time, space information, temperature and humidity information. The sampling period was 10 seconds.
Step 22: and uploading the screened data, uploading the screened data according to the guarantee period requirement and the transmission network limit, and storing the data in the cloud server according to a period P days, wherein P is less than or equal to D.
In practice, the uploading period may be selected to be 12 hours, i.e. 0.5 days, and the uploading is performed at each 12 o' clock.
The step 3 comprises the following steps:
step 31: and data preliminary evaluation, namely, when the data is uploaded by the equipment each time, the cloud end combines the historical data of the equipment to carry out preliminary state evaluation on the equipment, and generates a preliminary maintenance guarantee plan of the equipment according to a state evaluation result.
Step 32: the preliminary maintenance and security plan of the equipment is stored after being generated, and the result is directly sent to the mobile terminal of the equipment maintenance and security, and the mobile terminal is generally watched by special maintenance and security personnel.
The step 4 comprises the following steps:
step 41: and verifying the state of the equipment, and connecting the maintenance support mobile terminal with the equipment by a maintenance support worker in a wireless or wired mode according to the requirement of a maintenance support plan, reading the original data of the equipment and verifying whether the state of the equipment is consistent with the support plan.
Step 42: and generating a maintenance support scheme, and generating a maintenance support implementation scheme by the maintenance support mobile terminal according to the original data, the case retrieval, the intelligent algorithm and other contents after the verification result is consistent.
Step 42 specifically includes:
step 421: the inference step comprises: inputting key data of current equipment, searching similar cases in a case library, giving fault diagnosis results and solving measures if the similar cases exist, switching to a deep neural network model based on a time sequence if the similar cases do not exist, searching the cases by adopting a searching mode based on K-neighbor matching, and searching the cases by adopting a searching mode based on the K-neighbor matching specifically comprises the following steps:
each case contains
Species characteristics, failure case Ci(i ═ 1, 2.., n) can be represented by an m-dimensional vector: a. thei＝(ai1，ai2，...，aim)，aij(j ═ 1, 2.., m) is fault case CiThe value of the jth feature of (1);
the similarity between cases is defined as:
wherein 0 is less than or equal to Sim (C)i，Cj)≤1；ωkRepresents the weight of the kth feature in the case feature vector, an
Step 422: aiming at the characteristic of time serialization of the acquired equipment state information sequence, modeling the data characteristic vector fed back by the equipment according to the time sequence by adopting a generalized autoregressive conditional variance model GARCH, and judging whether the equipment fails according to the obtained calculation result;
the modeling of the data characteristic vector fed back by the equipment according to the time sequence by adopting the generalized autoregressive conditional variance model GARCH specifically comprises the following steps:
time series Xt：
Xt＝E{Xt|Ψt-1}+εt (3)
Wherein psit-1Represents all time series X obtained at time t-11,…,Xt-1，εtRepresenting residual errors and describing the residual errorsThe equation:
in the formula: ztIs a random variable with a mean value of zero and a variance of 1; p and q are the orders of the model respectively; alpha is alphaiAnd betajAs a parameter to be estimated of the model, for making the conditional variance ht>Requirement of 0 for alphaiAnd betajAre all greater than 0, and alpha is used to make the model wide and smoothiAnd betajThe conditions also need to be satisfied:
∑iαi+∑jβj<1 (6)
in the GARCH model, the parameter alpha of the conditional variance is determined by adopting the maximum likelihood principle0，α1，β1Make an estimate if { X }1,X2,…XTIs a signal produced by the GARCH model, then the likelihood function is defined by the formula:
where h istThe logarithm of the above formula is taken to obtain a log-likelihood function which is obtained by a recursion method as follows:
wherein: x ═ X (X)1,…,Xj)T，h＝(h1,…,hj)TThe limiting condition is formula (6), and the parameter alpha of the model0，α1，β1Solving by a maximization formula (9);
judging whether the equipment fails or not according to the result obtained by the model; if no fault is diagnosed, directly returning no fault information, if fault, going to step 424;
step 424, neural network fault prediction step based on hierarchy: according to the characteristics of the diagnosis object, decomposing the system, establishing a proper comprehensive hierarchical classification model, calculating by using the model to obtain a corresponding fault type, and generating a corresponding maintenance guarantee scheme according to the fault type. In this embodiment, a neural network is used to construct a classification model corresponding to a fault classification level, each artificial neural network is a three-layer BP network, and a result obtained by calculation using a hierarchical neural network matches a specific type of a fault.
