CN111581889A - Fault prediction method, system and equipment for heating equipment assembly - Google Patents
Fault prediction method, system and equipment for heating equipment assembly Download PDFInfo
- Publication number
- CN111581889A CN111581889A CN202010456797.1A CN202010456797A CN111581889A CN 111581889 A CN111581889 A CN 111581889A CN 202010456797 A CN202010456797 A CN 202010456797A CN 111581889 A CN111581889 A CN 111581889A
- Authority
- CN
- China
- Prior art keywords
- fault
- time
- model
- circulating pump
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Operations Research (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a fault prediction method, a system and equipment for a heat supply equipment assembly, wherein the fault prediction method comprises the following steps: collecting historical operation maintenance data and fault characteristic data of a heating equipment assembly; inputting historical operation maintenance data and fault characteristic data of the heating equipment assembly into an artificial intelligence model, and training to generate a prediction model; inputting real-time operation maintenance data and fault characteristic data of the heating equipment assembly into a prediction model to obtain a fault prediction result; the invention expects to find out the rule from the historical data of the equipment use by the artificial intelligence technology, predict the fault condition and the fault time of the equipment and achieve the aim of early warning.
Description
Technical Field
The invention relates to the field of heating, in particular to a fault prediction method, a system and equipment for a heating equipment assembly.
Background
The equipment components needing to be overhauled and replaced in the heating system comprise a heat exchanger, a water pump and the like, the heat exchanger and the water pump need to be overhauled regularly, otherwise, once the equipment components break down, heating can be seriously affected.
Adopt artifical timing to overhaul the mode among the prior art and overhaul and change the heating equipment subassembly, degree of automation is low, can not in time discover equipment trouble or latent fault, causes the heat energy extravagant easily, and the human cost is high, leads to heating power company operation cost to increase.
Disclosure of Invention
In order to solve the technical problem, the invention provides a fault prediction method, a system and equipment for a heating equipment assembly.
In order to solve the technical problems, the invention adopts the following technical scheme:
a method of fault prediction for a heating plant assembly comprising the steps of:
the method comprises the following steps: collecting historical operation maintenance data and fault characteristic data of a heating equipment assembly;
step two: inputting historical operation maintenance data and fault characteristic data of the heating equipment assembly into an artificial intelligence model, and training to generate a prediction model;
step three: and inputting real-time operation maintenance data and fault characteristic data of the heating equipment assembly into the prediction model to obtain a fault prediction result.
Specifically, in the second step, the prediction model comprises a scanning layer model and a detection layer model, historical operation maintenance data of the heating equipment assembly is input into the artificial intelligence model, and the scanning layer model is generated through training; inputting real-time operation maintenance data of the heat supply equipment components into a scanning layer model, preliminarily judging the fault condition of each heat supply equipment component, inputting the real-time fault characteristic data of the heat supply equipment components into a detection layer model if a certain heat supply equipment component has the possibility of fault, confirming the possibility of fault again, and acquiring the predicted fault time of the heat supply equipment components.
Specifically, the heat supply equipment assembly comprises a circulating pump and a heat exchange plate, and the detection layer model comprises a circulating pump detection model and a heat exchange plate detection model; inputting historical fault characteristic data of the circulating pump into an artificial intelligence model, training to generate a circulating pump detection model, inputting real-time fault characteristic data of the circulating pump into the circulating pump detection model if a scanning layer judges that a certain circulating pump has the possibility of fault, reconfirming the possibility of the fault of the circulating pump, and acquiring the predicted fault time of the circulating pump; inputting historical fault characteristic data of the heat exchanger into an artificial intelligence model, training to generate a heat exchanger detection model, inputting real-time fault characteristic data of the heat exchanger into the heat exchanger detection model if a scanning layer judges that a certain heat exchanger has the possibility of fault, confirming the possibility of the heat exchanger fault again, and obtaining the predicted fault time of the heat exchanger.
Specifically, the fault characteristic data of the circulating pump comprises the running flow rate of the circulating pump, the running frequency of the circulating pump, the fault condition of the circulating pump, the fault times of the circulating pump, the normal running time of the circulating pump and the fault time of the circulating pump each time.
