Intelligent Fault Diagnosis Method for Blade Damage of Quad-Rotor UAV Based on Stacked Pruning Sparse Denoising Autoencoder and Convolutional Neural Network
This paper introduces a new intelligent fault diagnosis method based on stack pruning sparse denoising autoencoder and convolutional neural network (sPSDAE-CNN). This method processes the original input data by using a stack denoising autoencoder. Different from the traditional autoencoder, stack pr...
Gespeichert in:
| Veröffentlicht in: | Machines (Basel) Jg. 9; H. 12; S. 360 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Basel
MDPI AG
01.12.2021
|
| Schlagworte: | |
| ISSN: | 2075-1702, 2075-1702 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | This paper introduces a new intelligent fault diagnosis method based on stack pruning sparse denoising autoencoder and convolutional neural network (sPSDAE-CNN). This method processes the original input data by using a stack denoising autoencoder. Different from the traditional autoencoder, stack pruning sparse denoising autoencoder includes a fully connected autoencoding network, the features extracted from the front layer of the network are used for the operation of the subsequent layer, which means that some new connections will appear between the front and rear layers of the network, reduce the loss of information, and obtain more effective features. Firstly, a one-dimensional sliding window is introduced for data enhancement. In addition, transforming one-dimensional time-domain data into the two-dimensional gray image can further improve the deep learning (DL) ability of models. At the same time, pruning operation is introduced to improve the training efficiency and accuracy of the network. The convolutional neural network model with sPSDAE has a faster training speed, strong adaptability to noise interference signals, and can also suppress the over-fitting problem of the convolutional neural network to a certain extent. Actual experiments show that for the fault of unmanned aerial vehicle (UAV) blade damage, the sPSDAE-CNN model we use has better stability and reliable prediction accuracy than traditional convolutional neural networks. At the same time, For noise signals, better results can be obtained. The experimental results show that the sPSDAE-CNN model still has a good diagnostic accuracy rate in a high-noise environment. In the case of a signal-to-noise ratio of −4, it still has an accuracy rate of 90%. |
|---|---|
| AbstractList | This paper introduces a new intelligent fault diagnosis method based on stack pruning sparse denoising autoencoder and convolutional neural network (sPSDAE-CNN). This method processes the original input data by using a stack denoising autoencoder. Different from the traditional autoencoder, stack pruning sparse denoising autoencoder includes a fully connected autoencoding network, the features extracted from the front layer of the network are used for the operation of the subsequent layer, which means that some new connections will appear between the front and rear layers of the network, reduce the loss of information, and obtain more effective features. Firstly, a one-dimensional sliding window is introduced for data enhancement. In addition, transforming one-dimensional time-domain data into the two-dimensional gray image can further improve the deep learning (DL) ability of models. At the same time, pruning operation is introduced to improve the training