WO2023096185A1 - Procédé pour diagnostiquer une défaillance de machine sur la base de l'apprentissage profond en utilisant des sons et des vibrations, et dispositif de diagnostic l'utilisant - Google Patents
Procédé pour diagnostiquer une défaillance de machine sur la base de l'apprentissage profond en utilisant des sons et des vibrations, et dispositif de diagnostic l'utilisant Download PDFInfo
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- WO2023096185A1 WO2023096185A1 PCT/KR2022/016624 KR2022016624W WO2023096185A1 WO 2023096185 A1 WO2023096185 A1 WO 2023096185A1 KR 2022016624 W KR2022016624 W KR 2022016624W WO 2023096185 A1 WO2023096185 A1 WO 2023096185A1
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- failure
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- failure classification
- deep learning
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 66
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000013135 deep learning Methods 0.000 title claims abstract description 28
- 238000013136 deep learning model Methods 0.000 claims abstract description 53
- 230000003595 spectral effect Effects 0.000 claims description 29
- 238000001228 spectrum Methods 0.000 claims description 21
- 230000015654 memory Effects 0.000 claims description 11
- 238000011156 evaluation Methods 0.000 claims description 10
- 238000005070 sampling Methods 0.000 claims description 9
- 230000005484 gravity Effects 0.000 claims description 5
- 238000009826 distribution Methods 0.000 claims description 4
- 230000007704 transition Effects 0.000 claims description 4
- 238000000605 extraction Methods 0.000 abstract description 6
- 239000000284 extract Substances 0.000 abstract description 2
- 238000003860 storage Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000012774 diagnostic algorithm Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000000306 recurrent effect Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
- 230000006403 short-term memory Effects 0.000 description 1
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/20—Ensemble learning
Definitions
- the present invention intends to propose a method for diagnosing machine failures based on deep learning regardless of the type of data.
- the first to n-th deep learning models may include at least one supervised learning model and at least one unsupervised learning model for classifying the failure state of the machine.
- the first feature to the mth feature include a Mel spectrogram divided by a frequency range in consideration of human hearing, an MFCC that is a coefficient when the Mel spectrogram is regarded as one signal and cosine-transformed, and a signal sign for a unit time Zero crossing rate, which is the number of transitions, spectral Roll-Off, which is the value of f when the energy corresponding to the frequency below f matches a specific ratio of the total energy, spectral centroid, which is the center of gravity or weighted average of the spectrum, and the variance of the spectral distribution It may include at least two of spectral bandwidth, spectral contrast, which is a difference between maximum and minimum energies in a partial spectrum, spectral flatness, which is a flatness of a spectrum, and chromagram, which is a harmonic characteristic of a spectrum.
- the diagnosis device determines the failure of the machine among the 1_1 th failure classification information to the n_m th failure classification information by referring to the probability of answering each of the 1_1 th failure classification information to the n_m th failure classification information. It is possible to select specific failure classification information for , and output the specific failure classification information as the failure diagnosis information.
- the diagnosis apparatus may output the failure diagnosis information by performing a weighted sum of the 1_1 th failure classification information to the n_m th failure classification information.
- the diagnostic apparatus for diagnosing machine failures using sound or vibration based on deep learning instructions for diagnosing machine failures using sound or vibration based on deep learning memory in which they are stored; and a processor configured to perform an operation for diagnosing a failure of a machine using sound or vibration based on the deep learning according to the instructions stored in the memory.
- the present invention can actively cope with unlearned failure types by using sound data or vibration data based on deep learning.
- FIG. 2 is a flow chart schematically showing a method for diagnosing a machine failure using sound or vibration based on deep learning according to an embodiment of the present invention
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- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Software Systems (AREA)
- Automation & Control Theory (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
- Human Computer Interaction (AREA)
Abstract
La présente invention concerne un procédé pour diagnostiquer une défaillance de machine sur la base de l'apprentissage profond en utilisant des sons ou des vibrations. Le procédé comprend les étapes suivantes : (A) lorsque des données sonores ou des données de vibration détectées à partir d'une machine sont acquises, un dispositif de diagnostic introduit les données sonores ou les données de vibration dans une unité d'extraction de caractéristiques de sorte que l'unité d'extraction de caractéristiques extrait des caractéristiques 1 à m à partir des données sonores ou des données de vibration ; (b) le dispositif de diagnostic introduit les caractéristiques 1 à m dans des modèles d'apprentissage profond 1 à n de façon à amener les modèles d'apprentissage profond 1 à n à apprendre les caractéristiques 1 à m et délivrer en sortie des informations de classification de défaillance 1_1 à n_m qui classent des états de défaillance pour les caractéristiques 1 à m ; et (c) le dispositif de diagnostic délivre en sortie des informations de diagnostic de défaillance qui diagnostiquent la défaillance de la machine en assemblant les informations de classification de défaillance 1_1 à n_m
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020210167790A KR20230080242A (ko) | 2021-11-29 | 2021-11-29 | 딥 러닝 기반으로 음향 및 진동을 이용하여 기계의 고장을 진단하는 방법 및 이를 이용한 진단 장치 |
| KR10-2021-0167790 | 2021-11-29 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2023096185A1 true WO2023096185A1 (fr) | 2023-06-01 |
Family
ID=86539789
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/KR2022/016624 WO2023096185A1 (fr) | 2021-11-29 | 2022-10-27 | Procédé pour diagnostiquer une défaillance de machine sur la base de l'apprentissage profond en utilisant des sons et des vibrations, et dispositif de diagnostic l'utilisant |
Country Status (2)
| Country | Link |
|---|---|
| KR (1) | KR20230080242A (fr) |
| WO (1) | WO2023096185A1 (fr) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116595444A (zh) * | 2023-07-19 | 2023-08-15 | 北京大学第一医院 | 一种基于深度学习的医疗器械的故障类别检测方法和系统 |
| CN118335118A (zh) * | 2024-06-12 | 2024-07-12 | 陕西骏景索道运营管理有限公司 | 基于声纹分析的索道入侵事件快速分析预警方法及装置 |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102027389B1 (ko) * | 2019-03-20 | 2019-10-01 | (주)브이엠에스 | 오토인코더와 딥러닝을 이용한 기계 장비 고장 진단 장치 |
| KR20210055992A (ko) * | 2019-11-08 | 2021-05-18 | 엘지전자 주식회사 | 인공지능 모델 관리 방법 및 장치 |
| KR20210077389A (ko) * | 2019-12-17 | 2021-06-25 | (주)유코아시스템 | 기계설비의 기계음을 이용한 딥러닝기반 이상징후 감지시스템 |
| KR20210081145A (ko) * | 2019-12-23 | 2021-07-01 | 시그널링크 주식회사 | 진동과 소음신호를 이용한 기계결함진단장치 및 그 신호를 이용한 빅데이터 기반의 스마트 센서 시스템 |
| KR20210082596A (ko) * | 2019-12-26 | 2021-07-06 | 주식회사 모트롤 | 감속 장치의 고장진단 장치 및 방법 |
-
2021
- 2021-11-29 KR KR1020210167790A patent/KR20230080242A/ko active Search and Examination
-
2022
- 2022-10-27 WO PCT/KR2022/016624 patent/WO2023096185A1/fr unknown
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102027389B1 (ko) * | 2019-03-20 | 2019-10-01 | (주)브이엠에스 | 오토인코더와 딥러닝을 이용한 기계 장비 고장 진단 장치 |
| KR20210055992A (ko) * | 2019-11-08 | 2021-05-18 | 엘지전자 주식회사 | 인공지능 모델 관리 방법 및 장치 |
| KR20210077389A (ko) * | 2019-12-17 | 2021-06-25 | (주)유코아시스템 | 기계설비의 기계음을 이용한 딥러닝기반 이상징후 감지시스템 |
| KR20210081145A (ko) * | 2019-12-23 | 2021-07-01 | 시그널링크 주식회사 | 진동과 소음신호를 이용한 기계결함진단장치 및 그 신호를 이용한 빅데이터 기반의 스마트 센서 시스템 |
| KR20210082596A (ko) * | 2019-12-26 | 2021-07-06 | 주식회사 모트롤 | 감속 장치의 고장진단 장치 및 방법 |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116595444A (zh) * | 2023-07-19 | 2023-08-15 | 北京大学第一医院 | 一种基于深度学习的医疗器械的故障类别检测方法和系统 |
| CN116595444B (zh) * | 2023-07-19 | 2023-09-26 | 北京大学第一医院 | 一种基于深度学习的医疗器械的故障类别检测方法和系统 |
| CN118335118A (zh) * | 2024-06-12 | 2024-07-12 | 陕西骏景索道运营管理有限公司 | 基于声纹分析的索道入侵事件快速分析预警方法及装置 |
Also Published As
| Publication number | Publication date |
|---|---|
| KR20230080242A (ko) | 2023-06-07 |
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