CN106682685B - Local temperature change abnormity detection method based on microwave heating temperature field distribution characteristic deep learning - Google Patents

Local temperature change abnormity detection method based on microwave heating temperature field distribution characteristic deep learning Download PDF

Info

Publication number
CN106682685B
CN106682685B CN201611106593.5A CN201611106593A CN106682685B CN 106682685 B CN106682685 B CN 106682685B CN 201611106593 A CN201611106593 A CN 201611106593A CN 106682685 B CN106682685 B CN 106682685B
Authority
CN
China
Prior art keywords
data
microwave heating
layer
temperature
parameters
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
Application number
CN201611106593.5A
Other languages
Chinese (zh)
Other versions
CN106682685A (en
Inventor
王楷
熊庆宇
马龙昆
孙国坦
赵友金
余星
姚政
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.)
Chongqing University
Original Assignee
Chongqing University
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 Chongqing University filed Critical Chongqing University
Priority to CN201611106593.5A priority Critical patent/CN106682685B/en
Publication of CN106682685A publication Critical patent/CN106682685A/en
Application granted granted Critical
Publication of CN106682685B publication Critical patent/CN106682685B/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
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Software Systems (AREA)
  • Molecular Biology (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Mathematical Physics (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Testing And Monitoring For Control Systems (AREA)
  • Control Of High-Frequency Heating Circuits (AREA)

Abstract

The invention discloses a local temperature change anomaly detection method based on deep learning of microwave heating temperature field distribution characteristics, which is characterized in that multidimensional big data distributed in the temperature field in the microwave heating process are learned by adopting a method of combining a convolutional neural network and automatic coding, the deep logical relationship among the data is found out, the characteristics with more expressive ability than input are obtained by learning the structure of the data, and then anomaly detection is carried out by adopting an Isolation Forest algorithm. The invention can reliably detect the phenomenon that the dielectric coefficient and the thermal conductivity of the heated medium change along with the rise of the temperature due to the coupling of the complex time-varying electromagnetic field and the temperature field in the microwave heating process, so that the medium is locally overheated and even thermally out of control. And then timely processing is carried out, and safety accidents are avoided.

