CN111259993A - Fault diagnosis method and device based on neural network - Google Patents
Fault diagnosis method and device based on neural network Download PDFInfo
- Publication number
- CN111259993A CN111259993A CN202010148263.2A CN202010148263A CN111259993A CN 111259993 A CN111259993 A CN 111259993A CN 202010148263 A CN202010148263 A CN 202010148263A CN 111259993 A CN111259993 A CN 111259993A
- Authority
- CN
- China
- Prior art keywords
- neural network
- characteristic data
- fault
- fault diagnosis
- network model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003745 diagnosis Methods 0.000 title claims abstract description 52
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 22
- 238000003062 neural network model Methods 0.000 claims abstract description 40
- 238000012549 training Methods 0.000 claims abstract description 30
- 238000007781 pre-processing Methods 0.000 claims abstract description 18
- 238000012544 monitoring process Methods 0.000 claims abstract description 5
- 210000002569 neuron Anatomy 0.000 claims description 18
- 238000012545 processing Methods 0.000 claims description 8
- 238000001514 detection method Methods 0.000 claims description 5
- 230000001133 acceleration Effects 0.000 claims description 3
- 230000008676 import Effects 0.000 claims description 2
- 230000008569 process Effects 0.000 abstract description 8
- 230000009471 action Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000002485 combustion reaction Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000006467 substitution reaction 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
- G06F18/21—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
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M15/00—Testing of engines
-
- 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
Abstract
The invention discloses a fault diagnosis method and a fault diagnosis device based on a neural network, and relates to the technical field of engine fault diagnosis, wherein the fault diagnosis method comprises the steps of S1, extracting characteristic data information of fault equipment; s2, preprocessing the characteristic data information; s3, importing the characteristic information into a neural network model; s4, importing a training sample set; s5, obtaining a fault diagnosis result and S6 by the neural network model, and deriving a diagnosis result of the equipment fault, wherein through the fault diagnosis steps and by means of the BP neural network model, in the actual diagnosis process, by extracting each characteristic data information in the existing engine, the characteristic data information is directly imported into the BP neural network model trained by the training sample set, so that the specific fault result of the engine can be rapidly diagnosed; the fault diagnosis device based on the neural network comprises a characteristic data monitoring unit, a data preprocessing module, a BP neural network model, a training sample set importing module and a fault diagnosis result outputting module.
Description
Technical Field
The invention relates to the technical field of engine fault diagnosis, in particular to a fault diagnosis method and device based on a neural network.
Background
An Engine (Engine), a machine capable of converting other forms of energy into mechanical energy, originally produced in the uk, is suitable both for power generation and for the whole machine comprising a power plant (e.g. gasoline engines, aeronautical engines). The types thereof include, for example, internal combustion engines (gasoline engines, etc.), external combustion engines (stirling engines, steam engines, etc.), electric motors, and the like.
With the increasing service life of the engine, the problem of failure often occurs in the normal use process, in the prior art, the process of finding out specific failure points of the engine is long, the engine needs to be disassembled one by one, time and labor are wasted, the overhauling efficiency is low, the labor cost is high, and the development requirements of the society are not facilitated.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a fault diagnosis method and device based on a neural network, and solves the problems that in the prior art, the process of finding out specific fault points of an engine is long, the engine needs to be disassembled one by one, time and labor are wasted, the overhauling efficiency is low, the labor cost is high, and the development requirements of the society are not facilitated.
In order to achieve the purpose, the invention is realized by the following technical scheme: a fault diagnosis method based on a neural network comprises the following steps:
s1, extracting the characteristic data information of the fault equipment: extracting characteristic data information corresponding to the fault engine from the existing fault engine;
s2, preprocessing the characteristic data information: importing each feature data extracted from the existing fault engine in the step S1 into a data processing module for preprocessing;
s3, importing the characteristic information into a neural network model: importing each feature data preprocessed in the step S2 into the constructed BP neural network model;
s4, importing a training sample set: introducing a training sample set formed by existing characteristic data and known fault result data into the PB neural network model constructed in the step S3, and training the constructed BP neural network model;
s5, obtaining a fault diagnosis result by the neural network model: the method comprises the following substeps:
a. importing each characteristic data information into the whole BP neural network model in the step S2, inputting the characteristic data information into each node in the input layer, and outputting the characteristic data information serving as the output of the neuron in the input layer;
b. calculating the output of each node of the neuron in the hidden layer according to a formula to be used as the input of an output layer;
c. calculating the output of each neuron node of the output layer according to a formula;
d. and c, judging the result output by the neuron node corresponding to the output layer in the step c according to a threshold function.
