CN111174370A - Fault detection method and device, storage medium and electronic device - Google Patents
Fault detection method and device, storage medium and electronic device Download PDFInfo
- Publication number
- CN111174370A CN111174370A CN201811332883.0A CN201811332883A CN111174370A CN 111174370 A CN111174370 A CN 111174370A CN 201811332883 A CN201811332883 A CN 201811332883A CN 111174370 A CN111174370 A CN 111174370A
- Authority
- CN
- China
- Prior art keywords
- model
- data
- signal data
- fault type
- trained
- 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.)
- Pending
Links
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/32—Responding to malfunctions or emergencies
- F24F11/38—Failure diagnosis
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Debugging And Monitoring (AREA)
Abstract
The invention provides a fault detection method and device, a storage medium and an electronic device, wherein the method comprises the following steps: acquiring signal data when intelligent equipment fails; analyzing the signal data by using a first model, and determining a fault type of the intelligent device, wherein the first model is trained by machine learning by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the signal data and the fault type corresponding to the signal data at least solve the problem that the fault type of the intelligent equipment cannot be accurately detected in the related technology.
Description
Technical Field
The present invention relates to the field of communications, and in particular, to a fault detection method and apparatus, a storage medium, and an electronic apparatus.
Background
With the rapid development of science and technology, the development of the air conditioner frequency conversion technology is faster and faster, the control parameters are more and more, and the fault reasons and phenomena are more and more complex; the fault expression mode of the air conditioner in the traditional technology is only that the fault code is displayed through an LED lamp or a nixie tube carried by a hanging air conditioner, which causes great trouble to the use and the maintenance of a client. Meanwhile, the existing hanging air conditioner basically has no fault intelligent diagnosis, detection and control functions, and a few air conditioners only judge the functionality, or do not have an implementation method, or judge the functionality only through external parameters such as environmental parameters and the like. However, the operation of the air conditioner is a dynamic, time-varying, non-linear process, and the manifestation of the fault is uncertain.
Aiming at the problems that the fault type of the intelligent equipment cannot be accurately detected in the related technology and the like, an effective solution is not provided yet.
Disclosure of Invention
The embodiment of the invention provides a fault detection method and device, a storage medium and an electronic device, which are used for at least solving the problems that the fault type of intelligent equipment cannot be accurately detected in the related technology and the like.
According to an embodiment of the present invention, there is provided a fault detection method including: acquiring signal data when intelligent equipment fails; analyzing the signal data by using a first model, and determining a fault type of the intelligent device, wherein the first model is trained by machine learning by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: signal data, and a fault type corresponding to the signal data.
According to another embodiment of the present invention, there is also provided a fault detection apparatus including: the acquisition module is used for acquiring signal data when the intelligent equipment fails; an analysis module for analyzing the signal data using a first model; a determining module, configured to determine a fault type of the smart device, where the first model is trained through machine learning using multiple sets of data, and each set of data in the multiple sets of data includes: signal data, and a fault type corresponding to the signal data.
According to another embodiment of the present invention, there is also provided a storage medium having a computer program stored therein, wherein the computer program is configured to execute any of the above fault detection methods when executed.
According to another embodiment of the present invention, there is also provided an electronic apparatus including a memory in which a computer program is stored and a processor configured to run the computer program to perform any one of the above-described fault detection methods.
According to the invention, signal data when the intelligent equipment fails is obtained; analyzing the signal data by using a first model to determine a fault type of the intelligent device, wherein the first model is trained by machine learning by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: by adopting the technical scheme, the problems that the fault type of the intelligent equipment cannot be accurately detected in the related technology and the like are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a fault detection method according to an embodiment of the invention;
fig. 2 is a block diagram of a configuration of a fault detection apparatus according to an embodiment of the present invention;
fig. 3 is another structural block diagram of a fault detection apparatus according to an embodiment of the present invention;
fig. 4 is a flow chart of a fault detection method according to a preferred embodiment of the present invention.
