CN113392936B - Oven fault diagnosis method based on machine learning - Google Patents
Oven fault diagnosis method based on machine learning Download PDFInfo
- Publication number
- CN113392936B CN113392936B CN202110777869.7A CN202110777869A CN113392936B CN 113392936 B CN113392936 B CN 113392936B CN 202110777869 A CN202110777869 A CN 202110777869A CN 113392936 B CN113392936 B CN 113392936B
- Authority
- CN
- China
- Prior art keywords
- oven
- temperature
- fault
- heating
- calculating
- 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
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000003745 diagnosis Methods 0.000 title claims abstract description 22
- 238000010801 machine learning Methods 0.000 title claims abstract description 14
- 238000010438 heat treatment Methods 0.000 claims abstract description 73
- 238000012549 training Methods 0.000 claims abstract description 9
- 238000001514 detection method Methods 0.000 claims description 36
- 239000011159 matrix material Substances 0.000 claims description 18
- 239000006185 dispersion Substances 0.000 claims description 7
- 238000012544 monitoring process Methods 0.000 claims description 3
- 230000005856 abnormality Effects 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M99/00—Subject matter not provided for in other groups of this subclass
- G01M99/002—Thermal testing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M99/00—Subject matter not provided for in other groups of this subclass
- G01M99/005—Testing of complete machines, e.g. washing-machines or mobile phones
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M99/00—Subject matter not provided for in other groups of this subclass
- G01M99/008—Subject matter not provided for in other groups of this subclass by doing functionality tests
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Software Systems (AREA)
- Medical Informatics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Electric Stoves And Ranges (AREA)
Abstract
The invention discloses an oven fault diagnosis method based on machine learning, which comprises the following steps: s1, acquiring temperature information of heating areas of a faultless oven in a plurality of heating states to form a characteristic data set P A (ii) a S2, collecting temperature information of heating areas of the faulty oven in a plurality of heating states to form a characteristic data set P B (ii) a S3, adding a label to each feature data of the two feature data sets; s4, constructing a classifier by adopting a machine learning method, and training the classifier to obtain a classifier with mature training; and S5, carrying out fault diagnosis on the oven to be detected based on the trained classifier, and judging whether the oven to be detected has faults or not. The invention can realize the fault diagnosis of the oven based on the high temperature condition of the oven, the temperature fluctuation condition within a certain time and the temperature fluctuation conditions of different heating points, and has higher diagnosis accuracy.
Description
Technical Field
The invention relates to oven fault diagnosis, in particular to an oven fault diagnosis method based on machine learning.
Background
The oven is a common household appliance in people's life, and before the oven leaves a factory from a manufacturer or after the oven is returned to the factory for maintenance, fault diagnosis needs to be carried out on the oven frequently to judge whether the oven can be used as a qualified product leaving the factory or a qualified product for maintenance; however, at present, the diagnosis method is often to see whether the temperature is higher than the threshold value when the oven is operated at the set temperature.
However, this diagnostic method can diagnose only a high temperature abnormality, cannot diagnose a temperature fluctuation abnormality at different heating points in the heating region, and cannot diagnose a temperature fluctuation abnormality during the heating continuation.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an oven fault diagnosis method based on machine learning, which can realize fault diagnosis of an oven based on the high-temperature condition of the oven, the temperature fluctuation condition within a certain time and the temperature fluctuation conditions of different heating points and has higher diagnosis accuracy.
The purpose of the invention is realized by the following technical scheme: a machine learning-based oven fault diagnosis method comprises the following steps:
s1, collecting temperature information of heating areas of a plurality of fault-free ovens in a heating state to obtain temperature data of each heating area of the fault-free ovens, extracting characteristic data of each fault-free oven according to the collected information to form a characteristic data set P A ;
S2, acquiring temperature information of the heating areas of the faulty ovens in a plurality of heating states to obtain temperature data of the heating areas of the faulty ovens, extracting characteristic data of each faulty oven according to the acquired information to form a characteristic data set P B ;
S3, characteristic data set P is subjected to A The label y is added to each feature data in the feature data set P, and the feature data set P is labeled with 0 B The tag y of each feature data in (1);
s4, adopting a machine learning method to construct a classifier, and according to the feature data set P A And a feature data set P B Training the classifier by the data to obtain a trained classifier;
and S5, carrying out fault diagnosis on the oven to be detected based on the trained classifier, and judging whether the oven to be detected has faults or not.