Description

Local temperature change abnormity detection method based on microwave heating temperature field distribution characteristic deep learning
Technical Field
The invention relates to a microwave heating control technology.
Background
Microwave heating essentially utilizes the energy characteristics of microwaves. Microwave energy directly penetrates through the medium, and molecules in the medium absorb the microwave energy to make the molecular thermal motion aggravated, so that the temperature rises, and the heating purpose is achieved. Compared with other traditional heating modes, the heating device has the advantages of high efficiency, no pollution, high heating speed, small heat loss and the like. Microwave heating is a new cleaning heating mode and has no doubt great application value.
In the microwave heating process, however, a coupling condition of a complex time-varying electromagnetic field and a temperature field is involved, the dielectric coefficient and the thermal conductivity of the heated medium change with the rise of temperature, and the change of the variable factors can cause the phenomenon of local overheating and even thermal runaway of the medium. This situation can lead to safety accidents if not handled in a timely manner.
Disclosure of Invention
The invention aims to solve the problem of local overheating (thermal runaway) in the microwave heating process.
The technical scheme adopted for achieving the purpose of the invention is that the local temperature change abnormity detection method based on microwave heating temperature field distribution characteristic deep learning is characterized by comprising the following steps:
obtaining a temperature anomaly detection model of the microwave heating device through the steps 1-4:
1) arranging m acquisition nodes in a microwave heating temperature field of a microwave heating device, and acquiring temperature data of each acquisition node at n0 moments to form a multi-dimensional data sample set Cq；
2) The multidimensional data sample set C obtained in the step 1 is processedqCarrying out normalization processing to obtain data
Figure GDA0002254009750000011
3) Depth feature extraction:
3-1) selecting a training sample S in the multi-dimensional data of the normalized microwave distribution fieldqAs an input variable, a convolution kernel size L is setkAnd step length AkObtaining a data set H formed by the characteristic mapping valuesk。
3-2) setting the size of the pooling layer, and processing the data set H by adopting a maximum pooling methodkObtaining a data set Ik，
3-3) data set IkThe automatic encoder is brought in, parameters of each layer of the network are reversely corrected by utilizing a back propagation algorithm through the minimum reconstruction error, and finally the characteristics are learned
Figure GDA0002254009750000012
Repeating the step 3), continuously learning new sample data, and finally updating to obtain a characteristic data set through layer-by-layer iteration of the network
Figure GDA0002254009750000021
4) With data sets
Figure GDA0002254009750000022
As input to the anomaly detection algorithm Isolation Forest,
obtaining a temperature anomaly detection model:
Figure GDA0002254009750000023
x is an input matrix of the model;
during operation, whether the temperature of the microwave heating device is abnormal is obtained through the following steps:
A) recording the temperature data of each acquisition node in m acquisition nodes of the microwave heating device at n0 moments by adopting the same method as the step 1), and storing the temperature data into a matrix E;
B) performing normalization processing on the matrix E by adopting the same method as the step 2) to obtain a matrix F:
C) selecting the matrix F as an input variable, and setting the length L of the convolutional layer sliding windowKAnd step length AKSequentially extracting parameters of each line from input variables, traversing the parameters of each line by using a sliding window, and cutting the parameters into a plurality of data fragments
Figure GDA0002254009750000024
Wherein u is
Figure GDA0002254009750000025
Rounding up to the obtained value, K is 1, 2 … … K, K is the number of convolutional network layers.
D) By data fragments
Figure GDA0002254009750000026
And a linear filter
Figure GDA0002254009750000027
Performing convolution, adding an offset pbkAs input of an activation function, wherein the activation function adopts sigmoid, tanh, or relu, and a data set PH formed by feature mapping values obtained by the activation functionk：
E) Setting a pooling window with a GK size and a maximum pooling data set PHkObtaining a data set PIk：
F) To the data set PIkCarrying in an automatic encoder, reversely correcting parameters of each layer of the network layer by using a back propagation algorithm through minimum reconstruction errors, and training to obtain characteristics
Figure GDA0002254009750000028
Learning new data training samples by adopting the same method as the step 3), updating the input variables in the step 3-1), obtaining new samples for training each layer parameter of the network according to the previous training samples, updating k by k +1, and coiling the length L of the sliding window of the layerk+1And step length Ak+1The length of the pooling window is unchanged. Repeating the process of 3), and finally learning the characteristics
Figure GDA0002254009750000029
LKAnd AKRespectively setting the length and the step length of the sliding window of the convolutional layer when the steps D) to E) are carried out for the Kth time; gKSetting the size of the pooling window for the Kth time of performing the steps D) to E);
G) feature matrix
Figure GDA00022540097500000210
As the temperature abnormality detection model obtained in step 4):
Figure GDA0002254009750000031
the output is abnormal or normal.
It is worth to be noted that the invention adopts a multilayer convolution network structure, extracts deep-level feature expressions from an original multi-dimensional data signal, and then transmits the deep-level feature expressions to an Isolation Forest anomaly detection model to detect anomaly features. The method is high-efficiency, can extract better feature expression from the original data acquired in the microwave heating process, brings the extracted features into an anomaly detection system for detection, and can reliably detect whether the temperature change of the heated medium distribution field is abnormal or not.
Drawings
FIG. 1. Process flow of the invention
FIG. 2 is a schematic diagram of convolutional neural network feature extraction
FIG. 3 is a diagram of an automatic coding feature map
FIG. 4 shows the detection of anomalies (red dots for anomalies) in 700 power microwave data using the method of the present invention
FIG. 5 illustrates the detection of anomalies (red dots for anomalies) in 800 power microwave data using the method of the present invention.