S6, deriving the diagnosis result of the equipment fault: the diagnosis result of the device failure output in step S5 is derived.
Preferably, the characteristic data information in step S1 includes a temperature signal, an oil pressure signal, a current intensity signal, a voltage intensity signal, and a vibration signal of the faulty engine.
Preferably, in the sub-step b in the step S5, the formula for calculating the output value of each node of the neuron in the hidden layer is:
Oj＝f(Ij)＝1/[exp(-Ij)]
in the formula: omegajiIs the weight between the hidden layer node j and the input layer node i, theta j is the bias of the hidden layer node j, f (x) is the function of sigmoid, and the expressionIs f (x) 1/[1+ exp (-x)]。
Preferably, in the sub-step c of step S5, the formula for calculating the output value of each neuron node in the output layer is:
yk＝f(Ik)＝1/[exp(-Ik)]
in the formula: omegakjAnd theta k is the offset of the hidden layer node k.
Preferably, in the step S4, the training sample set is imported, and the training sample is (x)p1，xp2，xp3......xpn) The number of samples is (P ═ 1,2,3 … … P).
The invention also discloses a fault diagnosis device based on the neural network, which comprises a characteristic data monitoring unit, a data preprocessing module, a BP neural network model, a training sample set importing module and a fault diagnosis result outputting module, wherein the characteristic data detecting unit is used for collecting parameter information in the existing fault engine, and the data preprocessing module is used for processing the characteristic information extracted by the characteristic data detecting unit.
Preferably, the BP neural network model is configured to perform diagnosis processing on a fault of the entire engine according to each processed feature data information, the training sample set importing module is configured to import a training sample set into the BP neural network model, and the fault diagnosis result output module is configured to output a result obtained by the BP neural network model.
Preferably, the characteristic data detection unit includes a temperature sensor, a pressure sensor, a voltage sensor, a current sensor, and an acceleration sensor.
Advantageous effects
The invention provides a fault diagnosis method and device based on a neural network. Compared with the prior art, the method has the following beneficial effects:
the fault diagnosis method and the fault diagnosis device based on the neural network comprise the steps of S1, extracting characteristic data information of fault equipment; s2, preprocessing the characteristic data information; s3, importing the characteristic information into a neural network model; s4, importing a training sample set; s5, obtaining a fault diagnosis result and S6 by the neural network model, deriving a diagnosis result of the equipment fault, and by the fault diagnosis step and by means of the BP neural network model, in the actual diagnosis process, extracting each characteristic data information in the existing engine, preprocessing the characteristic data information, and directly introducing the characteristic data information into the BP neural network model trained by the training sample set, the specific fault result of the engine can be quickly diagnosed, the efficiency of overhauling the engine is improved, the overhauling time is shortened, the engine is not required to be disassembled, the labor cost is reduced, in addition, the fault diagnosis device based on the neural network comprises a characteristic data monitoring unit, a data preprocessing module, the BP neural network model, a training sample set introduction module and a fault diagnosis result output module, wherein the characteristic data detection unit is used for collecting each parameter information in the existing fault engine, the data preprocessing module is used for processing each feature information extracted by the feature data detection unit, and is convenient, fast, safe and convenient to use.
Drawings
FIG. 1 is a schematic block flow diagram of the present invention;
FIG. 2 is a schematic structural diagram of a BP neural network according to the present invention;
fig. 3 is a block diagram schematically illustrating the structure of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides a technical solution: a fault diagnosis method based on a neural network comprises the following steps:
s1, extracting the characteristic data information of the fault equipment: extracting characteristic data information corresponding to the fault engine from the existing fault engine;
s2, preprocessing the characteristic data information: importing each feature data extracted from the existing fault engine in the step S1 into a data processing module for preprocessing;
s3, importing the characteristic information into a neural network model: importing each feature data preprocessed in the step S2 into the constructed BP neural network model;
s4, importing a training sample set: introducing a training sample set formed by existing characteristic data and known fault result data into the PB neural network model constructed in the step S3, and training the constructed BP neural network model;
s5, obtaining a fault diagnosis result by the neural network model: the method comprises the following substeps:
a. importing each characteristic data information into the whole BP neural network model in the step S2, inputting the characteristic data information into each node in the input layer, and outputting the characteristic data information serving as the output of the neuron in the input layer;
b. calculating the output of each node of the neuron in the hidden layer according to a formula to be used as the input of an output layer;
c. calculating the output of each neuron node of the output layer according to a formula;
d. and c, judging the result output by the neuron node corresponding to the output layer in the step c according to a threshold function.