The step 5 comprises the following steps:
step 51: equipment maintenance support, maintenance support personnel carry out the maintenance support to equipment according to maintenance support implementation scheme, for example: actively replacing and maintaining department equipment, maintaining equipment and the like.
Step 52: the experts cooperate, during the maintenance support of the equipment, if the maintenance support personnel encounter the condition that the maintenance support scheme is not appropriate or the problem is unclear, the maintenance support personnel can cooperatively communicate with the remote experts through the maintenance support mobile terminal, the maintenance support terminal transmits information such as original data of the equipment, the maintenance support scheme, on-site video, audio and the like to the remote experts, and the remote experts feed back guidance suggestions and suggestions to the mobile terminal.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (2)
1. The cooperative method for the active maintenance and guarantee of the equipment is characterized by comprising the following steps:
step 1, sensing multi-dimensional original data of equipment by various equipment sensors, and storing the multi-dimensional original data locally;
step 2, after the original data are sampled, selecting partial data to be uploaded regularly and storing the partial data to an equipment maintenance guarantee cloud;
step 3, the maintenance support cloud end evaluates the equipment state according to the uploaded data and the historical data, generates a preliminary equipment maintenance support plan, records a corresponding result and sends the result to the equipment maintenance support mobile terminal;
step 4, connecting the maintenance support mobile terminal with the equipment, reading original data of the equipment, further diagnosing and verifying the state of the equipment, and generating a maintenance support scheme according to a preliminary maintenance support plan and an intelligent algorithm;
step 5, maintenance support personnel maintain the equipment according to the scheme and cooperatively interact with remote personnel through the mobile terminal to realize active and cooperative maintenance support of the equipment;
the step 1 specifically comprises the following steps:
step 11: sensing device information: the system senses the M-dimensional state information X ═ X of system equipment through various sensors1,X2,…,XM]The method comprises the following steps of obtaining basic information including identification information, time, position information and working state of equipment, wherein the information acquisition period is T;
step 12: storage device information: storing M-dimensional data of sensing equipment information in an equipment local area, and storing data for D days;
the step 2 specifically comprises the following steps:
step 21: screening of raw data: after local data of the equipment are screened, selecting a small amount of N-dimensional key data, wherein the sampling period is I '. multidot.T, I' is not less than 1, and N is less than M;
step 22: uploading screened data, uploading the screened data according to the guarantee period requirement and the transmission network limit, and storing the data in a cloud server according to a period P days, wherein P is less than or equal to D;
in step 22, the uploading period is selected to be 12 hours, namely 0.5 day, and uploading is carried out at each 12-point moment;
the step 3 specifically comprises the following steps:
step 31: preliminary evaluation of data: the cloud end carries out preliminary state evaluation on the equipment by combining historical data of the equipment when the equipment uploads data each time, and generates a preliminary maintenance guarantee plan of the equipment according to a state evaluation result;