efficiency and accuracy of the network. The convolutional neural network model with sPSDAE has a faster training speed, strong adaptability to noise interference signals, and can also suppress the over-fitting problem of the convolutional neural network to a certain extent. Actual experiments show that for the fault of unmanned aerial vehicle (UAV) blade damage, the sPSDAE-CNN model we use has better stability and reliable prediction accuracy than traditional convolutional neural networks. At the same time, For noise signals, better results can be obtained. The experimental results show that the sPSDAE-CNN model still has a good diagnostic accuracy rate in a high-noise environment. In the case of a signal-to-noise ratio of −4, it still has an accuracy rate of 90%. |
| Author | Yang, Pu Geng, Huilin Liu, Peng Wen, Chenwan |
| Author_xml | – sequence: 1 givenname: Pu orcidid: 0000-0003-2283-8835 surname: Yang fullname: Yang, Pu – sequence: 2 givenname: Chenwan orcidid: 0000-0002-0694-8287 surname: Wen fullname: Wen, Chenwan – sequence: 3 givenname: Huilin orcidid: 0000-0002-3653-5619 surname: Geng fullname: Geng, Huilin – sequence: 4 givenname: Peng surname: Liu fullname: Liu, Peng |
| BookMark | eNp1UcluFDEQbaEgEULOXC1xbmL3YrePkwmBkRK2BK5WtZeOJz2uie0G8S98bHpmQEKRqMsrvar3VMvL4ihgsEXxmtG3dS3p2Qb0nQ82SVbRmtNnxXFFRVsyQaujf_IXxWlKazqHZHXXdMfF71XIdhz9YEMmlzCNmVx4GAImn8i1zXdoiMNIzkcwllzABgZL0JEvE5jyK-a59G3xnZxDsoZgIDcZ9P2cfo5T8GEgN1uIaRbagD7tiMWU0QaNxkYCwZAlhh84TtljgJF8tFPcQ_6J8f5V8dzBmOzpHzwpbi_f3S4_lFef3q-Wi6tS14Ln0nROc9oaqaXsWc8c6LamVSsd0K6jjvPGtoJJXvW8dgaAiUYbVrtetFK7-qRYHWwNwlpto99A_KUQvNoTGAcFMXs9WtUKZ5gxjtfcNM6JjnHtTDW7iV67vdebg9c24sNkU1ZrnOK8WlIVZ5WQzQxzV3vo0hFTitYp7TPsbpAj-FExqnZvVU_eOuvOnuj-Tvs_xSOAW6ve |
| CitedBy_id | crossref_primary_10_1088_1742_6596_2822_1_012084 crossref_primary_10_1016_j_jestch_2024_101702 crossref_primary_10_3390_machines10020146 crossref_primary_10_1007_s00170_023_12453_3 crossref_primary_10_3390_s23104907 crossref_primary_10_1007_s13272_024_00752_8 crossref_primary_10_1016_j_nanoen_2025_111073 crossref_primary_10_3390_machines13080697 crossref_primary_10_1007_s11227_023_05584_7 crossref_primary_10_1016_j_ymssp_2024_111418 crossref_primary_10_1155_2022_7674421 crossref_primary_10_1155_2023_6608967 crossref_primary_10_1177_09544100241262550 crossref_primary_10_3390_drones7050286 crossref_primary_10_1038_s41598_024_69462_9 crossref_primary_10_1007_s10846_025_02267_8 crossref_primary_10_1038_s41598_024_85076_7 crossref_primary_10_3390_machines10080690 crossref_primary_10_1016_j_eswa_2025_128940 |
| Cites_doi | 10.1016/j.jsv.2016.05.027 10.1016/j.knosys.2019.105313 10.1038/nature14539 10.1016/j.jfranklin.2020.04.024 10.1109/TKDE.2004.1269664 10.1109/5.58337 10.2478/pomr-2018-0079 10.1016/j.measurement.2020.108815 10.1109/ICASSP.2019.8682194 10.1016/j.measurement.2021.109116 10.1016/j.sigpro.2016.07.028 10.3115/v1/P14-1062 10.1016/j.jcps.2014.09.004 10.1109/MIE.2020.2964814 10.1016/j.jngse.2019.103039 10.3390/robotics8030059 10.2174/1874120701004010123 10.1016/j.arcontrol.2015.03.004 10.1177/17298814211002734 10.1016/j.ymssp.2020.106861 10.23919/ACC45564.2020.9148044 10.1016/j.measurement.2016.07.054 10.1109/TAES.2011.5937287 10.1007/s12325-017-0505-z 10.1155/2021/6672812 10.1109/ACCESS.2021.3063277 10.1007/s10921-017-0420-x 10.1177/09544100211015587 10.1109/ACCESS.2020.2992692 10.1007/s12161-017-0903-5 10.1109/TITS.2019.2897583 10.33969/JIEC.2020.21006 10.1108/ILT-11-2019-0496 10.1016/j.knosys.2021.106796 10.3390/s17020425 10.1109/ChiCC.2015.7260639 |