S6, deriving the diagnosis result of the equipment fault: the diagnosis result of the device failure output in step S5 is derived.
Further, the characteristic data information in step S1 includes a temperature signal, an oil pressure signal, a current intensity signal, a voltage intensity signal, and a vibration signal of the faulty engine.
Further, in sub-step b in step S5, the formula for calculating the output value of each node of the neuron in the hidden layer is:
Oj＝f(Ij)＝1/[exp(-Ij)]
in the formula: omegajiIs the weight between hidden node j and input node i, θ j is the bias of hidden node j, f (x) is the function of sigmoid, and the expression is f (x) 1/[1+ exp (-x)]。
Further, in the sub-step c in step S5, the formula for calculating the output value of each neuron node in the output layer is:
yk＝f(Ik)＝1/[exp(-Ik)]
in the formula: omegakjAnd theta k is the offset of the hidden layer node k.
Further, in step S4, the training sample set is imported, and the training sample is (x)p1，xp2，xp3......xpn) The number of samples is (P ═ 1,2,3 … … P).
Referring to fig. 3, the present invention further discloses a fault diagnosis apparatus based on a neural network, which includes a feature data monitoring unit, a data preprocessing module, a BP neural network model, a training sample set importing module, and a fault diagnosis result outputting module, wherein the feature data detecting unit is configured to collect parameter information of an existing fault engine, and the data preprocessing module is configured to process feature information extracted by the feature data detecting unit.
Furthermore, the BP neural network model is used for diagnosing and processing faults of the whole engine according to the processed characteristic data information, the training sample set importing module is used for importing a training sample set into the BP neural network model, and the fault diagnosis result output module is used for outputting results obtained by the BP neural network model.
Further, the characteristic data detection unit includes a temperature sensor, a pressure sensor, a voltage sensor, a current sensor, and an acceleration sensor.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A fault diagnosis method based on a neural network is characterized by comprising the following steps:
s1, extracting the characteristic data information of the fault equipment: extracting characteristic data information corresponding to the fault engine from the existing fault engine;
s2, preprocessing the characteristic data information: importing each feature data extracted from the existing fault engine in the step S1 into a data processing module for preprocessing;
s3, importing the characteristic information into a neural network model: importing each feature data preprocessed in the step S2 into the constructed BP neural network model;
s4, importing a training sample set: introducing a training sample set formed by existing characteristic data and known fault result data into the PB neural network model constructed in the step S3, and training the constructed BP neural network model;
s5, obtaining a fault diagnosis result by the neural network model: the method comprises the following substeps:
a. importing each characteristic data information into the whole BP neural network model in the step S2, inputting the characteristic data information into each node in the input layer, and outputting the characteristic data information serving as the output of the neuron in the input layer;
b. calculating the output of each node of the neuron in the hidden layer according to a formula to be used as the input of an output layer;
c. calculating the output of each neuron node of the output layer according to a formula;
d. and c, judging the result output by the neuron node corresponding to the output layer in the step c according to a threshold function.
S6, deriving the diagnosis result of the equipment fault: the diagnosis result of the device failure output in step S5 is derived.
2. The neural network based fault diagnosis method of claim 1, wherein the characteristic data information in step S1 includes a temperature signal, an oil pressure signal, a current intensity signal, a voltage intensity signal and a vibration signal of the faulty engine.
3. The neural network-based fault diagnosis method according to claim 1, wherein in the sub-step b of the step S5, the formula for calculating the output values of the respective nodes of the neurons in the hidden layer is as follows:
Oj＝f(Ij)＝1/[exp(-Ij)]
in the formula: omegajiIs the weight between hidden node j and input node i, θ j is the bias of hidden node j, f (x) is the function of sigmoid, and the expression is f (x) 1/[1+ exp (-x)]。
4. The neural network-based fault diagnosis method according to claim 1, wherein in the sub-step c of step S5, the formula for calculating the output values of the neuron nodes in the output layer is:
yk＝f(Ik)＝1/[exp(-Ik)]
in the formula: omegakjAnd theta k is the offset of the hidden layer node k.
5. The neural network-based fault diagnosis method according to claim 1, wherein in step S4, the training sample set is imported, and the training sample set is (x)p1，xp2，xp3......xpn) The number of samples is (P ═ 1,2,3 … … P).
6. A fault diagnosis device based on a neural network is characterized by comprising a characteristic data monitoring unit, a data preprocessing module, a BP neural network model, a training sample set importing module and a fault diagnosis result outputting module, wherein the characteristic data detecting unit is used for collecting parameter information of an existing fault engine, and the data preprocessing module is used for processing the characteristic information extracted by the characteristic data detecting unit.
7. The neural network-based fault diagnosis device according to claim 6, wherein the BP neural network model is configured to diagnose faults of the entire engine according to the processed feature data information, the training sample set importing module is configured to import a training sample set into the BP neural network model, and the fault diagnosis result outputting module is configured to output a result obtained by the BP neural network model.
8. The neural network-based failure diagnosis device according to claim 6, wherein the characteristic data detection unit includes a temperature sensor, a pressure sensor, a voltage sensor, a current sensor, and an acceleration sensor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010148263.2A CN111259993A (en) | 2020-03-05 | 2020-03-05 | Fault diagnosis method and device based on neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010148263.2A CN111259993A (en) | 2020-03-05 | 2020-03-05 | Fault diagnosis method and device based on neural network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111259993A true CN111259993A (en) | 2020-06-09 |
Family
ID=70947608
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010148263.2A Pending CN111259993A (en) | 2020-03-05 | 2020-03-05 | Fault diagnosis method and device based on neural network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111259993A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111830409A (en) * | 2020-06-30 | 2020-10-27 | 佛山科学技术学院 | Motor thermal fault diagnosis method and device based on deep neural network |
| CN113435111A (en) * | 2021-06-08 | 2021-09-24 | 西安交通大学 | Fault diagnosis method and system for reciprocating compressor |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104568446A (en) * | 2014-09-27 | 2015-04-29 | 芜湖扬宇机电技术开发有限公司 | Method for diagnosing engine failure |
| CN105043776A (en) * | 2015-08-12 | 2015-11-11 | 中国人民解放军空军勤务学院 | Aircraft engine performance monitoring and fault diagnosis method |
| CN109115501A (en) * | 2018-07-12 | 2019-01-01 | 哈尔滨工业大学（威海） | A kind of Civil Aviation Engine Gas path fault diagnosis method based on CNN and SVM |
| CN109580230A (en) * | 2018-12-11 | 2019-04-05 | 中国航空工业集团公司西安航空计算技术研究所 | A kind of Fault Diagnosis of Engine and device based on BP neural network |
| CN109655275A (en) * | 2018-11-29 | 2019-04-19 | 沈阳工程学院 | A kind of gas turbine cycle heat-economy diagnostic method based on sensitivity coefficient method |
| CN110287594A (en) * | 2019-06-25 | 2019-09-27 | 东北大学 | A kind of aero-engine method for diagnosing status based on neural network algorithm |
| CN110470481A (en) * | 2019-08-13 | 2019-11-19 | 南京信息工程大学 | Fault Diagnosis of Engine based on BP neural network |
| CN110533167A (en) * | 2019-08-23 | 2019-12-03 | 中国石化销售有限公司华南分公司 | A kind of electric valve actuating mechanism method for diagnosing faults and diagnostic system |
| CN110702418A (en) * | 2019-10-10 | 2020-01-17 | 山东超越数控电子股份有限公司 | Aircraft engine fault prediction method |
-
2020
- 2020-03-05 CN CN202010148263.2A patent/CN111259993A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104568446A (en) * | 2014-09-27 | 2015-04-29 | 芜湖扬宇机电技术开发有限公司 | Method for diagnosing engine failure |
| CN105043776A (en) * | 2015-08-12 | 2015-11-11 | 中国人民解放军空军勤务学院 | Aircraft engine performance monitoring and fault diagnosis method |
| CN109115501A (en) * | 2018-07-12 | 2019-01-01 | 哈尔滨工业大学（威海） | A kind of Civil Aviation Engine Gas path fault diagnosis method based on CNN and SVM |
| CN109655275A (en) * | 2018-11-29 | 2019-04-19 | 沈阳工程学院 | A kind of gas turbine cycle heat-economy diagnostic method based on sensitivity coefficient method |
| CN109580230A (en) * | 2018-12-11 | 2019-04-05 | 中国航空工业集团公司西安航空计算技术研究所 | A kind of Fault Diagnosis of Engine and device based on BP neural network |
| CN110287594A (en) * | 2019-06-25 | 2019-09-27 | 东北大学 | A kind of aero-engine method for diagnosing status based on neural network algorithm |
| CN110470481A (en) * | 2019-08-13 | 2019-11-19 | 南京信息工程大学 | Fault Diagnosis of Engine based on BP neural network |
| CN110533167A (en) * | 2019-08-23 | 2019-12-03 | 中国石化销售有限公司华南分公司 | A kind of electric valve actuating mechanism method for diagnosing faults and diagnostic system |
| CN110702418A (en) * | 2019-10-10 | 2020-01-17 | 山东超越数控电子股份有限公司 | Aircraft engine fault prediction method |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111830409A (en) * | 2020-06-30 | 2020-10-27 | 佛山科学技术学院 | Motor thermal fault diagnosis method and device based on deep neural network |
| CN113435111A (en) * | 2021-06-08 | 2021-09-24 | 西安交通大学 | Fault diagnosis method and system for reciprocating compressor |
| CN113435111B (en) * | 2021-06-08 | 2022-10-25 | 西安交通大学 | Fault diagnosis method and system for reciprocating compressor |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109446187B (en) | Method for monitoring health state of complex equipment based on attention mechanism and neural network | |
| CN111722145B (en) | Synchronous motor excitation winding turn-to-turn short circuit mild fault diagnosis method | |
| CN109555566B (en) | Steam turbine rotor fault diagnosis method based on LSTM | |
| Gao et al. | A novel fault identification method for photovoltaic array via convolutional neural network and residual gated recurrent unit | |
| US6240343B1 (en) | Apparatus and method for diagnosing an engine using computer based models in combination with a neural network | |
| Yadav et al. | Audio signature-based condition monitoring of internal combustion engine using FFT and correlation approach | |
| CN103471841B (en) | A kind of rotating machinery vibrating failure diagnosis method | |
| CN111259993A (en) | Fault diagnosis method and device based on neural network | |
| CN114993669B (en) | Multi-sensor information fusion transmission system fault diagnosis method and system | |
| CN110196365A (en) | A kind of vehicle power drive system method for diagnosing faults | |
| CN111985558A (en) | Electric energy meter abnormity diagnosis method and system | |
| CN113608140A (en) | Battery fault diagnosis method and system | |
| CN111795819B (en) | Gear box fault diagnosis method integrating vibration and current signal collaborative learning | |
| CN114169424A (en) | Discharge capacity prediction method based on k nearest neighbor regression algorithm and electricity utilization data | |
| CN115238785A (en) | Rotary machine fault diagnosis method and system based on image fusion and integrated network | |
| Gongora et al. | Neural approach for bearing fault detection in three phase induction motors | |
| CN114881071A (en) | Synchronous motor rotor winding turn-to-turn short circuit fault diagnosis method based on multi-source information | |
| dos Santos et al. | Thermographic image-based diagnosis of failures in electrical motors using deep transfer learning | |
| Azrin et al. | An overview of the spark plug engine profile in a spark ignition engine | |
| Hassani et al. | Design of a cost-effective deep convolutional neural network–based scheme for diagnosing faults in smart grids | |
| CN116738367A (en) | Rolling bearing fault diagnosis method based on multi-scale map transducer | |
| Qu et al. | Fault diagnosis for PV arrays considering dust impact based on transformed graphical features of characteristic curves and convolutional neural network with CBAM modules | |
| CN111753876B (en) | Product quality detection method based on deep neural network | |
| CN115374829A (en) | Deep learning-based bearing fault diagnosis method and system | |
| CN115310693A (en) | Comprehensive energy data cleaning method, system, equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200609 |