CN113392936A - Oven fault diagnosis method based on machine learning - Google Patents

Oven fault diagnosis method based on machine learning Download PDF

Info

Publication number
CN113392936A
CN113392936A CN202110777869.7A CN202110777869A CN113392936A CN 113392936 A CN113392936 A CN 113392936A CN 202110777869 A CN202110777869 A CN 202110777869A CN 113392936 A CN113392936 A CN 113392936A
Authority
CN
China
Prior art keywords
oven
temperature
fault
heating
characteristic 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
Application number
CN202110777869.7A
Other languages
Chinese (zh)
Other versions
CN113392936B (en
Inventor
李清华
张仁军
林涛
黄伟杰
艾克华
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Sichuan Yingchuangli Electronic Technology Co Ltd
Original Assignee
Sichuan Yingchuangli Electronic Technology Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Sichuan Yingchuangli Electronic Technology Co Ltd filed Critical Sichuan Yingchuangli Electronic Technology Co Ltd
Priority to CN202110777869.7A priority Critical patent/CN113392936B/en
Publication of CN113392936A publication Critical patent/CN113392936A/en
Application granted granted Critical
Publication of CN113392936B publication Critical patent/CN113392936B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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)
  • Electric Stoves And Ranges (AREA)
  • Testing And Monitoring For Control Systems (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 PA(ii) a S2, acquiring temperature information of heating areas of the fault oven in a plurality of heating states to form a characteristic data set PB(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 mature 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 invention can realize the purpose of realizing the temperature fluctuation of different heating points based on the high temperature condition of the oven, the temperature fluctuation condition within a certain time and the temperature fluctuation condition of different heating pointsThe method has higher diagnosis accuracy for the fault diagnosis of the oven.