Detailed Description
The present invention is further illustrated by the following examples, but it should not be construed that the scope of the above-described subject matter is limited to the following examples. Various substitutions and alterations can be made without departing from the technical idea of the invention and the scope of the invention is covered by the present invention according to the common technical knowledge and the conventional means in the field.
An anomaly detection method of multi-dimensional big data information of temperature field distribution in a microwave heating process based on deep learning,
building a background analysis system and a detection device;
the background analysis system heats the data sample C in the original microwave heating processaDuring analysis, obtaining a temperature anomaly detection model of the microwave heating device through the steps 1-4:
the method comprises the following steps:
1) obtaining a raw data sample CqQ is the data sample number, q is 1, 2 … …,
wherein:
Figure GDA0002254009750000041
Figure GDA0002254009750000042
microwave data for a node, sample CqThe method comprises the steps that m different nodes are provided, data of each node are collected at time t, and t is 1, 2 … … n 0;
2) normalizing the original data sample Cq to obtain
Figure GDA0002254009750000043
Construct data set { S1, S2 … … }
3) Depth feature extraction:
3-1) selecting data samples S in the data setqAs an input variable, the size L of the convolution kernel is setkAnd step length AkSequentially extracting parameters of each line from input variables, traversing the parameters of each line by using a sliding window, and cutting the parameters into a plurality of data fragments
Figure GDA0002254009750000044
Wherein i is
Figure GDA0002254009750000045
Rounding up to obtain a value, wherein K is 1 and 2 … … K, and K is the number of layers of the convolutional network;
3-2) with data fragments
Figure GDA0002254009750000046
And a linear filter
Figure GDA0002254009750000047
Performing convolution and adding an offset bkAs the input variable of the activation function, obtaining a data set H formed by feature mapping values through the activation functionkThe activation function can adopt sigmoid, tanh, relu and other functions.
Figure GDA0002254009750000048
Figure GDA0002254009750000051
3-3) setting a pooling window with the size of GK, and processing the data set H by adopting a maximum pooling methodkObtaining a data set Ik：
Figure GDA0002254009750000052
Figure GDA0002254009750000053
3-4) obtaining a characteristic data set IkSubstituting into an automatic encoder, correcting parameters of each layer of the network layer by layer in a reverse direction by using a back propagation algorithm through minimum reconstruction errors, and training to obtain characteristics
Figure GDA0002254009750000054
Learning a new data training sample by adopting the same method as the step 3), updating the input variable in the step 3-1) by using the new sample, training the new sample according to the network layer parameters obtained by the previous training sample, updating k by using k +1, keeping the length Lk +1 and the step length Ak +1 of the convolutional layer sliding window unchanged, and keeping the length of the pooling window unchanged. Repeating the process of 3), and finally learning the characteristics
Figure GDA0002254009750000055
Figure GDA0002254009750000056
Figure GDA0002254009750000057
LKAnd AKRespectively setting the size and the step length of a convolution kernel when the Kth time is subjected to the steps 3-2) to 3-3); gKSetting the size of a pooling window when the step 3-2) to the step 3-3) is carried out for the Kth time;
4) with data sets
Figure GDA0002254009750000061
Obtaining an abnormal score S (X) for inputting Isolation Forest, and obtaining a temperature abnormal detection model:
Figure GDA0002254009750000062
the detection device comprises a data collection part and a data analysis part; during working, whether the temperature of the microwave heating device is abnormal is obtained through the steps A to G:
A) the data collection part collects data of the heating nodes, stores the data into a matrix E and transmits the data to the data analysis part;
wherein:
Figure GDA0002254009750000063
Figure GDA0002254009750000064
the data of one node is obtained, in a sample E, m different nodes are provided, the parameter of each node is collected at pt time, and pt is 1 and 2 … … pn 0;
B) normalizing the data sample E by the same method as the step 2) to obtain the data sample E
Figure GDA0002254009750000065
C) Selecting the matrix F as an input variable, and setting the length L of the convolutional layer sliding windowKAnd step length AKSequentially extracting parameters of each line from input variables, traversing the parameters of each line by using a sliding window, and cutting the parameters into a plurality of data fragments
Figure GDA0002254009750000071
Wherein u is
Figure GDA0002254009750000072
Rounding up to the obtained value, K is 1, 2 … … K, K is the number of convolutional network layers.
D) By data fragments
Figure GDA0002254009750000073
And a linear filter
Figure GDA0002254009750000074
Performing convolution, adding an offset pbkAs the input of the activation function, wherein the activation function may adopt sigmoid, tanh, relu, etc., and the data set PH formed by the feature mapping values obtained by the activation functionk：
Figure GDA0002254009750000075
Figure GDA0002254009750000076
E) Setting a pooling window with a GK size and a maximum pooling data set PHkObtaining a data set PIk：
Figure GDA0002254009750000077
Figure GDA0002254009750000078
F) To the data set PIkCarrying in an automatic encoder, reversely correcting parameters of each layer of the network layer by using a back propagation algorithm through minimum reconstruction errors, and training to obtain characteristics
Figure GDA0002254009750000079
Learning new data training samples by adopting the same method as the step 3), updating the input variables in the step 3-1), training new samples according to network layer parameters obtained by the previous training sample, and updating by using k +1k, the length Lk +1 and the step Ak +1 of the convolutional layer sliding window are unchanged, and the length of the pooling window is also unchanged. Repeating the process of 3), and finally learning the characteristics
Figure GDA0002254009750000081
Figure GDA0002254009750000082
Figure GDA0002254009750000083
LKAnd AKRespectively setting the length and the step length of the sliding window of the convolutional layer when the steps D) to E) are carried out for the Kth time; gKSetting the size of the pooling window for the Kth time of performing the steps D) to E);
G) feature matrix
Figure GDA0002254009750000084
As the temperature abnormality detection model:
Figure GDA0002254009750000085
the output is abnormal or normal.