| ContentType | Journal Article |
| Copyright | 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 7TB 8FD 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO FR3 HCIFZ L6V M7S PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS DOA |
| DOI | 10.3390/machines9120360 |
| DatabaseName | CrossRef Mechanical & Transportation Engineering Abstracts Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central Technology collection ProQuest One ProQuest Central Engineering Research Database SciTech Premium Collection ProQuest Engineering Collection Engineering Database Proquest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database Technology Collection Technology Research Database ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest Central Korea ProQuest Central (New) Engineering Collection Engineering Database ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection ProQuest One Academic UKI Edition Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database (ProQuest) url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2075-1702 |
| ExternalDocumentID | oai_doaj_org_article_57fd1ddf636d4ff7816cfd2cd17bcfcf 10_3390_machines9120360 |
| GroupedDBID | 5VS 8FE 8FG AADQD AAFWJ AAYXX ABJCF ACIWK ADBBV ADMLS AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS BCNDV BENPR BGLVJ CCPQU CITATION GROUPED_DOAJ HCIFZ IAO KQ8 L6V M7S MODMG M~E OK1 PHGZM PHGZT PIMPY PQGLB PROAC PTHSS RNS 7TB 8FD ABUWG AZQEC DWQXO FR3 PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c376t-d8fc605d9c99b1b1fac530259fa0880f664e571962b63fdaa174cd13fb759cf3 |
| IEDL.DBID | M7S |
| ISICitedReferencesCount | 20 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000738096500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2075-1702 |
| IngestDate | Fri Oct 03 12:24:21 EDT 2025 Fri Jul 25 12:14:07 EDT 2025 Tue Nov 18 22:21:10 EST 2025 Sat Nov 29 07:16:25 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c376t-d8fc605d9c99b1b1fac530259fa0880f664e571962b63fdaa174cd13fb759cf3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-2283-8835 0000-0002-3653-5619 0000-0002-0694-8287 |
| OpenAccessLink | https://www.proquest.com/docview/2612794261?pq-origsite=%requestingapplication% |
| PQID | 2612794261 |
| PQPubID | 2032370 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_57fd1ddf636d4ff7816cfd2cd17bcfcf proquest_journals_2612794261 crossref_citationtrail_10_3390_machines9120360 crossref_primary_10_3390_machines9120360 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-12-01 |
| PublicationDateYYYYMMDD | 2021-12-01 |
| PublicationDate_xml | – month: 12 year: 2021 text: 2021-12-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Machines (Basel) |
| PublicationYear | 2021 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Polat (ref_16) 2020; 2 Tang (ref_13) 2020; 8 Tao (ref_14) 2020; 357 Guo (ref_40) 2016; 93 Chen (ref_21) 2020; 64 Wang (ref_32) 2021; 2021 ref_12 Che (ref_27) 2020; 72 ref_33 ref_31 Chen (ref_22) 2019; 21 Chen (ref_7) 2021; 176 Ioffe (ref_36) 2015; 37 Guo (ref_30) 2021; 18 LeCun (ref_34) 2015; 521 Tan (ref_44) 2004; 16 Chady (ref_10) 2017; 36 Zhu (ref_4) 2018; 25 Esteki (ref_11) 2017; 10 Azamfar (ref_19) 2020; 144 Xiao (ref_9) 2021; 9 Sibi (ref_35) 2013; 47 ref_25 ref_24 Lu (ref_37) 2017; 130 ref_45 Haupt (ref_3) 2015; 34 Yu (ref_2) 2015; 39 Cheng (ref_28) 2021; 216 Werbos (ref_39) 1990; 78 ref_41 Xiong (ref_17) 2020; 2 Janssens (ref_43) 2016; 377 Avci (ref_42) 2017; 7 Hu (ref_5) 2019; 72 He (ref_20) 2021; 191 Glowacz (ref_15) 2021; 171 ref_29 Liu (ref_6) 2011; 31 Huang (ref_23) 2021; 70 ref_26 Chen (ref_8) 2015; 25 Heys (ref_38) 2010; 4 Hu (ref_18) 2020; 14 Bateman (ref_1) 2011; 47 |