The step S1 includes the sub-steps of:
s101, for any fault-free oven, arranging a temperature detection array in a heating area of the fault-free oven, wherein the temperature detection array comprises m rows and n columns of m × n temperature sensors; starting a fault-free oven, and entering a heating state after the fault-free oven is preheated for a set time;
s102, temperature acquisition is carried out on a heating area of the faultless oven in a heating state by using a temperature detection array, and an obtained temperature information matrix K A :
Wherein,the signals which are collected by the temperature sensors in the ith row and the jth column are represented, i is 1,2, …, m, j is 1,2, …, n;
Calculating the discrete degree p of the detection results of different temperature sensors A :
S104, repeatedly executing the steps S102 to S103 for T times at different moments in the heating state of the fault-free oven to obtain T temperature average valuesAnd calculating T discrete degreesWhereinRepresents the average temperature value obtained by the t repeated execution process,represents the discrete degree obtained by the T repeated execution process, T is 1,2, …, T;
S106, repeating the steps S101 to S105 for each fault-free oven to obtain corresponding characteristic dataForming a feature data set P A :
Where M represents the total number of non-faulty ovens.
The step S2 includes the following sub-steps:
s201, for any fault oven, arranging a temperature detection array in a heating area of the fault oven, wherein the temperature detection array comprises m rows and n columns of m × n temperature sensors; starting a fault oven, and entering a heating state after the fault oven is preheated for a set time;
s202, temperature acquisition is carried out on a heating area of the fault oven in a heating state by using a temperature detection array, and an obtained temperature information matrix K B :
Wherein,the signal collected by the temperature sensor in the ith row and the jth column is represented, i is 1,2, …, m, j is 1,2, …, n;
Calculating the discrete degree p of the detection results of different temperature sensors B :
S204, repeatedly executing the steps S202 to S203 for T times at different moments in the heating state of the fault oven to obtain T temperature average valuesAnd calculating T discrete degreesWhereinRepresents the average temperature value obtained by the t repeated execution process,represents the discrete degree obtained by the T repeated execution process, T is 1,2, …, T;
S206, for each fault oven, repeating the steps S201 to S205 to obtain corresponding characteristic dataForming a feature data set P B :
Where N represents the total number of failed ovens.
The step S5 includes:
s501, for an oven to be tested, arranging a temperature detection array in a heating area of the oven, wherein the temperature detection array comprises m rows and n columns of m × n temperature sensors; starting the oven to be tested, and entering a heating state after the oven to be tested is preheated for a set time;
s502, temperature acquisition is carried out on a heating area of the oven to be detected in a heating state by utilizing a temperature detection array, and an obtained temperature information matrix K C :
Wherein,the signal collected by the temperature sensor in the ith row and the jth column is represented, i is 1,2, …, m, j is 1,2, …, n;
s503, judging whether temperature information larger than a set threshold exists in the signals acquired by the temperature sensors:
if the temperature information larger than the set threshold exists, monitoring the temperature sensor corresponding to the temperature information within the duration h seconds, if the temperature value output by the temperature sensor within the h seconds does not recover below the set threshold, judging that the oven to be tested is in failure, and if the temperature value output by the temperature sensor within the h seconds recovers below the set threshold, entering the step S504;
if the temperature information larger than the set threshold does not exist, directly entering step S504;
Calculating the discrete degree p of the detection results of different temperature sensors C :
S505, repeatedly executing the steps S502 to S504 for T times at different moments of the oven to be tested in the heating state to obtain T temperature average valuesAnd calculating T discrete degreesWhereinRepresents the average temperature value obtained by the t-th repeated execution process,represents the discrete degree obtained by the repeated execution process for the T time, wherein T is 1,2, …, T;
judging whether the dispersion degree Q is larger than a preset threshold value:
if so, determining that the oven to be tested has a fault;
if not, go to step S508;
s508, feature data are combinedAnd sending the data into a classifier with mature training, if the classifier outputs 0, determining that the oven to be tested has no fault, and if the classifier outputs 1, determining that the oven to be tested has fault.