Specifically, the fault characteristic data of the heat exchange plate includes ion concentration in water, time for detecting the ion concentration in water each time, fault condition of the heat exchange plate, fault frequency of the heat exchange plate, normal operation time of the heat exchange plate, and time for fault occurrence of the heat exchange plate each time.
Specifically, the operation and maintenance data comprises the name of the equipment, the operation time length of the equipment, the maintenance times, the time for starting each maintenance, the maintenance type, the equipment stopping time, the times for influencing a heating system and the times for causing major accidents.
Specifically, in the second step, before the historical operation maintenance data and the fault characteristic data of the heating equipment assembly are input into the artificial intelligence model, invalid data in the historical operation maintenance data need to be removed, and alignment processing is carried out according to a time dimension; and eliminating invalid data in the historical fault characteristic data, and aligning according to a time dimension.
Specifically, the artificial intelligence model is a logistic regression model or a neural network model.
A fault prediction system for a heating plant assembly, comprising:
the data acquisition module is used for acquiring historical operation maintenance data and fault characteristic data of the heat supply equipment assembly;
the model generation module inputs historical operation maintenance data and fault characteristic data of the heating equipment assembly into the artificial intelligence model and trains and generates a prediction model;
and the prediction module is used for inputting the real-time operation maintenance data and the fault characteristic data of the heating equipment assembly into the prediction model to obtain a fault prediction result.
A computer device comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, performs the steps of the failure prediction method.
Compared with the prior art, the invention has the beneficial technical effects that:
1. the invention collects historical operation maintenance data and fault characteristic data, inputs the data into the artificial intelligence model, trains out two layers of prediction models, assists field maintainers to timely and accurately maintain equipment about to break down, reduces heat energy waste caused by untimely overhaul or replacement, and also can reduce the number of field maintainers and save labor cost.
Drawings
Fig. 1 is a flow chart of the failure prediction method of the present invention.
Detailed Description
A preferred embodiment of the present invention will be described in detail below with reference to the accompanying drawings.
The equipment components needing to be overhauled and replaced in the heating system comprise a heat exchanger, a water pump and the like, the heat exchanger and the water pump need to be overhauled regularly, otherwise, once the equipment components break down, heating can be seriously affected.
Adopt artifical timing to overhaul the mode among the prior art and overhaul and change the heating equipment subassembly, degree of automation is low, can not in time discover equipment trouble or latent fault, and the heat energy that causes easily is extravagant, and the human cost is high, leads to heating power company operation cost to increase.
The invention expects to find out the rule from the historical data of the equipment use by the artificial intelligence technology, predict the fault condition and the fault time of the equipment and achieve the aim of early warning.
Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and react in a manner similar to human intelligence, with the primary goal of enabling a machine to perform complex tasks that typically require human intelligence to complete.
Common artificial intelligence models include linear regression models, logistic regression models, decision tree models, bayesian models, support vector machine models, and neural network models.
In this embodiment, the artificial intelligence model is a logistic regression model or a neural network model.
The failure of the heating equipment component has internal rules, a set of system is naturally formed between the equipment and parameters associated with the equipment, and when the equipment is about to fail, the parameters associated with the equipment are changed; similarly, when the parameters related to the equipment are changed, the possibility of the equipment failure can be reflected to a certain extent; the logistic regression model and the neural network model can search the incidence relation between systems in the historical operation maintenance data and fault characteristic data of the heat supply equipment assembly, a prediction model is formed through continuous training, and the fault condition of the heat supply equipment assembly is presumed by collecting parameters related to the fault in real time.
As shown in fig. 1, a fault prediction method for a heating plant assembly includes the steps of:
s1: and collecting historical operation maintenance data and fault characteristic data of the heating equipment assembly.
Specifically, the operation and maintenance data comprises the name of the equipment, the operation time length of the equipment, the maintenance times, the time for starting each maintenance, the maintenance type, the equipment stopping time, the times for influencing a heating system and the times for causing major accidents.