Claims (1)

1. A local temperature change abnormity detection method based on microwave heating temperature field distribution characteristic deep learning is characterized in that:
obtaining a temperature abnormity detection model of the microwave heating device through steps 1) to 4):
1) in the microwave heating temperature field of the microwave heating device, m collection nodes are arranged, and n is0At each moment, acquiring the temperature data of each acquisition node to form a multi-dimensional data sample set Cq；
2) A multi-dimensional data sample set C obtained in the step 1) is usedqCarrying out normalization processing to obtain data
Figure FDA0002254009740000011
3) Depth feature extraction:
3-1) selecting a training sample S in the multi-dimensional data of the normalized microwave distribution fieldqAs an input variable, a convolution kernel size L is setKAnd step length AkSequentially extracting parameters of each line from input variables, traversing the parameters of each line by using a sliding window, and cutting the parameters into a plurality of data fragments
Figure FDA0002254009740000012
Wherein i is
Figure FDA0002254009740000013
Rounding up to obtain a value, wherein K is 1 and 2.. K, and K is the number of layers of the convolutional network; by data fragments
Figure FDA0002254009740000014
And a linear filter Wi kPerforming convolution and adding an offset bkAs input of the activation function, a data set H formed by feature mapping values is obtained through the activation functionk；
3-2) setting the size of the pooling layer, and processing the data set H by adopting a maximum pooling methodKObtaining a data set Ik，
3-3) data set IkThe automatic encoder is brought in, parameters of each layer of the network are reversely corrected by utilizing a back propagation algorithm through the minimum reconstruction error, and finally the characteristics are learned
Figure FDA0002254009740000015
Repeating the step 3), continuously learning new sample data, and finally updating to obtain characteristics through layer-by-layer iteration of the network
Figure FDA0002254009740000016
4) Is characterized by
Figure FDA0002254009740000017
As input to the anomaly detection algorithm Isolation Forest,
obtaining a temperature anomaly detection model:
Figure FDA0002254009740000018
x is an input matrix of the model;
during working, whether the temperature of the microwave heating device is abnormal is obtained through the steps A to G:
A) recording m collection nodes of the microwave heating device by the same method as the step 1), wherein each collection node is positioned at n0Temperature data of each moment are stored in a matrix E;
B) normalizing the matrix E by adopting the same method as the step 2) to obtain a matrix F;
C) selecting the matrix F as an input variable, and setting the length L of the convolutional layer sliding windowKAnd step length AkSequentially extracting parameters of each line from input variables, traversing the parameters of each line by using a sliding window, and cutting the parameters into a plurality of data fragments
Figure FDA0002254009740000021
Wherein u is
Figure FDA0002254009740000022
Rounding up to obtain a value, wherein K is 1 and 2.. K, and K is the number of layers of the convolutional network; pn-N complex0The total number of the acquisition time points of the matrix E is;
D) by data fragments
Figure FDA0002254009740000023
And a linear filter
Figure FDA0002254009740000024
Performing convolution, adding an offset pbkAs input of an activation function, wherein the number of activations is sigmoid, tanh, or relu, and a data set PH formed by feature mapping values obtained by the activation functionk：
E) Is provided withIs sized as GKPooling window, max pooling data set PHkObtaining a data set PIk：
F) To the data set PIkCarrying in an automatic encoder, reversely correcting parameters of each layer of the network layer by using a back propagation algorithm through minimum reconstruction errors, and training to obtain characteristics
Figure FDA0002254009740000025
Learning new data training samples by adopting the same method as the step 3), updating the input variables in the step 3-1), obtaining new samples for training each layer parameter of the network according to the previous training samples, updating k by k +1, and coiling the length L of the sliding window of the layerk+1And step length Ak+1The length of the pooling window is not changed; repeating the process of 3), and finally learning the characteristics
Figure FDA0002254009740000026
LKAnd AKRespectively setting the length and the step length of the sliding window of the convolutional layer when the steps D) to E) are carried out for the Kth time; gKSetting the size of the pooling window for the Kth time of performing the steps D) to E);
G) will be characterized by
Figure FDA0002254009740000027
As the temperature abnormality detection model obtained in step 4):
Figure FDA0002254009740000028
CN201611106593.5A 2016-12-06 2016-12-06 Local temperature change abnormity detection method based on microwave heating temperature field distribution characteristic deep learning Active CN106682685B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201611106593.5A CN106682685B (en) 2016-12-06 2016-12-06 Local temperature change abnormity detection method based on microwave heating temperature field distribution characteristic deep learning