| References_xml | – volume: 377 start-page: 331 year: 2016 ident: ref_43 article-title: Convolutional neural network based fault detection for rotating machinery publication-title: J. Sound Vib. doi: 10.1016/j.jsv.2016.05.027 – volume: 191 start-page: 105313 year: 2021 ident: ref_20 article-title: Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2019.105313 – volume: 521 start-page: 436 year: 2015 ident: ref_34 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 357 start-page: 7286 year: 2020 ident: ref_14 article-title: An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks publication-title: J. Frankl. Inst. doi: 10.1016/j.jfranklin.2020.04.024 – volume: 16 start-page: 385 year: 2004 ident: ref_44 article-title: A support vector machine with a hybrid kernel and minimal Vapnik-Chervonenkis dimension publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2004.1269664 – volume: 78 start-page: 1550 year: 1990 ident: ref_39 article-title: Backpropagation through time: What it does and how to do it publication-title: Proc. IEEE doi: 10.1109/5.58337 – volume: 25 start-page: 92 year: 2018 ident: ref_4 article-title: Research on SDG fault diagnosis of ocean shipping boiler system based on fuzzy granular computing under data fusion publication-title: Pol. Marit. Res. doi: 10.2478/pomr-2018-0079 – volume: 171 start-page: 108815 year: 2021 ident: ref_15 article-title: Fault diagnosis of electric impact drills using thermal imaging publication-title: Measurement doi: 10.1016/j.measurement.2020.108815 – ident: ref_41 doi: 10.1109/ICASSP.2019.8682194 – volume: 176 start-page: 109116 year: 2021 ident: ref_7 article-title: Fault diagnosis of rolling bearing using marine predators algorithm-based support vector machine and topology learning and out-of-sample embedding publication-title: Measurement doi: 10.1016/j.measurement.2021.109116 – volume: 130 start-page: 377 year: 2017 ident: ref_37 article-title: Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification publication-title: Signal Process. doi: 10.1016/j.sigpro.2016.07.028 – volume: 37 start-page: 448 year: 2015 ident: ref_36 article-title: Batch normalization: Accelerating deep network training by reducing internal covariate shift publication-title: Int. Conf. Mach. Learn. – ident: ref_33 doi: 10.3115/v1/P14-1062 – volume: 25 start-page: 326 year: 2015 ident: ref_8 article-title: The effects of affect, processing goals and temporal distance on information processing: Qualifications on temporal construal theory publication-title: J. Consum. Psychol. doi: 10.1016/j.jcps.2014.09.004 – volume: 14 start-page: 65 year: 2020 ident: ref_18 article-title: Advanced fault diagnosis for lithium-ion battery systems: A review of fault mechanisms, fault features, and diagnosis procedures publication-title: IEEE Ind. Electron. Mag. doi: 10.1109/MIE.2020.2964814 – volume: 72 start-page: 103039 year: 2019 ident: ref_5 article-title: Fuzzy fault tree analysis of hydraulic fracturing flowback water storage failure publication-title: J. Nat. Gas Sci. Eng. doi: 10.1016/j.jngse.2019.103039 – volume: 7 start-page: 49 year: 2017 ident: ref_42 article-title: Structural damage detection in real time: Implementation of 1D convolutional