The time of starting maintenance each time is a time point, the equipment outage time is a time period, a certain relation exists between the parameters and equipment faults, but the parameters are considered from the whole heat supply equipment assembly, and the characteristics of each heat supply equipment assembly are not considered, so that a scanning layer model generated by operation maintenance data training can only roughly estimate the possibility of the faults of each heat supply equipment assembly, and if a certain assembly is judged to be possible to be in fault, the real-time fault characteristic parameters of the assembly are input into a corresponding detection layer model to be accurately estimated; since the scanning layer is only roughly estimated, the operation speed is high and the efficiency is high.
S2: and inputting historical operation maintenance data and fault characteristic data of the heating equipment assembly into an artificial intelligence model, and training to generate a prediction model.
Specifically, in the second step, the prediction model comprises a scanning layer model and a detection layer model, historical operation maintenance data of the heating equipment assembly is input into the artificial intelligence model, and the scanning layer model is generated through training; inputting real-time operation maintenance data of the heat supply equipment components into a scanning layer model, preliminarily judging the fault condition of each heat supply equipment component, inputting the real-time fault characteristic data of the heat supply equipment components into a detection layer model if a certain heat supply equipment component has the possibility of fault, confirming the possibility of fault again, and acquiring the predicted fault time of the heat supply equipment components.
Specifically, the heat supply equipment assembly comprises a circulating pump and a heat exchange plate, and the detection layer model comprises a circulating pump detection model and a heat exchange plate detection model; inputting historical fault characteristic data of the circulating pump into an artificial intelligence model, training to generate a circulating pump detection model, inputting real-time fault characteristic data of the circulating pump into the circulating pump detection model if a scanning layer judges that a certain circulating pump has the possibility of fault, reconfirming the possibility of the fault of the circulating pump, and acquiring the predicted fault time of the circulating pump; inputting historical fault characteristic data of the heat exchanger into an artificial intelligence model, training to generate a heat exchanger detection model, inputting real-time fault characteristic data of the heat exchanger into the heat exchanger detection model if a scanning layer judges that a certain heat exchanger has the possibility of fault, confirming the possibility of the heat exchanger fault again, and obtaining the predicted fault time of the heat exchanger.
The training of the prediction model needs to be carried out regularly, the training frequency is increased during the heating season, and the prediction model is updated regularly.
Specifically, the fault characteristic data of the circulating pump comprises the running flow rate of the circulating pump, the running frequency of the circulating pump, the fault condition of the circulating pump, the fault times of the circulating pump, the normal running time of the circulating pump and the fault time of the circulating pump each time.
Specifically, the fault characteristic data of the heat exchange plate includes ion concentration in water, time for detecting the ion concentration in water each time, fault condition of the heat exchange plate, fault frequency of the heat exchange plate, normal operation time of the heat exchange plate, and time for fault occurrence of the heat exchange plate each time.
The circulation pump fault condition refers to the type of the circulation pump fault, such as damage of important parts and damage of non-important parts, and is used for describing the severity of the circulation pump fault; the time when the circulating pump fails each time is a time point, and the failure rule can be found from the time distribution of multiple failures of the circulating pump.
The explanations of the failure condition of the heat exchange plate and the time of each failure of the heat exchange plate are similar to the explanations above.
The heating equipment assembly comprises a circulation pump and a heat exchange plate, which will now be described by taking the circulation pump as an example.
Every heat supply equipment subassembly has different operational environment, carry out the fault prediction to it, different parameters need be considered naturally, in the fault characteristic data of circulating pump, circulating pump operating frequency can embody the fatigue degree of circulating pump, circulating pump operating flow can embody its controllability to rivers, whether the unusual flow condition can appear, and the fault rule of circulating pump has been embodied to the circulating pump fault time, circulating pump normal operating time and the time that the circulating pump broke down each time, through the study to above-mentioned parameter, obtain circulating pump detection model, and gather in the real-time fault characteristic data input circulating pump detection model of circulating pump, confirm once more the possibility that breaks down to this circulating pump, and obtain the prediction fault time of this circulating pump.
Compared with operation maintenance data, the fault characteristic data has more specific considered factors and narrower applicable range, and the generated detection layer model is more accurate in prediction.