Description

Oven fault diagnosis method based on machine learning
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 PA；
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 PB；
S3, characteristic data set P is subjected toAIs added with a label y of 0, and the feature data set P is addedBAdding a label to each feature data in1, when the label is y;
s4, adopting a machine learning method to construct a classifier, and according to the feature data set PAAnd a feature data set PBTraining 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 KA：
Figure BDA0003156445740000021
Wherein,
Figure BDA0003156445740000022
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;
s103, calculating a temperature information matrix KAAverage value of temperature in
Figure BDA0003156445740000023
Figure BDA0003156445740000024
Calculating the discrete degree p of the detection results of different temperature sensorsA：
Figure BDA0003156445740000025
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 values
Figure BDA0003156445740000026
And calculating T discrete degrees
Figure BDA0003156445740000027
Wherein
Figure BDA0003156445740000028
Represents the average temperature value obtained by the t repeated execution process,
Figure BDA0003156445740000029
represents the discrete degree obtained by the repeated execution process for the T time, wherein T is 1, 2.
S105, constructing characteristic data of the fault-free oven
Figure BDA00031564457400000210
S106, repeating the steps S101 to S105 for each fault-free oven to obtain corresponding characteristic data
Figure BDA00031564457400000211
Forming a feature data set PA：
Figure BDA00031564457400000212
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, utilizing temperature probeMeasuring array, collecting temperature of heating area of fault oven in heating state to obtain temperature information matrix KB：
Figure BDA0003156445740000031
Wherein,
Figure BDA0003156445740000032
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;
s203, calculating a temperature information matrix KBAverage value of temperature in
Figure BDA0003156445740000033
Figure BDA0003156445740000034
Calculating the discrete degree p of the detection results of different temperature sensorsB：
Figure BDA0003156445740000035
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 values
Figure BDA0003156445740000036
And calculating T discrete degrees
Figure BDA0003156445740000037
Wherein
Figure BDA0003156445740000038
Represents the average temperature value obtained by the t repeated execution process,
Figure BDA0003156445740000039
represents the discrete degree obtained by the repeated execution process for the T time, wherein T is 1, 2.
S205, constructing characteristic data of a fault oven
Figure BDA00031564457400000310
S206, for each fault oven, repeating the steps S201 to S205 to obtain corresponding characteristic data
Figure BDA00031564457400000311
Forming a feature data set PB：
Figure BDA00031564457400000312
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 using a temperature detection array, and an obtained temperature information matrix KC：
Figure BDA0003156445740000041
Wherein,
Figure BDA0003156445740000042
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;
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;
s504, calculating a temperature information matrix KCAverage value of temperature in
Figure BDA0003156445740000043
Figure BDA0003156445740000044
Calculating the discrete degree p of the detection results of different temperature sensorsC：
Figure BDA0003156445740000045
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 values
Figure BDA0003156445740000046
And calculating T discrete degrees
Figure BDA0003156445740000047
Wherein
Figure BDA0003156445740000048
Represents the average temperature value obtained by the t repeated execution process,
Figure BDA0003156445740000049
represents the discrete degree obtained by the repeated execution process for the T time, wherein T is 1, 2.
S205, establishing characteristic data of the oven to be tested
Figure BDA00031564457400000410
S206. calculation
Figure BDA00031564457400000411
Degree of dispersion Q:
Figure BDA00031564457400000412
wherein,
Figure BDA00031564457400000413
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 S207;
s207, feature data are combined
Figure BDA0003156445740000051
And 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 invention has the beneficial effects that: 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, acquiring temperature information of heating areas of a faultless oven in a plurality of heating states to obtain all faultless oven heating areasTemperature data of the heating area of the fault oven, and characteristic data of each fault-free oven are extracted according to the acquired information to form a characteristic data set PA；
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 PB；
S3, characteristic data set P is subjected toAIs added with a label y of 0, and the feature data set P is addedBThe 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 PAAnd a feature data set PBTraining 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 KA：
Figure BDA0003156445740000061
Wherein,
Figure BDA0003156445740000062
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;
s103, calculating a temperature information matrix KAAverage value of temperature in
Figure BDA0003156445740000063
Figure BDA0003156445740000064
Calculating the discrete degree p of the detection results of different temperature sensorsA：
Figure BDA0003156445740000065
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 values
Figure BDA0003156445740000066
And calculating T discrete degrees
Figure BDA0003156445740000067
Wherein
Figure BDA0003156445740000068
Represents the average temperature value obtained by the t repeated execution process,
Figure BDA0003156445740000069
represents the discrete degree obtained by the repeated execution process for the T time, wherein T is 1, 2.
S105, constructing characteristic data of the fault-free oven
Figure BDA00031564457400000610
S106, repeating the steps S101 to S105 for each fault-free oven to obtain corresponding characteristic data
Figure BDA00031564457400000611
Forming a feature data set PA：
Figure BDA00031564457400000612
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 KB：
Figure BDA0003156445740000071
Wherein,
Figure BDA0003156445740000072
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;
s203, calculating a temperature information matrix KBAverage value of temperature in
Figure BDA0003156445740000073
Figure BDA0003156445740000074
Calculating the discrete degree p of the detection results of different temperature sensorsB：
Figure BDA0003156445740000075
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 values
Figure BDA0003156445740000076
And calculating T discrete degrees
Figure BDA0003156445740000077
Wherein
Figure BDA0003156445740000078
Represents the average temperature value obtained by the t repeated execution process,
Figure BDA0003156445740000079
represents the discrete degree obtained by the repeated execution process for the T time, wherein T is 1, 2.
S205, constructing characteristic data of a fault oven
Figure BDA00031564457400000710
S206, for each fault oven, repeating the steps S201 to S205 to obtain corresponding characteristic data
Figure BDA00031564457400000711
Forming a feature data set PB：
Figure BDA00031564457400000712
Where N represents the total number of failed ovens.
In step S4, the feature data set P is extractedAAnd a feature data set PBThe 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 adoptedAAnd a feature data set PBTraining each feature data to obtain a mature classifier;
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 KC：
Figure BDA0003156445740000081
Wherein,
Figure BDA0003156445740000082
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;
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;
s504, calculating a temperature information matrix KCAverage value of temperature in
Figure BDA0003156445740000083
Figure BDA0003156445740000084
Calculating the discrete degree p of the detection results of different temperature sensorsC：
Figure BDA0003156445740000085
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 values
Figure BDA0003156445740000086
And calculating T discrete degrees
Figure BDA0003156445740000087
Wherein
Figure BDA0003156445740000088
Represents the average temperature value obtained by the t repeated execution process,
Figure BDA0003156445740000089
represents the discrete degree obtained by the repeated execution process for the T time, wherein T is 1, 2.
S205, establishing characteristic data of the oven to be tested
Figure BDA00031564457400000810
S206. calculation
Figure BDA00031564457400000811
Degree of dispersion Q:
Figure BDA00031564457400000812
wherein,
Figure BDA00031564457400000813
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 S207;
s207, feature data are combined
Figure BDA0003156445740000091
And 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 description of the embodiments is only intended to facilitate the understanding of the method of the invention and its core ideas; 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 scope; and the modifications or the 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 (4)

