Fault diagnosis method and system based on standard self-learning data enhancement
The invention provides a fault diagnosis method and system based on standard self-learning data enhancement, and relates to the technical field of bearing fault diagnosis, and the method comprises the steps: constructing a fault diagnosis model based on a one-dimensional convolutional neural network...
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| creator | AN ZENGHUI YAN YINGLONG ZHANG YUXI WANG HOULIANG |
| description | The invention provides a fault diagnosis method and system based on standard self-learning data enhancement, and relates to the technical field of bearing fault diagnosis, and the method comprises the steps: constructing a fault diagnosis model based on a one-dimensional convolutional neural network; training the fault diagnosis model through a cross adversarial training mode of standard self-learning and data enhancement to obtain a complete data set and an intelligent fault diagnosis model under a strong non-stationary working condition; inputting the collected vibration signal to be diagnosed into the trained intelligent fault diagnosis model to obtain a bearing fault diagnosis result; according to the method, the one-dimensional convolutional neural network is taken as a basic framework, disturbance data is generated by using an incomplete training data set through a cross adversarial training mode of standard self-learning and data enhancement, a fault diagnosis model under a strong non-stationary workin |
| format | Patent |
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training the fault diagnosis model through a cross adversarial training mode of standard self-learning and data enhancement to obtain a complete data set and an intelligent fault diagnosis model under a strong non-stationary working condition; inputting the collected vibration signal to be diagnosed into the trained intelligent fault diagnosis model to obtain a bearing fault diagnosis result; according to the method, the one-dimensional convolutional neural network is taken as a basic framework, disturbance data is generated by using an incomplete training data set through a cross adversarial training mode of standard self-learning and data enhancement, a fault diagnosis model under a strong non-stationary workin</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; MEASURING ; PHYSICS ; TESTING ; TESTING STATIC OR DYNAMIC BALANCE OF MACHINES ORSTRUCTURES ; TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230307&DB=EPODOC&CC=CN&NR=115753103A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230307&DB=EPODOC&CC=CN&NR=115753103A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>AN ZENGHUI</creatorcontrib><creatorcontrib>YAN YINGLONG</creatorcontrib><creatorcontrib>ZHANG YUXI</creatorcontrib><creatorcontrib>WANG HOULIANG</creatorcontrib><title>Fault diagnosis method and system based on standard self-learning data enhancement</title><description>The invention provides a fault diagnosis method and system based on standard self-learning data enhancement, and relates to the technical field of bearing fault diagnosis, and the method comprises the steps: constructing a fault diagnosis model based on a one-dimensional convolutional neural network; 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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING MEASURING PHYSICS TESTING TESTING STATIC OR DYNAMIC BALANCE OF MACHINES ORSTRUCTURES TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR |
| title | Fault diagnosis method and system based on standard self-learning data enhancement |
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