The fault prediction result comprises the possibility of the fault of each heating equipment component and the time of the possible fault, the fault prediction result is updated every day, a threshold value can be set, and the equipment information which is most likely to have the fault and most likely to have the fault at the adjacent time is pushed to field maintenance personnel, so that the field maintenance personnel can perform priority maintenance conveniently.
Specifically, in the second step, before the historical operation maintenance data and the fault characteristic data of the heating equipment assembly are input into the artificial intelligence model, invalid data in the historical operation maintenance data need to be removed, and alignment processing is carried out according to a time dimension; and eliminating invalid data in the historical fault characteristic data, and aligning according to a time dimension.
The alignment processing according to the time dimension means that the time points of each piece of operation maintenance data or fault characteristic data are the same.
The invalid data is null data and abnormal data, wherein the abnormal data is data beyond a normal range.
S3: and inputting real-time operation maintenance data and fault characteristic data of the heating equipment assembly into the prediction model to obtain a fault prediction result.
A fault prediction system for a heating plant assembly, comprising:
the data acquisition module is used for acquiring historical operation maintenance data and fault characteristic data of the heat supply equipment assembly;
the model generation module inputs historical operation maintenance data and fault characteristic data of the heating equipment assembly into the artificial intelligence model and trains and generates a prediction model;
and the prediction module is used for inputting the real-time operation maintenance data and the fault characteristic data of the heating equipment assembly into the prediction model to obtain a fault prediction result.
A computer device comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, performs the steps of the failure prediction method.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein, and any reference signs in the claims are not intended to be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (10)
1. A method of fault prediction for a heating plant assembly comprising the steps of:
the method comprises the following steps: collecting historical operation maintenance data and fault characteristic data of a heating equipment assembly;
step two: inputting historical operation maintenance data and fault characteristic data of the heating equipment assembly into an artificial intelligence model, and training to generate a prediction model;
step three: and inputting real-time operation maintenance data and fault characteristic data of the heating equipment assembly into the prediction model to obtain a fault prediction result.
2. A fault prediction method for a heating plant assembly according to claim 1, characterized in that: inputting historical operation maintenance data of the heating equipment assembly into an artificial intelligence model, and training to generate the scanning layer model; inputting real-time operation maintenance data of the heat supply equipment components into a scanning layer model, preliminarily judging the fault condition of each heat supply equipment component, inputting the real-time fault characteristic data of the heat supply equipment components into a detection layer model if a certain heat supply equipment component has the possibility of fault, confirming the possibility of fault again, and acquiring the predicted fault time of the heat supply equipment components.
3. A fault prediction method for a heating plant assembly according to claim 2, characterized in that: the heat supply equipment assembly comprises a circulating pump and a heat exchange plate, and the detection layer model comprises a circulating pump detection model and a heat exchange plate detection model; inputting historical fault characteristic data of the circulating pump into an artificial intelligence model, training to generate a circulating pump detection model, inputting real-time fault characteristic data of the circulating pump into the circulating pump detection model if a scanning layer judges that a certain circulating pump has the possibility of fault, reconfirming the possibility of the fault of the circulating pump, and acquiring the predicted fault time of the circulating pump; inputting historical fault characteristic data of the heat exchanger into an artificial intelligence model, training to generate a heat exchanger detection model, inputting real-time fault characteristic data of the heat exchanger into the heat exchanger detection model if a scanning layer judges that a certain heat exchanger has the possibility of fault, confirming the possibility of the heat exchanger fault again, and obtaining the predicted fault time of the heat exchanger.
4. A fault prediction method for a heating plant assembly according to claim 3, characterized in that: the fault characteristic data of the circulating pump comprises the running flow of the circulating pump, the running frequency of the circulating pump, the fault condition of the circulating pump, the fault times of the circulating pump, the normal running time of the circulating pump and the fault time of the circulating pump at each time.