step 32: the equipment preliminary maintenance support plan is stored after being generated, and the result is directly sent to the equipment maintenance support mobile terminal;
the step 4 specifically comprises the following steps:
step 41: verifying the state of the device: according to the requirements of a maintenance guarantee plan, firstly, connecting a maintenance guarantee mobile terminal with equipment in a wireless or wired mode, reading original data of the equipment, and verifying whether the equipment state is consistent with the guarantee plan or not;
step 42: and (3) generating a maintenance guarantee scheme: after the verification result is consistent, the maintenance support mobile terminal generates a maintenance support implementation scheme;
step 42 specifically includes:
step 421: the inference step comprises: inputting key data of current equipment, searching similar cases in a case library, giving fault diagnosis results and solving measures if the similar cases exist, switching to a deep neural network model based on a time sequence if the similar cases do not exist, searching the cases by adopting a searching mode based on K-neighbor matching, and searching the cases by adopting a searching mode based on the K-neighbor matching specifically comprises the following steps:
each case containing m kinds of features, fault case Ci(i ═ 1, 2, …, n) can be represented by an m-dimensional vector: a. thei＝(ai1，ai2，...，aim)，aij(j ═ 1, 2.., m) is fault case CiThe value of the jth feature of (1);
the similarity between cases is defined as:
wherein 0 is less than or equal to Sim (C)i，Cj)≤1；ωkRepresents the weight of the kth feature in the case feature vector, an
Step 422: aiming at the characteristic of time serialization of the acquired equipment state information sequence, modeling the data characteristic vector fed back by the equipment according to the time sequence by adopting a generalized autoregressive conditional variance model GARCH, and judging whether the equipment fails according to the obtained calculation result;
the modeling of the data characteristic vector fed back by the equipment according to the time sequence by adopting the generalized autoregressive conditional variance model GARCH specifically comprises the following steps:
time series Xt：
Xt＝E{Xt|Ψt-1}+εt (3)
Wherein psit-1Represents all time series X obtained at time t-11,…,Xt-1，εtRepresenting the residual error, establishing a description equation for the residual error:
in the formula: ztIs a random variable with a mean value of zero and a variance of 1; p and q are the orders of the model respectively; alpha is alphaiAnd betajAs a parameter to be estimated of the model, for making the conditional variance ht>Requirement of 0 for alphaiAnd betajAre all greater than 0, and alpha is used to make the model wide and smoothiAnd betajThe conditions also need to be satisfied:
∑iαi+∑jβj<1 (6)
in the GARCH model, the parameter alpha of the conditional variance is determined by adopting the maximum likelihood principle0，α1，β1Make an estimate if { X }1,X2,…XTIs a signal produced by the GARCH model, then the likelihood function is defined by the formula:
where h istThe logarithm of the above formula is taken to obtain a log-likelihood function which is obtained by a recursion method as follows:
wherein: x ═ X (X)1,…,Xj)T，h＝(h1,…,hj)TThe limiting condition is formula (6), and the parameter alpha of the model0，α1，β1Solving by a maximization formula (9);
judging whether the equipment fails or not according to the result obtained by the model; if no fault is diagnosed, directly returning no fault information, if fault, going to step 424;
step 424, neural network fault prediction step based on hierarchy: according to the characteristics of the diagnosis object, decomposing the system, establishing a proper comprehensive hierarchical classification model, calculating by using the model to obtain a corresponding fault type, and generating a corresponding maintenance guarantee scheme according to the fault type.