Detailed Description
The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Example 1
In the present embodiment, a fault detection method is provided, and fig. 1 is a flowchart of a fault detection method according to an embodiment of the present invention, as shown in fig. 1, the flowchart includes the following steps:
step S102, acquiring signal data when the intelligent equipment fails;
step S104, analyzing the data by using a first model, and determining a fault type of the intelligent device, wherein the first model is trained by using multiple groups of data through machine learning, and each group of data in the multiple groups of data comprises: signal data, and a fault type corresponding to the signal data.
According to the invention, the signal data of the intelligent equipment when the intelligent equipment fails can be analyzed by using the first model so as to further determine the fault type of the intelligent equipment, the first model is trained by using multiple groups of data through machine learning, and each group of data in the multiple groups of data comprises: the technical scheme is adopted, the problems that the fault type of the intelligent equipment cannot be accurately detected in the related technology are solved, and a novel technical scheme for confirming the fault type of the intelligent equipment is provided.
The intelligent device in the above technical solution may be a household device such as an air conditioner, and the embodiment of the present invention does not limit this.
In order to accelerate the training speed of the first model and improve the classification accuracy of the fault types, before the data is analyzed by using the first model, the following technical scheme can be further executed:
the Nesterov momentum method and the independent adaptive learning rate method are used in the first model.
And accessing a Softmax classifier to the top layer of the first model, wherein the Softmax classifier is trained through a supervision algorithm, and then adjusting the first model by utilizing a back propagation BP neural network algorithm and a conjugate gradient algorithm to obtain the optimal parameters of the first model.
It should be noted that the first model at least includes: a deep belief network DBN model.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
Example 2
In this embodiment, a fault detection apparatus is further provided, and the apparatus is used to implement the foregoing embodiments and preferred embodiments, and the description of the apparatus is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 2 is a block diagram of a structure of a fault detection apparatus according to an embodiment of the present invention, as shown in fig. 2, the apparatus including:
the acquisition module 20 is configured to acquire signal data when the intelligent device fails;
an analysis module 22 for analyzing the data using a first model;
a determining module 24, configured to determine a fault type of the smart device, where the first model is trained through machine learning using multiple sets of data, and each set of data in the multiple sets of data includes: signal data, and a fault type corresponding to the signal data.
According to the invention, the signal data of the intelligent equipment when the intelligent equipment fails can be analyzed by using the first model so as to further determine the fault type of the intelligent equipment, the first model is trained by using multiple groups of data through machine learning, and each group of data in the multiple groups of data comprises: the technical scheme is adopted, the problems that the fault type of the intelligent equipment cannot be accurately detected in the related technology are solved, and a novel technical scheme for confirming the fault type of the intelligent equipment is provided.
Optionally, as shown in fig. 3, the apparatus further includes:
a module 26 is used for using the Nesterov momentum method and the independent adaptive learning rate method in the first model.
As shown in fig. 3, the apparatus further comprises:
an accessing module 28, configured to access a Softmax classifier at a top layer of the first model, where the Softmax classifier is trained by a supervision algorithm.
The above-mentioned fault detection method is described below with reference to a preferred embodiment, and fig. 4 is a flowchart of the fault detection method according to the preferred embodiment of the present invention, as shown in fig. 4, including the following steps:
step S402: classifying common fault types of the air conditioner, collecting fault type signals of the same type, selecting representative sample data as training sample data, for example, common air conditioner faults are divided into insufficient refrigerant, non-condensable gas, evaporator scaling and the like, and collecting signal data of the air conditioner faults, such as evaporation temperature, condensation temperature, temperature difference of a frozen air inlet and a frozen air outlet and the like as the training sample data;
step S404: a DBN model is constructed through a laminated limited Boltzmann machine RBM, and a Nesterov momentum method and an independent self-adaptive learning rate are added into the DBN model aiming at the problem of the fault classification precision of the conventional DBN model on original signals, so that the training speed is accelerated, and the classification precision is improved;
step S406: accessing a Softmax classifier at the top layer of the pre-trained model, individually training the Softmax classifier by using a supervision algorithm, and then performing global fine adjustment by using a BP neural network algorithm and a conjugate gradient method to obtain the optimal parameters of the model;
step S408: and inputting unknown fault air conditioner state signals to form a test sample set, and inputting the test sample into the trained DBN model to judge the fault type of the test sample air conditioner.