5. A fault prediction method for a heating plant assembly according to claim 3, characterized in that: the fault characteristic data of the heat exchange plate comprises ion concentration in water, time for detecting the ion concentration in the water each time, fault conditions of the heat exchange plate, fault times of the heat exchange plate, normal operation time of the heat exchange plate and time for the heat exchange plate to break down each time.
6. A fault prediction method for a heating plant assembly according to claim 1, characterized in that: the operation maintenance data comprises the name of the equipment, the operation duration of the equipment, the maintenance times, the time for starting each maintenance, the maintenance type, the equipment stopping time, the times for influencing a heating system and the times for causing major accidents.
7. A fault prediction method for a heating plant assembly according to claim 1, characterized in that: in the second step, before the historical operation maintenance data and the fault characteristic data of the heating equipment assembly are input into the artificial intelligence model, invalid data in the historical operation maintenance data need to be removed, and alignment processing is carried out according to time dimension; and eliminating invalid data in the historical fault characteristic data, and aligning according to a time dimension.
8. A fault prediction method for a heating plant assembly according to claim 1, characterized in that: the artificial intelligence model is a logistic regression model or a neural network model.
9. A fault prediction system for a heating plant assembly, comprising:
the data acquisition module is used for acquiring historical operation maintenance data and fault characteristic data of the heat supply equipment assembly;
the model generation module inputs historical operation maintenance data and fault characteristic data of the heating equipment assembly into the artificial intelligence model and trains and generates a prediction model;
and the prediction module is used for inputting the real-time operation maintenance data and the fault characteristic data of the heating equipment assembly into the prediction model to obtain a fault prediction result.
10. A computer device, characterized by: comprising a memory and a processor, in which a computer program is stored which, when being executed by the processor, carries out the steps of the failure prediction method as claimed in any one of the claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010456797.1A CN111581889B (en) | 2020-05-26 | 2020-05-26 | Fault prediction method, system and equipment for heat supply equipment component |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010456797.1A CN111581889B (en) | 2020-05-26 | 2020-05-26 | Fault prediction method, system and equipment for heat supply equipment component |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111581889A true CN111581889A (en) | 2020-08-25 |
| CN111581889B CN111581889B (en) | 2023-05-26 |
Family
ID=72117702
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010456797.1A Active CN111581889B (en) | 2020-05-26 | 2020-05-26 | Fault prediction method, system and equipment for heat supply equipment component |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111581889B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112346893A (en) * | 2020-11-10 | 2021-02-09 | 深圳市康必达控制技术有限公司 | Fault prediction method, device, terminal and storage medium |
| CN112688836A (en) * | 2021-03-11 | 2021-04-20 | 南方电网数字电网研究院有限公司 | Energy routing equipment online dynamic sensing method based on deep self-coding network |
| CN113282467A (en) * | 2021-05-28 | 2021-08-20 | 青岛海尔科技有限公司 | Information display method and device, storage medium and electronic device |
| CN114707266A (en) * | 2022-03-31 | 2022-07-05 | 江苏苏华泵业有限公司 | Industrial centrifugal pump operation stability prediction system based on artificial intelligence |
| CN115964942A (en) * | 2022-12-19 | 2023-04-14 | 广东邦普循环科技有限公司 | Power battery material firing system heating assembly aging prediction method and system |
| CN116796261A (en) * | 2023-08-16 | 2023-09-22 | 宁波天安菁华电力科技有限公司 | Closed switch equipment mechanical characteristic prediction method based on artificial intelligence |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019050726A1 (en) * | 2017-09-08 | 2019-03-14 | General Electric Company | Method and system to estimate boiler tube failures |
| CN109635997A (en) * | 2018-11-02 | 2019-04-16 | 广州裕申电子科技有限公司 | A kind of prediction technique and system on equipment maintenance opportunity |
-
2020