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 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 PA；
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 PB；
S3, characteristic data set P is subjected toAIs added with a label y of 0, and the feature data set P is addedBThe 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 PAAnd a feature data set PBTraining 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 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 KA：
Figure FDA0003156445730000011
Wherein,
Figure FDA0003156445730000012
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;
s103, calculating a temperature information matrix KAAverage value of temperature in
Figure FDA0003156445730000013
Figure FDA0003156445730000014
Calculating the discrete degree p of the detection results of different temperature sensorsA：
Figure FDA0003156445730000021
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 values
Figure FDA0003156445730000022
And calculating T discrete degrees
Figure FDA0003156445730000023
Wherein
Figure FDA0003156445730000024
Represents the average temperature value obtained by the t repeated execution process,
Figure FDA0003156445730000025
represents the discrete degree obtained by the repeated execution process for the T time, wherein T is 1, 2.
S105, constructing characteristic data of the fault-free oven
Figure FDA0003156445730000026
S106, repeating the steps S101 to S105 for each fault-free oven to obtain corresponding characteristic data
Figure FDA0003156445730000027
Forming a feature data set PA：
Figure FDA0003156445730000028
Where M represents the total number of non-faulty ovens.
3. 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, utilizing a temperature detection array to detect the fault in a heating stateTemperature acquisition is carried out in the heating area of the oven, and an obtained temperature information matrix KB：
Figure FDA0003156445730000029
Wherein,
Figure FDA00031564457300000210
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;
s203, calculating a temperature information matrix KBAverage value of temperature in
Figure FDA00031564457300000211
Figure FDA00031564457300000212
Calculating the discrete degree p of the detection results of different temperature sensorsB：
Figure FDA0003156445730000031
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 values
Figure FDA0003156445730000032
And calculating T discrete degrees
Figure FDA0003156445730000033
Wherein
Figure FDA0003156445730000034
Represents the average temperature value obtained by the t repeated execution process,
Figure FDA0003156445730000035
represents the discrete degree obtained by the repeated execution process for the T time, wherein T is 1, 2.
S205, constructing characteristic data of a fault oven
Figure FDA0003156445730000036
S206, for each fault oven, repeating the steps S201 to S205 to obtain corresponding characteristic data
Figure FDA0003156445730000037
Forming a feature data set PB：
Figure FDA0003156445730000038
Where N represents the total number of failed ovens.
4. 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 KC：
Figure FDA0003156445730000039
Wherein,
Figure FDA00031564457300000310
indicating the ith row and jth column of temperature sensorsThe acquired signals, i 1,2, and m, j 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;
s504, calculating a temperature information matrix KCAverage value of temperature in
Figure FDA00031564457300000311
Figure FDA0003156445730000041
Calculating the discrete degree p of the detection results of different temperature sensorsC：
Figure FDA0003156445730000042
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 values
Figure FDA0003156445730000043
And calculating T discrete degrees
Figure FDA0003156445730000044
Wherein
Figure FDA0003156445730000045
To representThe temperature average value obtained by the process of the t repeated execution,
Figure FDA0003156445730000046
represents the discrete degree obtained by the repeated execution process for the T time, wherein T is 1, 2.
S205, establishing characteristic data of the oven to be tested
Figure FDA0003156445730000047
S206. calculation
Figure FDA0003156445730000048
Degree of dispersion Q:
Figure FDA0003156445730000049
wherein,
Figure FDA00031564457300000410
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 S207;
s207, feature data are combined
Figure FDA00031564457300000411
And 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.
CN202110777869.7A 2021-07-09 2021-07-09 Oven fault diagnosis method based on machine learning Active CN113392936B (en)

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 true CN113392936A (en) 2021-09-14
CN113392936B 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)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116026889A (en) * 2022-12-30 2023-04-28 苏州春田机械有限公司 CCD detection device, system and control method for production line
CN117740083A (en) * 2024-02-19 2024-03-22 达斯玛环境科技（北京）有限公司 Method, system, equipment and storage medium for monitoring faults of stirrer
CN118051744A (en) * 2024-04-16 2024-05-17 天津君磊科技有限公司 Waterproof signal connector fault diagnosis method based on machine learning algorithm

Citations (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120304008A1 (en) * 2009-09-17 2012-11-29 Holger Hackstein Supervised fault learning using rule-generated samples for machine condition monitoring
CN106483449A (en) * 2016-09-09 2017-03-08 电子科技大学 Based on deep learning and the analog-circuit fault diagnosis method of Complex eigenvalues
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
US20180083833A1 (en) * 2016-09-16 2018-03-22 Oracle International Corporation Method and system for performing context-aware prognoses for health analysis of monitored systems
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
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
KR101967065B1 (en) * 2017-10-18 2019-04-08 울산대학교 산학협력단 Fault diagnosis apparatus and method for robust to environmental change
US20190243735A1 (en) * 2018-02-05 2019-08-08 Wuhan University Deep belief network feature extraction-based analogue circuit fault diagnosis method
CN110108431A (en) * 2019-05-22 2019-08-09 西安因联信息科技有限公司 A kind of Trouble Diagnostic Method of Machinery Equipment 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
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
US20190333291A1 (en) * 2017-01-13 2019-10-31 Huawei Technologies Co., Ltd. Cloud-Based Vehicle Fault Diagnosis Method, Apparatus, and System
CN111327271A (en) * 2020-01-20 2020-06-23 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
CN111967535A (en) * 2020-09-04 2020-11-20 安徽大学 Fault diagnosis method and device for temperature sensor in 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
CN112785016A (en) * 2021-02-20 2021-05-11 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