The beneficial effects of the invention are: the invention can realize the fault diagnosis of the oven based on the high temperature condition of the oven, the temperature fluctuation condition within a certain time and the temperature fluctuation conditions of different heating points, and has higher diagnosis accuracy.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, a method for diagnosing oven faults based on machine learning includes the following steps:
s1, collecting temperature information of heating areas of a plurality of fault-free ovens in a heating state to obtain temperature data of each heating area of the fault-free ovens, extracting characteristic data of each fault-free oven according to the collected information to form a characteristic data set P A ;
S2, acquiring temperature information of the heating areas of the faulty ovens in a plurality of heating states to obtain temperature data of the heating areas of the faulty ovens, extracting characteristic data of each faulty oven according to the acquired information to form a characteristic data set P B ;
S3, characteristic data set P is subjected to A Is added with a label y of 0, and the feature data set P is added B The tag y of each feature data in (1);
s4, adopting a machine learning method to construct a classifier and carrying out root feature data set P A And a feature data set P B Training the classifier by the data to obtain a trained classifier;
and S5, carrying out fault diagnosis on the oven to be detected based on the trained classifier, and judging whether the oven to be detected has faults or not.
The step S1 includes the sub-steps of:
s101, for any fault-free oven, arranging a temperature detection array in a heating area of the fault-free oven, wherein the temperature detection array comprises m rows and n columns of m × n temperature sensors; starting a fault-free oven, and entering a heating state after the fault-free oven is preheated for a set time;
s102, temperature acquisition is carried out on a heating area of the faultless oven in a heating state by using a temperature detection array, and an obtained temperature information matrix K A :
Wherein,the signal collected by the temperature sensor in the ith row and the jth column is represented, i is 1,2, …, m, j is 1,2, …, n;
Calculating the discrete degree p of the detection results of different temperature sensors A :
S104, repeatedly executing the steps S102 to S103 for T times at different moments in the heating state of the fault-free oven to obtain T temperature average valuesAnd calculate accordinglyGiving T discrete degreesWhereinRepresents the average temperature value obtained by the t repeated execution process,represents the discrete degree obtained by the repeated execution process for the T time, wherein T is 1,2, …, T;
S106, repeating the steps S101 to S105 for each fault-free oven to obtain corresponding characteristic dataForming a feature data set P A :
Where M represents the total number of non-faulty ovens.
The step S2 includes the following sub-steps:
s201, for any fault oven, arranging a temperature detection array in a heating area of the fault oven, wherein the temperature detection array comprises m rows and n columns of m × n temperature sensors; starting a fault oven, and entering a heating state after the fault oven is preheated for a set time;
s202, temperature acquisition is carried out on a heating area of the fault oven in a heating state by using a temperature detection array, and an obtained temperature information matrix K B :
Wherein,the signal collected by the temperature sensor in the ith row and the jth column is represented, i is 1,2, …, m, j is 1,2, …, n;
Calculating the discrete degree p of the detection results of different temperature sensors B :
S204, repeatedly executing the steps S202 to S203 for T times at different moments in the heating state of the fault oven to obtain T temperature average valuesAnd calculating T discrete degreesWhereinRepresents the average temperature value obtained by the t repeated execution process,represents the discrete degree obtained by the repeated execution process for the T time, wherein T is 1,2, …, T;
S206, for each fault oven, repeating the steps S201 to S205 to obtain corresponding characteristic dataForming a feature data set P B :
Where N represents the total number of failed ovens.
In step S4, the feature data set P is extracted A And a feature data set P B The feature data in the method is used as the input of a classifier, the label corresponding to the feature data is used as the expected output of the classifier to realize the training of the classifier, and a feature data set P is adopted A And a feature data set P B After each feature data in the data is trained, a mature classifier is obtained;
the step S5 includes:
s501, for an oven to be tested, arranging a temperature detection array in a heating area of the oven, wherein the temperature detection array comprises m rows and n columns of m × n temperature sensors; starting the oven to be tested, and entering a heating state after the oven to be tested is preheated for a set time;
s502, temperature acquisition is carried out on a heating area of the oven to be detected in a heating state by using a temperature detection array, and an obtained temperature information matrix K C :
Wherein,the signal collected by the temperature sensor in the ith row and the jth column is represented, i is 1,2, …, m, j is 1,2, …, n;
s503, judging whether temperature information larger than a set threshold exists in the signals collected by the temperature sensors:
if the temperature information larger than the set threshold exists, monitoring the temperature sensor corresponding to the temperature information within the duration h seconds, if the temperature value output by the temperature sensor within the h seconds does not recover below the set threshold, judging that the oven to be tested is in failure, and if the temperature value output by the temperature sensor within the h seconds recovers below the set threshold, entering the step S504;
if the temperature information larger than the set threshold does not exist, directly entering step S504;
Calculating the discrete degree p of the detection results of different temperature sensors C :
S505, repeatedly executing the steps S502 to S504 for T times at different moments of the oven to be tested in the heating state to obtain T temperature average valuesAnd calculating T discrete degreesWhereinRepresents the average temperature value obtained by the t-th repeated execution process,represents the discrete degree obtained by the repeated execution process for the T time, wherein T is 1,2, …, T;
judging whether the dispersion degree Q is greater than a preset threshold value:
if so, determining that the oven to be tested has a fault;
if not, go to step S508;
s508, feature data are combinedAnd sending the data into a classifier with mature training, if the classifier outputs 0, determining that the oven to be tested has no fault, and if the classifier outputs 1, determining that the oven to be tested has fault.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; the above embodiments are merely provided to help understand the method of the present invention and its core idea; meanwhile, for a person skilled in the art, according to the idea of the present invention, certain modifications or substitutions may be made in the specific embodiments and the application range; and these modifications or substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (3)
1. A machine learning-based oven fault diagnosis method is characterized in that: the method comprises the following steps:
s1, collecting temperature information of heating areas of a faultless oven in a plurality of heating states,obtaining the temperature data of the heating areas of the fault-free ovens, extracting the characteristic data of each fault-free oven according to the collected information, and forming a characteristic data set P A ;
The step S1 includes the sub-steps of:
s101, for any fault-free oven, arranging a temperature detection array in a heating area of the fault-free oven, wherein the temperature detection array comprises m rows and n columns of m × n temperature sensors; starting a fault-free oven, and entering a heating state after the fault-free oven is preheated for a set time;
s102, temperature acquisition is carried out on a heating area of the faultless oven in a heating state by using a temperature detection array, and an obtained temperature information matrix K A :
Wherein,the signal is acquired by a temperature sensor in the ith row and the jth column, i is 1,2, and m, j is 1,2, n;
Calculating the discrete degree p of the detection results of different temperature sensors A :
S104, repeatedly executing the steps S102 to S103 for T times at different moments in the heating state of the fault-free ovenObtaining T temperature average valuesAnd calculating T discrete degreesWhereinRepresents the average temperature value obtained by the t repeated execution process,represents the discrete degree obtained by the repeated execution process for the T time, wherein T is 1, 2.
S106, repeating the steps S101 to S105 for each fault-free oven to obtain corresponding characteristic dataForming a feature data set P A :
Wherein M represents the total number of non-faulty ovens;
s2, acquiring temperature information of the heating areas of the faulty ovens in a plurality of heating states to obtain temperature data of the heating areas of the faulty ovens, extracting characteristic data of each faulty oven according to the acquired information to form a characteristic data set P B ;
S3, characteristic data set P is subjected to A Is added with a label y of 0, and the feature data set P is added B Each feature data in (1) is added with a label y;
s4, adopting a machine learning method to construct a classifier, and according to the feature data set P A And a feature data set P B Training the classifier by the data to obtain a trained classifier;
and S5, carrying out fault diagnosis on the oven to be detected based on the trained classifier, and judging whether the oven to be detected has faults or not.
2. The machine learning-based oven fault diagnosis method according to claim 1, characterized in that: the step S2 includes the following sub-steps:
s201, for any fault oven, arranging a temperature detection array in a heating area of the fault oven, wherein the temperature detection array comprises m rows and n columns of m × n temperature sensors; starting a fault oven, and entering a heating state after the fault oven is preheated for a set time;
s202, temperature acquisition is carried out on a heating area of the fault oven in a heating state by using a temperature detection array, and an obtained temperature information matrix K B :
Wherein,the signal is acquired by a temperature sensor in the ith row and the jth column, i is 1,2, and m, j is 1,2, n;
Calculating the dispersion of the detection results of different temperature sensorsDegree p B :
S204, repeatedly executing the steps S202 to S203 for T times at different moments in the heating state of the fault oven to obtain T temperature average valuesAnd calculating T discrete degreesWhereinRepresents the average temperature value obtained by the t repeated execution process,represents the discrete degree obtained by the T-th repeated execution process, wherein T is 1, 2.
S206, for each fault oven, repeating the steps S201 to S205 to obtain corresponding characteristic dataForming a feature data set P B :
Where N represents the total number of failed ovens.