According to the preferred embodiment of the invention, a DBN training model is improved through a Nesterov momentum method and an independent self-adaptive learning rate, a Softmax classifier is trained independently through a supervision algorithm, and global fine adjustment is carried out through a BP neural network algorithm and a conjugate gradient method, so that the training speed of an air conditioner fault detection model is accelerated, an optimal training model is obtained, and the precision of air conditioner fault detection is improved.
Example 3
An embodiment of the present invention further provides a storage medium including a stored program, where the program executes any one of the methods described above.
Alternatively, in the present embodiment, the storage medium may be configured to store program codes for performing the following steps:
s1, acquiring signal data when the intelligent equipment fails;
s2, analyzing the data by using a first model, and determining the fault type of the intelligent device, wherein the first model is trained by machine learning by using multiple groups of data, and each group of data in the multiple groups of data comprises: signal data, and a fault type corresponding to the signal data.
Optionally, the storage medium is further arranged to store program code for performing the steps of:
s3, using a Nesterov momentum method and an independent adaptive learning rate method in the first model.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method of fault detection, comprising:
acquiring signal data when intelligent equipment fails;
analyzing the signal data by using a first model, and determining a fault type of the intelligent device, wherein the first model is trained by machine learning by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: signal data, and a fault type corresponding to the signal data.
2. The method of claim 1, wherein prior to analyzing the signal data using the first model, the method further comprises:
the Nesterov momentum method and the independent adaptive learning rate method are used in the first model.
3. The method of claim 1, wherein prior to analyzing the signal data using the first model, the method further comprises:
accessing a Softmax classifier at a top level of the first model, wherein the Softmax classifier is trained by a supervised algorithm.
4. The method of claim 3, wherein after accessing a Softmax classifier at a top level of the first model, the method further comprises:
and adjusting the first model by utilizing a back propagation BP neural network algorithm and a conjugate gradient algorithm.
5. The method according to any of claims 1-4, characterized in that the first model comprises at least: a deep belief network DBN model.
6. A fault detection device, comprising:
the acquisition module is used for acquiring signal data when the intelligent equipment fails;
an analysis module for analyzing the signal data using a first model;
a determining module, configured to determine a fault type of the smart device, where the first model is trained through machine learning using multiple sets of data, and each set of data in the multiple sets of data includes: signal data, and a fault type corresponding to the signal data.
7. The apparatus of claim 6, further comprising:
a use module for using a Nesterov momentum method and an independent adaptive learning rate method in the first model.
8. The apparatus of claim 6, further comprising:
an access module configured to access a Softmax classifier at a top layer of the first model, wherein the Softmax classifier is trained by a supervision algorithm.