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201611106593.5A CN106682685B (en) 2016-12-06 2016-12-06 Local temperature change abnormity detection method based on microwave heating temperature field distribution characteristic deep learning

Publications (2)

Publication Number Publication Date
CN106682685A CN106682685A (en) 2017-05-17
CN106682685B true CN106682685B (en) 2020-05-01

Family

ID=58866338

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201611106593.5A Active CN106682685B (en) 2016-12-06 2016-12-06 Local temperature change abnormity detection method based on microwave heating temperature field distribution characteristic deep learning

Country Status (1)

Country Link
CN (1) CN106682685B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108777873A (en) * 2018-06-04 2018-11-09 江南大学 The wireless sensor network abnormal deviation data examination method of forest is isolated based on weighted blend

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109521725A (en) * 2017-09-20 2019-03-26 西门子公司 The method, apparatus and equipment and machine readable media of detection abnormal data
CN108182450B (en) * 2017-12-25 2021-03-30 电子科技大学 Airborne ground penetrating radar target identification method based on deep convolutional network
CN108921440B (en) * 2018-07-11 2022-08-05 平安科技（深圳）有限公司 Pollutant abnormity monitoring method, system, computer equipment and storage medium
CN109655483B (en) * 2018-12-14 2021-06-15 四川大学 Material microstructure defect detection method based on deep learning algorithm
CN110766056B (en) * 2019-09-27 2022-05-06 中山大学 Abnormal image detection method integrating image generation and multi-label classification
CN111428886B (en) * 2020-04-10 2023-08-04 青岛聚好联科技有限公司 Method and device for adaptively updating deep learning model of fault diagnosis
CN113177290B (en) * 2021-03-25 2023-09-26 中国人民解放军军事科学院国防科技创新研究院 Satellite component temperature field prediction method based on depth agent model normalization
CN113253125B (en) * 2021-05-19 2023-02-17 北方工业大学 Information fusion-based lithium iron phosphate battery thermal runaway monitoring method and system
CN114547970B (en) * 2022-01-25 2024-02-20 中国长江三峡集团有限公司 Intelligent diagnosis method for abnormality of top cover drainage system of hydropower plant