neural networks for SHM applications publication-title: Struct. Health Monit. Damage Detect. – ident: ref_24 doi: 10.3390/robotics8030059 – volume: 4 start-page: 123 year: 2010 ident: ref_38 article-title: Revisiting the simplified Bernoulli equation publication-title: Open Biomed. Eng. J. doi: 10.2174/1874120701004010123 – volume: 2 start-page: 72 year: 2020 ident: ref_17 article-title: Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles publication-title: Appl. Energy – volume: 39 start-page: 46 year: 2015 ident: ref_2 article-title: A survey of fault-tolerant controllers based on safety-related issues publication-title: Annu. Rev. Control doi: 10.1016/j.arcontrol.2015.03.004 – volume: 18 start-page: 17298814211002734 year: 2021 ident: ref_30 article-title: Robust fault diagnosis and fault-tolerant control for nonlinear quadrotor unmanned aerial vehicle system with unknown actuator faults publication-title: Int. J. Adv. Robot. Syst. doi: 10.1177/17298814211002734 – volume: 144 start-page: 106861 year: 2020 ident: ref_19 article-title: Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2020.106861 – ident: ref_25 doi: 10.23919/ACC45564.2020.9148044 – volume: 93 start-page: 490 year: 2016 ident: ref_40 article-title: Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis publication-title: Measurement doi: 10.1016/j.measurement.2016.07.054 – volume: 47 start-page: 2119 year: 2011 ident: ref_1 article-title: Fault diagnosis and fault-tolerant control strategy for the aerosonde UAV publication-title: IEEE Trans. Aerosp. Electron. Syst. doi: 10.1109/TAES.2011.5937287 – volume: 34 start-page: 986 year: 2015 ident: ref_3 article-title: Expert system for bone scan interpretation improves progression assessment in bone metastatic prostate cancer publication-title: Adv. Ther. doi: 10.1007/s12325-017-0505-z – volume: 31 start-page: 85 year: 2011 ident: ref_6 article-title: A complete analytic model for fault diagnosis of power systems publication-title: Proc. Chin. Soc. Electr. Eng. – ident: ref_29 – volume: 2021 start-page: 6672812 year: 2021 ident: ref_32 article-title: A Robust Fault-Tolerant Control for Quadrotor Helicopters against Sensor Faults and External Disturbances publication-title: Complexity doi: 10.1155/2021/6672812 – volume: 47 start-page: 1264 year: 2013 ident: ref_35 article-title: Analysis of different activation functions using back propagation neural networks publication-title: J. Theor. Appl. Inf. Technol. – volume: 9 start-page: 39839 year: 2021 ident: ref_9 article-title: A multidimensional information fusion-based matching decision method for manufacturing service resource publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3063277 – volume: 36 start-page: 40 year: 2017 ident: ref_10 article-title: The application of rough sets theory to design of weld defect classifiers publication-title: J. Nondestruct. Eval. doi: 10.1007/s10921-017-0420-x – volume: 64 start-page: 1 year: 2020 ident: ref_21 article-title: Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives publication-title: IEEE Trans. Intell. Transp. Syst. – ident: ref_31 doi: 10.1177/09544100211015587 – volume: 8 start-page: 86510 year: 2020 ident: ref_13 article-title: Convolutional neural network in intelligent fault diagnosis toward rotatory machinery publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2992692 – volume: 10 start-page: 3312 year: 2017 ident: ref_11 article-title: Chromatographic fingerprinting with