9. A storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any of claims 1 to 5 when executed.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811332883.0A CN111174370A (en) | 2018-11-09 | 2018-11-09 | Fault detection method and device, storage medium and electronic device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811332883.0A CN111174370A (en) | 2018-11-09 | 2018-11-09 | Fault detection method and device, storage medium and electronic device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111174370A true CN111174370A (en) | 2020-05-19 |
Family
ID=70646066
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811332883.0A Pending CN111174370A (en) | 2018-11-09 | 2018-11-09 | Fault detection method and device, storage medium and electronic device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111174370A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111770427A (en) * | 2020-06-24 | 2020-10-13 | 杭州海康威视数字技术股份有限公司 | Microphone array detection method, device, equipment and storage medium |
| CN111981635A (en) * | 2020-07-21 | 2020-11-24 | 沈阳安新自动化控制有限公司 | Central air conditioner fault prediction and diagnosis method adopting double intelligent algorithms |
| CN112035286A (en) * | 2020-08-25 | 2020-12-04 | 海尔优家智能科技（北京）有限公司 | Method and device for determining fault cause, storage medium and electronic device |
| CN112051078A (en) * | 2020-07-31 | 2020-12-08 | 海尔优家智能科技（北京）有限公司 | Target device fault detection method and device, storage medium and electronic device |
| CN112503721A (en) * | 2020-11-20 | 2021-03-16 | 国网江苏综合能源服务有限公司 | Split type air conditioner fault identification method based on probabilistic neural network |
| CN112887885A (en) * | 2021-01-12 | 2021-06-01 | 天津大学 | Hearing aid fault automatic detection system and hearing aid system |
| CN113433409A (en) * | 2021-06-30 | 2021-09-24 | 青岛科技大学 | Electric automobile IGBT type common direct current bus charging equipment fault diagnosis method based on deep learning |
| CN115031357A (en) * | 2022-05-10 | 2022-09-09 | 南京信息工程大学 | Novel voting strategy-based fault diagnosis method suitable for different types of fault characteristics |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20010003490A (en) * | 1999-06-23 | 2001-01-15 | 구자홍 | Detection device and method of incorrected connection for multiple airconditioner |
| US20090281974A1 (en) * | 2008-04-08 | 2009-11-12 | Infosys Technologies Limited | System and method for adaptive data masking |
| CN105805893A (en) * | 2016-04-15 | 2016-07-27 | 珠海格力电器股份有限公司 | Fault detecting method and device for air conditioner |
| CN106706031A (en) * | 2016-11-23 | 2017-05-24 | 国家电网公司 | On-line monitoring fault diagnosis system and method for artificial neural network high voltage circuit breaker |
| CN106769048A (en) * | 2017-01-17 | 2017-05-31 | 苏州大学 | Adaptive deep confidence network bearing fault diagnosis method based on Nesterov momentum method |
| CN107679649A (en) * | 2017-09-13 | 2018-02-09 | 珠海格力电器股份有限公司 | A kind of failure prediction method of electrical equipment, device, storage medium and electrical equipment |
| CN108416363A (en) * | 2018-01-30 | 2018-08-17 | 平安科技（深圳）有限公司 | Generation method, device, computer equipment and the storage medium of machine learning model |
-
2018
- 2018-11-09 CN CN201811332883.0A patent/CN111174370A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20010003490A (en) * | 1999-06-23 | 2001-01-15 | 구자홍 | Detection device and method of incorrected connection for multiple airconditioner |
| US20090281974A1 (en) * | 2008-04-08 | 2009-11-12 | Infosys Technologies Limited | System and method for adaptive data masking |
| CN105805893A (en) * | 2016-04-15 | 2016-07-27 | 珠海格力电器股份有限公司 | Fault detecting method and device for air conditioner |
| CN106706031A (en) * | 2016-11-23 | 2017-05-24 | 国家电网公司 | On-line monitoring fault diagnosis system and method for artificial neural network high voltage circuit breaker |
| CN106769048A (en) * | 2017-01-17 | 2017-05-31 | 苏州大学 | Adaptive deep confidence network bearing fault diagnosis method based on Nesterov momentum method |
| CN107679649A (en) * | 2017-09-13 | 2018-02-09 | 珠海格力电器股份有限公司 | A kind of failure prediction method of electrical equipment, device, storage medium and electrical equipment |