3. The machine learning-based oven fault diagnosis method according to claim 1, characterized in that: the step S5 includes:
s501, for an oven to be tested, arranging a temperature detection array in a heating area of the oven, wherein the temperature detection array comprises m rows and n columns of m × n temperature sensors; starting the oven to be tested, and entering a heating state after the oven to be tested is preheated for a set time;
s502, temperature acquisition is carried out on a heating area of the oven to be detected in a heating state by using a temperature detection array, and an obtained temperature information matrix K C :
Wherein,the signal is acquired by the temperature sensor in the ith row and the jth column, i is 1,2, …, m, j is 1,2,.
S503, judging whether temperature information larger than a set threshold exists in the signals collected by the temperature sensors:
if the temperature information larger than the set threshold exists, monitoring the temperature sensor corresponding to the temperature information within the duration h seconds, if the temperature value output by the temperature sensor within the h seconds does not recover below the set threshold, judging that the oven to be tested is in failure, and if the temperature value output by the temperature sensor within the h seconds recovers below the set threshold, entering the step S504;
if the temperature information larger than the set threshold does not exist, directly entering step S504;
Calculating the discrete degree p of the detection results of different temperature sensors C :
S505, repeatedly executing the steps S502 to S504 for T times at different moments of the oven to be tested in the heating state to obtain T temperature average valuesAnd calculating T discrete degreesWhereinRepresents the average temperature value obtained by the t repeated execution process,represents the discrete degree obtained by the repeated execution process for the T time, wherein T is 1,2, …, T;
judging whether the dispersion degree Q is larger than a preset threshold value:
if so, determining that the oven to be tested has a fault;
if not, go to step S508;
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110777869.7A CN113392936B (en) | 2021-07-09 | 2021-07-09 | Oven fault diagnosis method based on machine learning |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110777869.7A CN113392936B (en) | 2021-07-09 | 2021-07-09 | Oven fault diagnosis method based on machine learning |
Publications (2)
Publication Number | Publication Date |
---|---|
CN113392936A CN113392936A (en) | 2021-09-14 |
CN113392936B true CN113392936B (en) | 2022-09-02 |
Family
ID=77625688
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202110777869.7A Active CN113392936B (en) | 2021-07-09 | 2021-07-09 | Oven fault diagnosis method based on machine learning |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113392936B (en) |
Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116026889B (en) * | 2022-12-30 | 2023-12-01 | 苏州春田机械有限公司 | CCD detection device, system and control method for production line |
CN117740083B (en) * | 2024-02-19 | 2024-05-10 | 达斯玛环境科技(北京)有限公司 | Method, system, equipment and storage medium for monitoring faults of stirrer |
CN118051744B (en) * | 2024-04-16 | 2024-06-28 | 天津君磊科技有限公司 | Waterproof signal connector fault diagnosis method based on data processing |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106897542A (en) * | 2017-04-18 | 2017-06-27 | 浙江中烟工业有限责任公司 | Based on the cigarette cut tobacco segment fault diagnostic method that notable failure variable is extracted |
CN108204892A (en) * | 2018-01-24 | 2018-06-26 | 重庆邮电大学 | Roller set equipment fault detection method based on array-type flexible pressure sensor |
CN110361193A (en) * | 2019-04-04 | 2019-10-22 | 浙江运达风电股份有限公司 | Method for distinguishing is known for wind generating set pitch control bearing fault |
Family Cites Families (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP2478423A1 (en) * | 2009-09-17 | 2012-07-25 | Siemens Aktiengesellschaft | Supervised fault learning using rule-generated samples for machine condition monitoring |
CN106483449B (en) * | 2016-09-09 | 2019-01-25 | 电子科技大学 | Analog-circuit fault diagnosis method based on deep learning and Complex eigenvalues |
US11308049B2 (en) * | 2016-09-16 | 2022-04-19 | Oracle International Corporation | Method and system for adaptively removing outliers from data used in training of predictive models |
CN108303264B (en) * | 2017-01-13 | 2020-03-20 | 华为技术有限公司 | Cloud-based vehicle fault diagnosis method, device and system |
KR101967065B1 (en) * | 2017-10-18 | 2019-04-08 | 울산대학교 산학협력단 | Fault diagnosis apparatus and method for robust to environmental change |
CN108414923A (en) * | 2018-02-05 | 2018-08-17 | 武汉大学 | A kind of analog-circuit fault diagnosis method based on the extraction of depth confidence network characterization |