multivariate data analysis for detection and quantification of apricot kernel in almond powder publication-title: Food Anal. Methods doi: 10.1007/s12161-017-0903-5 – volume: 70 start-page: 1 year: 2021 ident: ref_23 article-title: Fault diagnosis of high-speed train bogie based on the improved-CEEMDAN and 1-D CNN algorithms publication-title: IEEE Trans. Instrum. Meas. – volume: 21 start-page: 450 year: 2019 ident: ref_22 article-title: A review of fault detection and diagnosis for the traction system in high-speed trains publication-title: Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2019.2897583 – volume: 2 start-page: 72 year: 2020 ident: ref_16 article-title: The fault diagnosis based on deep long short-term memory model from the vibration signals in the computer numerical control machines publication-title: J. Inst. Electron. Comput. doi: 10.33969/JIEC.2020.21006 – volume: 72 start-page: 947 year: 2020 ident: ref_27 article-title: Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network publication-title: Ind. Lubr. Tribol. doi: 10.1108/ILT-11-2019-0496 – volume: 216 start-page: 106796 year: 2021 ident: ref_28 article-title: Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2021.106796 – ident: ref_45 – ident: ref_26 doi: 10.3390/s17020425 – ident: ref_12 doi: 10.1109/ChiCC.2015.7260639 |
| SSID | ssj0000913848 |
| Score | 2.3274395 |
| Snippet | This paper introduces a new intelligent fault diagnosis method based on stack pruning sparse denoising autoencoder and convolutional neural network... |
| SourceID | doaj proquest crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 360 |
| SubjectTerms | Accuracy anti-noise Artificial neural networks convolutional neural network Damage Drones Fault diagnosis Feature extraction Image enhancement intelligent fault diagnosis Kalman filters Mathematical models Neural networks Noise Noise reduction Pruning Sensors Signal processing Signal to noise ratio stacked pruning sparse denoising autoencoder Training Unmanned aerial vehicles |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1NT9wwELUQ4kAPVYFW3RaqOfTAJe06n_ZxF7qiUouA0opbZHsy0kpLgnYTfk1_LGMnrLZUqJceIkuRnVie8cw8Z_JGiI-pNrydsyRSaeqilB1ApFRmI4lsCM0YpcXwo_C34vxc3dzoi41SXz4nrKcH7heOATuhRKQ8yTElKpTMHWHsUBbWkSNvfTnq2QBTwQZrmahU9Vw-CeP6z7chN7Faaek_vY3_cEOBrf8vYxw8zOyVeDmEhjDpp7Qntqp6X7zYIAw8EL-_rhk0W5iZbtHCaZ8rN1_B91AMGjgKhenCYAWn5patBTQEl53B6KphfA0_J79gyq4LoamBQ03exQgXy86fj8CPO8a5PLCqm7k_RIBJ1zae6hKrJZga4aSp7wdl5al6Zo_QhFTy1-J69uX65Cwa6itEjs1KG6Eix2gGtdPaSivJC41jIE2Gbc-Y8jytsoK3aGzzhNAYRi-87gnZItOOkjdiu27q6q0AG-sszkjzZVOD1hp2vaQ8Xxo_Ly5G4tPjapdu4B73JTAWJWMQL57yiXhG4ng94K6n3Xi-69SLb93N82WHG6xF5aBF5b-0aCQOH4VfDpt4VXp2NTZX3Lz7H-94L3ZjnxATcmEOxXa77KojsePu2_lq-SHo7wN6hvxo priority: 102 providerName: Directory of Open Access Journals |
| Title | Intelligent Fault Diagnosis Method for Blade Damage of Quad-Rotor UAV Based on Stacked Pruning Sparse Denoising Autoencoder and Convolutional Neural Network |
| URI | https://www.proquest.com/docview/2612794261 https://doaj.org/article/57fd1ddf636d4ff7816cfd2cd17bcfcf |
| Volume | 9 |