| CN108416363A (en) * | 2018-01-30 | 2018-08-17 | 平安科技（深圳）有限公司 | Generation method, device, computer equipment and the storage medium of machine learning model |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111770427A (en) * | 2020-06-24 | 2020-10-13 | 杭州海康威视数字技术股份有限公司 | Microphone array detection method, device, equipment and storage medium |
| CN111981635A (en) * | 2020-07-21 | 2020-11-24 | 沈阳安新自动化控制有限公司 | Central air conditioner fault prediction and diagnosis method adopting double intelligent algorithms |
| CN111981635B (en) * | 2020-07-21 | 2021-12-31 | 沈阳安新自动化控制有限公司 | Central air conditioner fault prediction and diagnosis method adopting double intelligent algorithms |
| CN112051078A (en) * | 2020-07-31 | 2020-12-08 | 海尔优家智能科技（北京）有限公司 | Target device fault detection method and device, storage medium and electronic device |
| CN112035286A (en) * | 2020-08-25 | 2020-12-04 | 海尔优家智能科技（北京）有限公司 | Method and device for determining fault cause, storage medium and electronic device |
| CN112503721A (en) * | 2020-11-20 | 2021-03-16 | 国网江苏综合能源服务有限公司 | Split type air conditioner fault identification method based on probabilistic neural network |
| CN112887885A (en) * | 2021-01-12 | 2021-06-01 | 天津大学 | Hearing aid fault automatic detection system and hearing aid system |
| CN113433409A (en) * | 2021-06-30 | 2021-09-24 | 青岛科技大学 | Electric automobile IGBT type common direct current bus charging equipment fault diagnosis method based on deep learning |
| CN115031357A (en) * | 2022-05-10 | 2022-09-09 | 南京信息工程大学 | Novel voting strategy-based fault diagnosis method suitable for different types of fault characteristics |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111174370A (en) | Fault detection method and device, storage medium and electronic device | |
| US20200387785A1 (en) | Power equipment fault detecting and positioning method of artificial intelligence inference fusion | |
| CN110009171B (en) | User behavior simulation method, device, equipment and computer readable storage medium | |
| CN112393931B (en) | Detection method, detection device, electronic equipment and computer readable medium | |
| CN102651088B (en) | Classification method for malicious code based on A_Kohonen neural network | |
| CN109447156B (en) | Method and apparatus for generating a model | |
| CN105303179A (en) | Fingerprint identification method and fingerprint identification device | |
| CN110427893A (en) | A kind of specific emitter identification method, apparatus and computer storage medium based on convolutional neural networks | |
| CN111028016A (en) | Sales data prediction method and device and related equipment | |
| CN112765890B (en) | Dynamic domain adaptive network-based multi-working-condition rotating machine residual life prediction method | |
| CN109543720B (en) | Wafer map defect mode identification method based on countermeasure generation network | |
| CN109623489B (en) | Improved machine tool health state evaluation method and numerical control machine tool | |
| CN109886328B (en) | Electric vehicle charging facility fault prediction method and system | |
| CN107622801A (en) | The detection method and device of disease probability | |
| CN107942956A (en) | Information processor, information processing method, message handling program and recording medium | |
| CN110175640A (en) | A kind of Fault Diagnosis Method of Electro-hydraulic based on machine learning | |
| CN109886337B (en) | Depth measurement learning method and system based on self-adaptive sampling | |
| CN113705699A (en) | Sample abnormity detection method, device, equipment and medium based on machine learning | |
| CN110969200A (en) | Image target detection model training method and device based on consistency negative sample | |
| CN108805174A (en) | clustering method and device | |
| CN106203103A (en) | The method for detecting virus of file and device | |
| CN109145981B (en) | Deep learning automatic model training method and equipment | |
| CN111595541A (en) | Multi-dimensional structure damage identification method based on mass vibration transmissibility data convolutional neural network processing | |
| CN114216682A (en) | Service life prediction method and device of rolling bearing based on TCN and BLS | |
| CN112801423A (en) | Method and device for identifying abnormity of air quality monitoring data and storage medium |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200519 |