CN108875796A (en) * | 2018-05-28 | 2018-11-23 | 福州大学 | Diagnosing failure of photovoltaic array method based on linear discriminant analysis and support vector machines |
CN110108431B (en) * | 2019-05-22 | 2021-07-16 | 西安因联信息科技有限公司 | Mechanical equipment fault diagnosis method based on machine learning classification algorithm |
CN110334740A (en) * | 2019-06-05 | 2019-10-15 | 武汉大学 | The electrical equipment fault of artificial intelligence reasoning fusion detects localization method |
CN111327271B (en) * | 2020-01-20 | 2021-11-26 | 福州大学 | Photovoltaic array fault diagnosis method based on semi-supervised extreme learning machine |
CN111948487A (en) * | 2020-07-17 | 2020-11-17 | 国网上海市电力公司 | High-voltage power equipment fault diagnosis method and system based on artificial intelligence |
CN111967535B (en) * | 2020-09-04 | 2023-11-14 | 安徽大学 | Fault diagnosis method and device for temperature sensor of grain storage management scene |
CN112734055A (en) * | 2020-12-25 | 2021-04-30 | 华帝股份有限公司 | Fault prediction method of cooking equipment |
CN112785016B (en) * | 2021-02-20 | 2022-06-07 | 南京领行科技股份有限公司 | New energy automobile maintenance and fault monitoring and diagnosis method based on machine learning |
-
2021
- 2021-07-09 CN CN202110777869.7A patent/CN113392936B/en active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106897542A (en) * | 2017-04-18 | 2017-06-27 | 浙江中烟工业有限责任公司 | Based on the cigarette cut tobacco segment fault diagnostic method that notable failure variable is extracted |
CN108204892A (en) * | 2018-01-24 | 2018-06-26 | 重庆邮电大学 | Roller set equipment fault detection method based on array-type flexible pressure sensor |
CN110361193A (en) * | 2019-04-04 | 2019-10-22 | 浙江运达风电股份有限公司 | Method for distinguishing is known for wind generating set pitch control bearing fault |
Also Published As
Publication number | Publication date |
---|---|
CN113392936A (en) | 2021-09-14 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN113392936B (en) | Oven fault diagnosis method based on machine learning | |
CN109446187B (en) | Method for monitoring health state of complex equipment based on attention mechanism and neural network | |
CN110110768B (en) | Rolling bearing fault diagnosis method based on parallel feature learning and multiple classifiers | |
CN102340811B (en) | Method for carrying out fault diagnosis on wireless sensor networks | |
CN110334740A (en) | The electrical equipment fault of artificial intelligence reasoning fusion detects localization method | |
KR101249576B1 (en) | Rotating Machinery Fault Diagnostic Method and System Using Support Vector Machines | |
CN111737909A (en) | Structural health monitoring data anomaly identification method based on space-time graph convolutional network | |
CN111289250A (en) | Method for predicting residual service life of rolling bearing of servo motor | |
CN109543743B (en) | Multi-sensor fault diagnosis method for refrigerating unit based on reconstructed prediction residual error | |
CN110533167B (en) | Fault diagnosis method and system for electric valve actuating mechanism | |
CN105445646A (en) | Testing method of analog-digital circuit fault diagnosis based on neural network expert system | |
JP6831729B2 (en) | Anomaly detection device | |
CN111397902A (en) | Rolling bearing fault diagnosis method based on feature alignment convolutional neural network | |
CN115950609B (en) | Bridge deflection anomaly detection method combining correlation analysis and neural network | |
CN104615123B (en) | K-nearest neighbor based sensor fault isolation method | |
CN113051689A (en) | Bearing residual service life prediction method based on convolution gating circulation network | |
CN110243497A (en) | A kind of sensor fault diagnosis method and system based on principal component analysis | |
CN116295948A (en) | Abnormality detection method, system and storage medium of industrial temperature sensor in large temperature difference environment | |
CN107655690A (en) | Motor bearings method for diagnosing faults based on artificial neural network | |
CN115455746A (en) | Nuclear power device operation monitoring data anomaly detection and correction integrated method | |
CN114091600A (en) | Data-driven satellite associated fault propagation path identification method and system | |
US11308408B2 (en) | Fault signal recovery system and method | |
CN112213103A (en) | Fault diagnosis method, device, system and medium for rail transit rolling stock bearing | |
CN117170304B (en) | PLC remote monitoring control method and system based on industrial Internet of things | |
CN110017857A (en) | Nonlinear transducer method for diagnosing faults based on adaptive learning and neural network |
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 |