| WOSCitedRecordID | wos000738096500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2075-1702 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913848 issn: 2075-1702 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2075-1702 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913848 issn: 2075-1702 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2075-1702 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913848 issn: 2075-1702 databaseCode: M7S dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2075-1702 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913848 issn: 2075-1702 databaseCode: BENPR dateStart: 20130301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database (ProQuest) customDbUrl: eissn: 2075-1702 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913848 issn: 2075-1702 databaseCode: PIMPY dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9NAEF5BywEOvBGBEs2BAxfT-O09oaRNRCUahbagcrJ2d7woUmoHP3rkl_Bjmdk44SW4cLAtedfWWjP7zc7s-BshXkZS0XSOQy-LIuNFZAC8LIu15yMBoRqhr9H9KPwunc-zy0u56ANuTZ9WucVEB9RYGY6RHzLVFekOXd6sv3hcNYp3V_sSGjfFPrMkBC5173wXY2HOyyzKNow-IXn3h1cuQ7FopM8bcKNfjJHj7P8Dkp2dmd373xHeF3f7FSaMNyrxQNwoyofizk-8g4_Et5MdEWcLM9WtWjjepNwtGzh1NaWBFrMwWSks4FhdEehAZeF9p9A7q8hNhw_jjzAhC4hQlUArVgIDhEXdcZgFztfkLtODRVktORYB466tmDETixpUiXBUlde9ztNQmSDEXVxG-mNxMZteHL31-jINniF0aj3MrCGnCKWRUvvatyx7WkpJqwjCRjZJoiJOaaYHOgktKkVOkEE_tDqNpbHhE7FXVmXxVIAOZBzEVtKhI4VaK7LgNmPaNXpfkA7E6624ctNTmHMljVVOrgzLN_9NvgPxavfAesPe8feuE5b_rhvTbrsbVf0572dxHqcWfUSbhAlG1qaZnxiLAX1Nqo01diAOtqqR91jQ5D_04tm_m5-L2wFnzLhkmQOx19Zd8ULcMtftsqmHYn8ynS_Ohi5qMHSKzuevU2pZnJwuPn0HZeINjQ |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VggQceKMGCuwBJC6m8dt7QChpiBo1jQoNqDdrd8eLIqV26kcR_4W_wH9kZmOHl-DWAwfLkl9a2998Mzsef8PY80BINOfQd5Ig0E6ADsBJklA5LiARyj64CuyPwtN4NktOT8XxFvvW_QtDZZUdJ1qihkJTjnyPpK4QO7h6szp3qGsUfV3tWmisYXGYffmMU7bq9WSE7_eF543fzvcPnLargKPRmGoHEqMxhgehhVCucg0NFT2_MBItrm-iKMjCGIHpqcg3ICXG7Bpc36g4FNr4eNkr7GpA5G8rBU82KR2S2EyCZC0g5Puiv3dmCyKzSrj0va__i--zLQL-8ADWrY1v_2cP5A671cbPfLAG_F22leX32M2fVBXvs6-TjcxozceyWdZ8tC4oXFT8yHbM5hiq8-FSQsZH8gwplReGv2skOO-LGnd9GHzkQ_TvwIucYzyOVAf8uGwoicRPVrKs8MQsLxaUaeGDpi5IDxSykssc-H6RX7QWjUMl-RO7svX2D9j8Mp7OQ7adF3m2w7jyROiFRuCiAglKSYxPTEKicng9L-6xVx06Ut0KtFOfkGWKEzWCU_obnHrs5eaE1Vqb5O-HDglum8NIVNxuKMpPactRaRgbcAFM5EcQGBMnbqQNeHg3sdJGmx7b7ZCYtkxXpT9g-Ojfu5-x6wfzo2k6ncwOH7MbHtUG2bKgXbZdl032hF3TF_WiKp9aq-IsvWTQfgdzm2TZ |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lj9MwEB4tXYTgwBtRWMAHkLiENonz8AGhdktFtbtVgAUtp8iPGFXqJiWPRfwX_gj_jrGTlJfgtgcOUaTYiRLnm5kv9uQbgMeUcTTnwHdiSqVDMQA4cRwIx1XoCPlYuULZH4UPo-UyPjlhyQ586_-FMWmVvU-0jloV0syRj4zUFWIHdyPdpUUks_mLzSfHVJAyK619OY0WIgfZl8_4-VY9X8zwXT_xvPnL4_1XTldhwJFoWLWjYi2RzysmGROucLW5bWQBTHO0vrEOQ5oFEYLUE6GvFefI36VyfS2igEnt42UvwC4yckoHsJssjpIP2wkeI7gZ07iVE_J9Nh6d2vTIrGKuWf0b_xIJbcGAP-KBDXLza__x8FyHqx2zJpPWFG7ATpbfhCs_6S3egq-LrQBpTea8Wddk1qYaripyZGtpEyTxZLrmKiMzforOlhSavG64ct4UNTa9m7wnU4z8ihQ5QaaOTlCRpGzM9BJ5u-FlhSdmebEyczBk0tSFUQpVWUl4rsh-kZ91to63aoRR7M5m4t-G4_MYnTswyIs8uwtEeCzwAs1wE5QrITgyFx0buTm8nhcN4VmPlFR20u2mgsg6xU84A630N2gN4en2hE2rWvL3rlMDvW03IzduDxTlx7TzXmkQaeUqpUM_VFTrKHZDqZWHTxMJqaUewl6PyrTzgVX6A5L3_t38CC4hVtPDxfLgPlz2TNKQzRfag0FdNtkDuCjP6lVVPuxMjEB6zqj9Di06bxk |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Intelligent+Fault+Diagnosis+Method+for+Blade+Damage+of+Quad-Rotor+UAV+Based+on+Stacked+Pruning+Sparse+Denoising+Autoencoder+and+Convolutional+Neural+Network&rft.jtitle=Machines+%28Basel%29&rft.au=Yang%2C+Pu&rft.au=Wen%2C+Chenwan&rft.au=Geng%2C+Huilin&rft.au=Liu%2C+Peng&rft.date=2021-12-01&rft.pub=MDPI+AG&rft.eissn=2075-1702&rft.volume=9&rft.issue=12&rft.spage=360&rft_id=info:doi/10.3390%2Fmachines9120360&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2075-1702&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2075-1702&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2075-1702&client=summon |