Patent Citations (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120304008A1 (en) * 2009-09-17 2012-11-29 Holger Hackstein Supervised fault learning using rule-generated samples for machine condition monitoring
CN106483449A (en) * 2016-09-09 2017-03-08 电子科技大学 Based on deep learning and the analog-circuit fault diagnosis method of Complex eigenvalues
US20180083833A1 (en) * 2016-09-16 2018-03-22 Oracle International Corporation Method and system for performing context-aware prognoses for health analysis of monitored systems
US20190333291A1 (en) * 2017-01-13 2019-10-31 Huawei Technologies Co., Ltd. Cloud-Based Vehicle Fault Diagnosis Method, Apparatus, and System
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
KR101967065B1 (en) * 2017-10-18 2019-04-08 울산대학교 산학협력단 Fault diagnosis apparatus and method for robust to environmental change
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
US20190243735A1 (en) * 2018-02-05 2019-08-08 Wuhan University Deep belief network feature extraction-based analogue circuit fault diagnosis method
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
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
CN110108431A (en) * 2019-05-22 2019-08-09 西安因联信息科技有限公司 A kind of Trouble Diagnostic Method of Machinery Equipment 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
CN111327271A (en) * 2020-01-20 2020-06-23 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
CN111967535A (en) * 2020-09-04 2020-11-20 安徽大学 Fault diagnosis method and device for temperature sensor in 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
CN112785016A (en) * 2021-02-20 2021-05-11 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

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
M. PRIST等: "Machine Learning-as-a-Service for Consumer Electronics Fault Diagnosis: a Comparison between Matlab and Azure ML", 《2020 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)》 *
许杰雄等: "一种结合模糊聚类和协同训练的轴承故障识别方法", 《研究与设计 微型电脑应用》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116026889A (en) * 2022-12-30 2023-04-28 苏州春田机械有限公司 CCD detection device, system and control method for production line
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
CN117740083A (en) * 2024-02-19 2024-03-22 达斯玛环境科技（北京）有限公司 Method, system, equipment and storage medium for monitoring faults of stirrer
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
CN118051744A (en) * 2024-04-16 2024-05-17 天津君磊科技有限公司 Waterproof signal connector fault diagnosis method based on machine learning algorithm

Also Published As

Publication number Publication date
CN113392936B (en) 2022-09-02

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
CN111737909B (en) Structural health monitoring data anomaly identification method based on space-time graph convolutional network
CN102340811B (en) Method for carrying out fault diagnosis on wireless sensor networks
KR101249576B1 (en) Rotating Machinery Fault Diagnostic Method and System Using Support Vector Machines
CN110334740A (en) The electrical equipment fault of artificial intelligence reasoning fusion detects localization method
CN111289250A (en) Method for predicting residual service life of rolling bearing of servo motor
CN105445646A (en) Testing method of analog-digital circuit fault diagnosis based on neural network expert system
CN111397902A (en) Rolling bearing fault diagnosis method based on feature alignment convolutional neural network
CN111579121A (en) Method for diagnosing temperature fault in new energy automobile battery pack on line based on big data
CN115950609B (en) Bridge deflection anomaly detection method combining correlation analysis and neural 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
CN115455746B (en) Nuclear power device operation monitoring data anomaly detection and correction integrated method
CN109826818A (en) A kind of method for diagnosing faults of mine ventilator
CN111079348B (en) Method and device for detecting slowly-varying signal
CN114814593B (en) Multi-fault rapid diagnosis method for battery pack with two-step detection strategy
CN115267589A (en) Multi-parameter joint diagnosis method for battery faults of electric vehicle
US11308408B2 (en) Fault signal recovery system and method
CN117170304B (en) PLC remote monitoring control method and system based on industrial Internet of things
CN114091600A (en) Data-driven satellite associated fault propagation path identification method and system
CN106195247B (en) A kind of control system of speed variator based on big data management mode
CN108805289A (en) A kind of Method of Motor Fault Diagnosis based on basal ganglion
CN116502526A (en) Improved PSO-GRNN neural network-based weighing sensor fault diagnosis method
CN111598161A (en) Engine gas circuit state diagnosis system based on CNN transfer learning

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