- 2020-05-26 CN CN202010456797.1A patent/CN111581889B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019050726A1 (en) * | 2017-09-08 | 2019-03-14 | General Electric Company | Method and system to estimate boiler tube failures |
| CN109635997A (en) * | 2018-11-02 | 2019-04-16 | 广州裕申电子科技有限公司 | A kind of prediction technique and system on equipment maintenance opportunity |
Non-Patent Citations (1)
| Title |
|---|
| 吴立金;夏冉;詹红燕;韩新宇;: "基于深度学习的故障预测技术研究" * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112346893A (en) * | 2020-11-10 | 2021-02-09 | 深圳市康必达控制技术有限公司 | Fault prediction method, device, terminal and storage medium |
| CN112688836A (en) * | 2021-03-11 | 2021-04-20 | 南方电网数字电网研究院有限公司 | Energy routing equipment online dynamic sensing method based on deep self-coding network |
| CN113282467A (en) * | 2021-05-28 | 2021-08-20 | 青岛海尔科技有限公司 | Information display method and device, storage medium and electronic device |
| CN114707266A (en) * | 2022-03-31 | 2022-07-05 | 江苏苏华泵业有限公司 | Industrial centrifugal pump operation stability prediction system based on artificial intelligence |
| CN115964942A (en) * | 2022-12-19 | 2023-04-14 | 广东邦普循环科技有限公司 | Power battery material firing system heating assembly aging prediction method and system |
| CN115964942B (en) * | 2022-12-19 | 2023-12-12 | 广东邦普循环科技有限公司 | Aging prediction method and system for heating component of power battery material firing system |
| CN116796261A (en) * | 2023-08-16 | 2023-09-22 | 宁波天安菁华电力科技有限公司 | Closed switch equipment mechanical characteristic prediction method based on artificial intelligence |
| CN116796261B (en) * | 2023-08-16 | 2023-11-07 | 宁波天安菁华电力科技有限公司 | Closed switch equipment mechanical characteristic prediction method based on artificial intelligence |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111581889B (en) | 2023-05-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111581889A (en) | Fault prediction method, system and equipment for heating equipment assembly | |
| Sharma et al. | System failure behavior and maintenance decision making using, RCA, FMEA and FM | |
| JP6159059B2 (en) | Plant operation optimization system and method | |
| Wakiru et al. | Maintenance optimization: application of remanufacturing and repair strategies | |
| Lind et al. | Functional modelling for fault diagnosis and its application for NPP | |
| US20050034023A1 (en) | Energy management system | |
| CN115244482A (en) | Hybrid risk model for maintenance optimization and system for performing such method | |
| Colone et al. | Predictive repair scheduling of wind turbine drive‐train components based on machine learning | |
| CN104392752A (en) | Real-time on-line nuclear reactor fault diagnosis and monitoring system | |
| CN110135064B (en) | Method, system and controller for predicting temperature faults of rear bearing of generator | |
| CN110046182A (en) | A kind of huge hydroelectric power plant's intelligent alarm threshold setting method and system | |
| Wang et al. | A de-ambiguous condition monitoring scheme for wind turbines using least squares generative adversarial networks | |
| Dewangan et al. | Reliability investigation of steam turbine used in thermal power plant | |
| Alamri et al. | Optimisation of preventive maintenance regime based on failure mode system modelling considering reliability | |
| CN116755964A (en) | Fault prediction and health management system for reinforcement server | |
| Ogaji et al. | Novel approach for improving power-plant availability using advanced engine diagnostics | |
| Ramesh et al. | Reliability assessment of cogeneration power plant in textile mill using fault tree analysis | |
| KR20220089853A (en) | Method for Failure prediction and prognostics and health management of renewable energy generation facilities using machine learning technology | |
| Tjernberg | Reliability-Centered Asset Management with Models for Maintenance Optimization and Predictive Maintenance: Including Case Studies for Wind Turbines | |
| Pariaman et al. | Availability improvement methodology in thermal power plant | |
| Cancemi et al. | The application of machine learning for on-line monitoring Nuclear Power Plant performance | |
| Rodriguez-Guerra et al. | Fault-tolerant control study and classification: Case study of a hydraulic-press model simulated in real-time | |
| Zhang | Comparison of data-driven and model-based methodologies of wind turbine fault detection with SCADA data | |
| Ma et al. | Design of Fine Life Cycle Prediction System for Failure of Medical Equipment | |
| Ristić et al. | A short overview on Industry 4.0 in maintenance of hydropower plants |
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 |