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104755892A (en) * 2012-10-26 2015-07-01 富士通株式会社 Temperature measuring system and abnormality detecting method
CN105164508A (en) * 2013-02-18 2015-12-16 赛拉诺斯股份有限公司 Systems and methods for collecting and transmitting assay results
CN105447512A (en) * 2015-11-13 2016-03-30 中国科学院自动化研究所 Coarse-fine optical surface defect detection method and coarse-fine optical surface defect detection device
CN105678267A (en) * 2016-01-08 2016-06-15 浙江宇视科技有限公司 Scene recognition method and device

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070010906A1 (en) * 2005-07-11 2007-01-11 Tokyo Electron Limited Apparatus and system for monitoring a substrate processing, program for monitoring the processing and storage medium storing same

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104755892A (en) * 2012-10-26 2015-07-01 富士通株式会社 Temperature measuring system and abnormality detecting method
CN105164508A (en) * 2013-02-18 2015-12-16 赛拉诺斯股份有限公司 Systems and methods for collecting and transmitting assay results
CN105447512A (en) * 2015-11-13 2016-03-30 中国科学院自动化研究所 Coarse-fine optical surface defect detection method and coarse-fine optical surface defect detection device
CN105678267A (en) * 2016-01-08 2016-06-15 浙江宇视科技有限公司 Scene recognition method and device

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Monitoring and Extracting Abnormalities in Land Surface Temperature Images for Automatic Identification of Forest Fires;Narasimha Prasad.etc;《2013 European Modelling Symposium》;20140331;第215-219页 *
基于图像分析的微波加热温度场监测系统研究与实现;崔丽艳;《中国优秀硕士学位论文全文数据库》;20160615;I138-1404 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108777873A (en) * 2018-06-04 2018-11-09 江南大学 The wireless sensor network abnormal deviation data examination method of forest is isolated based on weighted blend

Also Published As

Publication number Publication date
CN106682685A (en) 2017-05-17

Similar Documents

Publication Publication Date Title
CN106682685B (en) Local temperature change abnormity detection method based on microwave heating temperature field distribution characteristic deep learning
CN106682814B (en) Wind turbine generator fault intelligent diagnosis method based on fault knowledge base
CN106600140B (en) Gas pipeline fault prediction early warning system and method based on improved support vector machine
Yin et al. Wasserstein Generative Adversarial Network and Convolutional Neural Network (WG‐CNN) for Bearing Fault Diagnosis
CN108398268B (en) Bearing performance degradation evaluation method
CN104318305B (en) Inverter low-frequency noise fault diagnosis method based on wavelets and neural network
CN109460621A (en) Based on the gas turbine blades fault detection method for improving neural network
CN113132399A (en) Industrial control system intrusion detection method based on time convolution network and transfer learning
CN114363195B (en) Network flow prediction and early warning method for time and frequency spectrum residual convolution network
CN115037543B (en) Abnormal network flow detection method based on bidirectional time convolution neural network
CN111259953B (en) Equipment defect time prediction method based on capacitive equipment defect data
CN109597315A (en) A kind of mechanical equipment health degenerate state discrimination method, equipment and system
CN112231975A (en) Data modeling method and system based on reliability analysis of railway power supply equipment
CN116929815A (en) Equipment working state monitoring system and method based on Internet of things
CN111259949A (en) Fault identification model construction method, model and identification method for aircraft environmental control system
CN104677629A (en) Fault detection method for vehicle transmission
KR102471427B1 (en) Predictive diagnosis system for mechanical and electrical failures through motor noise and thermal distribution analysis
Huang et al. Automatic classification of magnetic tiles internal defects based on acoustic resonance analysis
CN116738367A (en) Rolling bearing fault diagnosis method based on multi-scale map transducer
CN115146675B (en) Rotary machine migration diagnosis method under variable working condition of depth multi-feature dynamic countermeasure
CN103399490B (en) A kind of carbon fibre precursor wet method coagulation bath temperature control technique that study is controlled based on immunological memory
CN105843891A (en) Incremental online characteristic extraction and analysis method and system
CN108459585A (en) Power station fan method for diagnosing faults based on sparse locally embedding depth convolutional network
Ali et al. Analysis on propeller design for medium-sized drone (DJI phantom 3)
Li et al. Tool wear monitoring using machine 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