2. The method of claim 1, wherein in step 21, a small amount of 5-dimensional key data is selected, including device identification information, time, space information, temperature and humidity information, with a sampling period of 10 seconds.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711052317.XA CN107862394B (en) | 2017-10-30 | 2017-10-30 | Equipment active maintenance support cooperative method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711052317.XA CN107862394B (en) | 2017-10-30 | 2017-10-30 | Equipment active maintenance support cooperative method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107862394A CN107862394A (en) | 2018-03-30 |
| CN107862394B true CN107862394B (en) | 2021-07-13 |
Family
ID=61698110
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711052317.XA Active CN107862394B (en) | 2017-10-30 | 2017-10-30 | Equipment active maintenance support cooperative method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107862394B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111160581A (en) * | 2019-12-31 | 2020-05-15 | 成都理工大学 | Energy-saving environment-friendly green circulating packaging management method based on active maintenance |
| CN114461735A (en) * | 2022-04-13 | 2022-05-10 | 天津中新智冠信息技术有限公司 | Industrial and mining data classification method and device and computer equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102529903A (en) * | 2010-12-31 | 2012-07-04 | 上海博泰悦臻电子设备制造有限公司 | Comprehensive vehicle failure detecting system |
| CN104793570A (en) * | 2014-01-16 | 2015-07-22 | 北京腾实信科技股份有限公司 | Portable motor train unit fault processing support equipment and portable motor train unit fault processing support system |
| CN106374382A (en) * | 2016-09-21 | 2017-02-01 | 厦门亿力吉奥信息科技有限公司 | Power network equipment failure maintenance method and system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6834256B2 (en) * | 2002-08-30 | 2004-12-21 | General Electric Company | Method and system for determining motor reliability |
-
2017
- 2017-10-30 CN CN201711052317.XA patent/CN107862394B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102529903A (en) * | 2010-12-31 | 2012-07-04 | 上海博泰悦臻电子设备制造有限公司 | Comprehensive vehicle failure detecting system |
| CN104793570A (en) * | 2014-01-16 | 2015-07-22 | 北京腾实信科技股份有限公司 | Portable motor train unit fault processing support equipment and portable motor train unit fault processing support system |
| CN106374382A (en) * | 2016-09-21 | 2017-02-01 | 厦门亿力吉奥信息科技有限公司 | Power network equipment failure maintenance method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107862394A (en) | 2018-03-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111694879B (en) | Multielement time sequence abnormal mode prediction method and data acquisition monitoring device | |
| WO2021257128A2 (en) | Quantum computing based deep learning for detection, diagnosis and other applications | |
| WO2022052510A1 (en) | Anomaly detection system and method for sterile filling production line | |
| CN111504676A (en) | Equipment fault diagnosis method, device and system based on multi-source monitoring data fusion | |
| CN110321940B (en) | Aircraft telemetry data feature extraction and classification method and device | |
| CN106228270B (en) | Energy consumption prediction method and system for big data driven extrusion equipment | |
| CN107862394B (en) | Equipment active maintenance support cooperative method | |
| CN116455941B (en) | Indoor environment multi-source data transmission method and system based on Internet of things | |
| Huang et al. | Reliable machine prognostic health management in the presence of missing data | |
| CN114842371B (en) | Unsupervised video anomaly detection method | |
| CN114254695A (en) | Spacecraft telemetry data self-adaptive anomaly detection method and device | |
| CN117171686A (en) | Method and system for detecting abnormal data of intelligent power grid based on federal learning | |
| CN110502552B (en) | Classification data conversion method based on fine-tuning conditional probability | |
| CN116861331A (en) | Expert model decision-fused data identification method and system | |
| CN110197289B (en) | Energy-saving equipment management system based on big data | |
| CN117077532A (en) | Multi-model fusion method for life prediction of wind turbine generator | |
| Bond et al. | A hybrid learning approach to prognostics and health management applied to military ground vehicles using time-series and maintenance event data | |
| CN115718861A (en) | Method and system for classifying power users and monitoring abnormal behaviors in high-energy-consumption industry | |
| Figueirêdo et al. | Multivariate real time series data using six unsupervised machine learning algorithms | |
| Berenji et al. | Case-based reasoning for fault diagnosis and prognosis | |
| CN114792026A (en) | Method and system for predicting residual life of aircraft engine equipment | |
| CN117805637A (en) | Battery safety monitoring method and system | |
| He et al. | Application of sparse representation method based on K-SVD-ADMM in anomaly detection of satellite telemetry | |
| CN117495109B (en) | Power stealing user identification system based on neural network | |
| US20230316302A1 (en) | Improving accuracy and efficiency of prediction processes on big data sets using domain based segmentation and time series clustering |
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 | ||
| CB03 | Change of inventor or designer information |
Inventor after: Wang Jinlong Inventor after: Sun Ning Inventor after: Jiao Yasen Inventor after: Fang Zhi Inventor after: Zheng Geng Inventor before: Wang Jinlong Inventor before: Jiao Yasen Inventor before: Fang Zhi Inventor before: Zheng Geng |
|
| CB03 | Change of inventor or designer information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |