CN113052365A - 一种基于mswr-lrcn的旋转类机械寿命预测方法 - Google Patents
一种基于mswr-lrcn的旋转类机械寿命预测方法 Download PDFInfo
- Publication number
- CN113052365A CN113052365A CN202110219996.5A CN202110219996A CN113052365A CN 113052365 A CN113052365 A CN 113052365A CN 202110219996 A CN202110219996 A CN 202110219996A CN 113052365 A CN113052365 A CN 113052365A
- Authority
- CN
- China
- Prior art keywords
- mswr
- lrcn
- data
- rul
- prediction method
- 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
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000012360 testing method Methods 0.000 claims abstract description 51
- 238000006731 degradation reaction Methods 0.000 claims abstract description 29
- 230000015556 catabolic process Effects 0.000 claims abstract description 28
- 238000012549 training Methods 0.000 claims abstract description 16
- 238000012545 processing Methods 0.000 claims abstract description 13
- 238000003062 neural network model Methods 0.000 claims abstract description 6
- 230000004913 activation Effects 0.000 claims description 8
- 238000011176 pooling Methods 0.000 claims description 7
- 238000010606 normalization Methods 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 3
- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 claims description 2
- 238000011478 gradient descent method Methods 0.000 claims description 2
- 230000015654 memory Effects 0.000 abstract description 4
- 238000004458 analytical method Methods 0.000 abstract description 3
- 230000002457 bidirectional effect Effects 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 13
- 238000005096 rolling process Methods 0.000 description 13
- 238000013135 deep learning Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 230000007246 mechanism Effects 0.000 description 5
- 230000010355 oscillation Effects 0.000 description 4
- 238000005070 sampling Methods 0.000 description 4
- 230000001133 acceleration Effects 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 3
- 230000008602 contraction Effects 0.000 description 3
- 230000036541 health Effects 0.000 description 3
- 230000007787 long-term memory Effects 0.000 description 3
- 230000006403 short-term memory Effects 0.000 description 3
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000007850 degeneration Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- G01M13/04—Bearings
- G01M13/045—Acoustic or vibration analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/253—Fusion techniques of extracted features
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Business, Economics & Management (AREA)
- Evolutionary Computation (AREA)
- Human Resources & Organizations (AREA)
- Biomedical Technology (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Biophysics (AREA)
- Strategic Management (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- Molecular Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Acoustics & Sound (AREA)
- Development Economics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Game Theory and Decision Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
Description
Claims (10)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110219996.5A CN113052365B (zh) | 2021-02-26 | 2021-02-26 | 一种基于mswr-lrcn的旋转类机械寿命预测方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110219996.5A CN113052365B (zh) | 2021-02-26 | 2021-02-26 | 一种基于mswr-lrcn的旋转类机械寿命预测方法 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN113052365A true CN113052365A (zh) | 2021-06-29 |
CN113052365B CN113052365B (zh) | 2022-07-01 |
Family
ID=76509606
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202110219996.5A Active CN113052365B (zh) | 2021-02-26 | 2021-02-26 | 一种基于mswr-lrcn的旋转类机械寿命预测方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113052365B (zh) |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110555230A (zh) * | 2019-07-12 | 2019-12-10 | 北京交通大学 | 基于集成gmdh框架的旋转机械剩余寿命预测方法 |
CN111832216A (zh) * | 2020-04-14 | 2020-10-27 | 新疆大学 | 基于eemd-mcnn-gru的滚动轴承剩余使用寿命预测方法 |
KR20200132665A (ko) * | 2019-05-17 | 2020-11-25 | 삼성전자주식회사 | 집중 레이어를 포함하는 생성기를 기반으로 예측 이미지를 생성하는 장치 및 그 제어 방법 |
CN112132052A (zh) * | 2020-09-24 | 2020-12-25 | 三峡大学 | 基于首层宽卷积核深度残差网络的输电线路短路故障诊断方法 |
CN112257333A (zh) * | 2020-09-24 | 2021-01-22 | 浙江工业大学 | 一种基于深度学习的机械设备内部组件寿命预测方法 |
-
2021
- 2021-02-26 CN CN202110219996.5A patent/CN113052365B/zh active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR20200132665A (ko) * | 2019-05-17 | 2020-11-25 | 삼성전자주식회사 | 집중 레이어를 포함하는 생성기를 기반으로 예측 이미지를 생성하는 장치 및 그 제어 방법 |
CN110555230A (zh) * | 2019-07-12 | 2019-12-10 | 北京交通大学 | 基于集成gmdh框架的旋转机械剩余寿命预测方法 |
CN111832216A (zh) * | 2020-04-14 | 2020-10-27 | 新疆大学 | 基于eemd-mcnn-gru的滚动轴承剩余使用寿命预测方法 |
CN112132052A (zh) * | 2020-09-24 | 2020-12-25 | 三峡大学 | 基于首层宽卷积核深度残差网络的输电线路短路故障诊断方法 |
CN112257333A (zh) * | 2020-09-24 | 2021-01-22 | 浙江工业大学 | 一种基于深度学习的机械设备内部组件寿命预测方法 |
Non-Patent Citations (3)
Title |
---|
WEI ZHANG ET AL.: "A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals", 《SENSORS》 * |
YUTING WU ET AL.: "Remaining useful life estimation of engineered systems using vanilla LSTM neural networks", 《NEUROCOMPUTING》 * |
曾安: "基于深度双向LSTM 的股票推荐系统", 《计算机科学》 * |
Also Published As
Publication number | Publication date |
---|---|
CN113052365B (zh) | 2022-07-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Medjaher et al. | Data-driven prognostics based on health indicator construction: Application to PRONOSTIA's data | |
Camci et al. | Feature evaluation for effective bearing prognostics | |
CN103115789B (zh) | 金属结构损伤剩余寿命的第二代小波支持向量机评估方法 | |
CN112179691B (zh) | 基于对抗学习策略的机械装备运行状态异常检测系统和方法 | |
CN115238753B (zh) | 一种基于局部离群因子的自适应shm数据清洗方法 | |
CN112257333A (zh) | 一种基于深度学习的机械设备内部组件寿命预测方法 | |
CN107505396A (zh) | 一种结构损伤在线实时监测方法及系统 | |
CN111896254A (zh) | 一种变速变载大型滚动轴承故障预测系统及方法 | |
Medjaher et al. | Feature extraction and evaluation for health assessment and failure prognostics | |
CN105571638A (zh) | 一种机械设备故障组合预测系统及方法 | |
Qin et al. | Remaining useful life prediction for rotating machinery based on optimal degradation indicator | |
CN112305388B (zh) | 一种发电机定子绕组绝缘局部放电故障在线监测诊断方法 | |
CN110990788A (zh) | 一种基于三元维纳过程的轴承剩余寿命预测方法 | |
CN109187024A (zh) | 一种活塞式空压机曲轴箱滚动轴承故障诊断方法 | |
CN117969092B (zh) | 一种盾构机主轴承的故障检测方法、设备及介质 | |
CN115901263A (zh) | 基于数字孪生的滚动轴承剩余寿命在线预测方法及系统 | |
CN104318043A (zh) | 滚动轴承振动性能可靠性变异过程检测方法与装置 | |
KR20210006832A (ko) | 기계고장 진단 방법 및 장치 | |
Hassan et al. | An In-Depth Study of Vibration Sensors for Condition Monitoring | |
CN113052365B (zh) | 一种基于mswr-lrcn的旋转类机械寿命预测方法 | |
CN117218602A (zh) | 一种结构健康监测数据异常诊断方法及系统 | |
Castilla-Gutiérrez et al. | Control and prediction protocol for bearing failure through spectral power density | |
CN103617350A (zh) | 一种基于诊断证据平滑更新的旋转机械设备故障诊断方法 | |
CN113052060A (zh) | 基于数据增强的轴承剩余寿命预测方法、装置及电子设备 | |
Mustafa et al. | Experimental research on machinery fault simulator (MFS): A review |
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 | ||
TR01 | Transfer of patent right |
Effective date of registration: 20231220 Address after: 232000 meters west of Wangwei Village Committee, Jiahe Town, Panji District, Huainan City, Anhui Province Patentee after: Huaihe Energy Zhunnan Panji Power Generation Co.,Ltd. Patentee after: Zheng Xiaoyong Patentee after: Zang Runze Patentee after: Chen Hao Patentee after: Chen Jiejue Patentee after: Mao Xiangyun Patentee after: Yang Xiangrong Patentee after: Wang Xianquan Patentee after: Pan Lijuan Address before: 310014 No. 18 Chao Wang Road, Xiacheng District, Zhejiang, Hangzhou Patentee before: JIANG University OF TECHNOLOGY |
|
TR01 | Transfer of patent right |